ESOLoop: An Entrepreneurship Self-Organization Framework for a Complex, Dynamic and Interconnected world.

By. Kiran Johny

Mobirise

Self-Organizing Entrepreneur Exploring the State Space


EsoLoop Framework: Short Introduction

What:

Eso-Loop Framework is an applied complexity framework designed to aid entrepreneurial action. This design was primarily inspired by the Constraints-led approach in sports coaching, and also inspired by successful applied complexity models like the Guided Self-organization approach, Cynefin framework, etc. The ESO in the Eso-Loop framework stands for Entrepreneurship Self-Organization, and the LOOP signifies the importance of feedback loops.

How:

Entrepreneurship is fundamentally a self-organizing system, and thus, entrepreneurship doesn't work properly with top-down order or prescription of ideas and artifacts like the lean-startup. Self-organization involves un-prestatable, adaptive, and emergent behaviors. The Eso-Loop framework, therefore, views entrepreneurship as a complex adaptive system with self-organization tendencies. It employs constraints-based dynamic design as a solution to aid entrepreneurial action. This approach is based on the scientific understanding that open complex adaptive systems have a tendency to self-organize under various constraints.

Abstract

Entrepreneurship is a complex decision domain. It is essential that solutions designed for complex domains like entrepreneurship must consider dynamics of complexity like non-linearity, inter-relatedness, emergent property, etc. Regardless of this, most of the dominant entrepreneurship perspectives still assume that entrepreneurship is the same all over the world. The proposed solutions and methods are often developed without any consideration to many of the non-linear dynamics that are inherent to complex domains like entrepreneurship. They usually ignore the massive diversity and uniqueness of personal, historical, cultural, institutional, social, and spatial contexts. While entrepreneurship operates at the evolutionary edge of social emergence, most of the current thinkers and their models never truly acknowledged its massive uncertainty and complexity. Further, the need for appropriate methods is neglected in favor of reductionistic one size fits all prescriptive models, cliche advice, and incrementalism. 

In the following part, I am introducing a complexity science-informed design solution to aid entrepreneurial actions. This is based on the scientific understanding that open complex adaptive systems like entrepreneurship have a tendency to self-organize under various constraints (Kauffman, 1995). Deriving from that, the framework is built on the premise that self-organization and design are complementary pairs (Kelso et al, 2016; Prokopenko, 2009; Gershenson, 2020). In the first part of the presentation, I will discuss complexity, the nature of entrepreneurial complexity, and the implication of complexity on human decision-making and expertise. Then I will discuss why existing entrepreneurship prescriptive models are inadequate for dealing with complexity. After that I will introduce three important components of the framework; The first is about setting the right complexity-based world view (Dent, 1999), for which an Ecological world view is adopted (Ulanowicz, 2009; Gibson, 2014; Capra, 1996). The second is about the idea of effectual self-organization, a primary enabling constraint (Simple rules or heuristics) for entrepreneurs to deliberately act like self-organizing systems. The third is about constraints and the role of constraints in shaping self-organization. Here I use Constraints-based design, an idea inspired from the Constraints-Led approach in sports coaching (Davids et al, 2007) to design and introduce various constraints that can shape entrepreneurial exploration and self-organization. Alicia Juarrero's(1999) conception of Context-free and Context-sensitive constraints is used as a fundamental frame over which various constraints are introduced. 

The framework is created as an integrative and harmonizing design solution that embodies dispositions such as self-organization, sense-making, exploration, evolvability, diversity, adaptability, learnability, and agency — all of which are essential for dealing with a complex-emergent decision domain like entrepreneurship.

Introduction to the Eso-Loop Framework ; An Entrepreneurial Self-Organization Framework

Highlights

This framework was developed based on a complexity frame. It considers factors like non-linearity, inter-relatedness, emergent property, etc. It promotes self-organization and designability as complimentary pairs.

This framework makes it easier for you to transparently see and navigate the big picture(The Forest), even if you had to attend to the contextual demands(The Tree).  

The framework stress the need to harmonize the growing collective human intelligence of both practitioner and academia developed models and bring it all accessible and affordable for the entrepreneurial actor at the time of his/her decision making and action.

This framework acknowledge that real world problems and its solutions transdisciplinary in nature, and promotes exploration by transcending traditional boundaries.

This framework embodies a "Work In Progress" attitude. The framework itself accepts its possible weakness. That is why evolvability is a key feature of this framework.

Existing prescriptive models proposes permanent structures that end up being inflexible. Eso-loop framework is based on constraints that are not permanent structures, but impermanent scaffolds meant to enable emergence. It can be adapted, changed, designed, and suited for the person and context.

This framework embodies and encapsulate various complexity friendly functional dispositions into a single framework so that an ecology of ideas in its dynamics will transfer to the users, not just one or two ideas or a linear process.

This framework has default immunity against agency hijacking and certainty merchants (Espinosa, 2015). People and ideas will compete for your attention and agency. This framework provides inbuilt dispositions and awareness against such effort. 

Table Of Contents

1

Part 1
Introduction to complexity, nature of entrepreneurship as a complex domain and the limitations of current thinking space.

2

Part 2
Building of a complexity friendly framework. 

3

Part 2(l)
The Worldview

4

Part 2(ll)
Effectual Self-Organization

5

Part 2(lll)
Constraints Theory and Design

6

Part 3
Praxis: Bringing It All Together

7

Part 4
Conclusion

8

Part 5
Images Charts 

9

Part 6
Citation and other details.  

Part 1

Introduction

Entrepreneurship is a complex (McKelvey 2004), heterogeneous, and multi-level phenomenon. It is affected by a large number of interconnected and interacting variables (Bygrave and Minniti, 2000; Giannetti and Simonov,2009), and depends on numerous contextual factors (Welter, 2011; Audretsch. et. al, 2012). This may include factors like; socioeconomic status (Giacomin et al. 2011), education (Millan et al. 2014), gender (Warnecke, 2013; Marlow and Martinez, 2018), time (Klyver et al., 2018; Bird and West, 1997), history (Wadhwani, 2016), location (Audretsch et al., 2012; Storey and Johnson, 2002), stakeholders (Dew and Sarasvathy, 2007), cognition (Dew, et al. 2015; Grégoire et al. 2011; Ward, 2004), intentions (Krueger et al., 2000; Fayolle and Liñán,2014; Krueger, 2017), skills (Lazear, 2004; Hsieh et al, 2017; Oosterbeek et al, 2010), opportunity recognition (Singh, 2001; Lumpkin and Lichtenstein, 2005), acquisition of resources (Leung et al. 2006; Martens et al. 2007), product development (Giardino, et al.,2014), marketing (Lam and Harker, 2015), etc, to list only a few.

One of the major implications of such complexity in entrepreneurship is the inherent uncertainty when it comes to decision-making (Knight, 1921 ). In complex domains like entrepreneurship, past behavior cases may not help us in predicting the future. To predict an event accurately, we need to have a stable system with adequate information. In the case of complex emergent systems, history never repeats. This is why when studying complex domains like entrepreneurship it is essential to use a complexity lens (Berger and Kuckertz, 2016).  

In the following chapters I will discuss two of my core arguments.

1. What is complexity and Why entrepreneurship is called a complex decision domain: In this part, I will explore the nature of complexity by looking into scientific and also decision-making implications of complexity. I will then list down some of the core features of entrepreneurial complexity.

2. Problems with existing models: Although entrepreneurship is a complex field most of the existing prescriptive action models like lean-startup or business planning, etc are created and used without any consideration to the essence of complexity. In this part, I will list some of the core weaknesses of existing entrepreneurship models when dealing with complexity.

Definition

The definition of complexity is by itself context-dependent (Standish, 2001). Its usefulness depends mostly on the domain and specific problems in question. This further adds to the difficulty in providing a universal definition of complexity. To have a generic definition applicable to all contexts, it is necessary to have adequate flexibility. Bruce Edmonds proposes a meta-level and accommodative definition of complexity i.e: “That property of a language expression which makes it difficult to formulate its overall behavior, even when given almost complete information about its atomic components and their inter-relations” (Edmonds, 1995). According to him, this is a very general definition, which is intended to have different interpretations in different contexts.

Melanie Mitchell(2009) also agrees with the above observation by suggesting that "various(complex) systems are quite different", but adds that, "viewed at an abstract level they have some intriguing properties in common". They are; Complex collective behavior: It is the collective action of vast numbers of components that give rise to the complex, hard-to-predict, and changing patterns of behavior that fascinate us; Signaling and information processing: Complex systems produce and use information and signals from both their internal and external environments; and Adaptation: All these systems adapt—that is, change their behavior to improve their chances of survival or success—through learning or evolutionary processes. Combining this observation, she proposes her definition of a complex system, i.e. "a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution" (Mitchell, 2009). For an explanation from the entrepreneurship field, Benyamin Lichtenstein(2000) suggested that four assumptions characterize complex systems. They are; Dynamics—Complex systems are dynamic and constantly changing; Irreducibility of elements: Elements are Irreducible due to the entwined nature of the elements; Interdependencies—The causality in complex systems cannot be described by linear models, as the causality is interdependent; Non-proportionality—Small inputs might have a large impact, whereas large inputs might hardly change the outcome.

Understanding by distinction

Apart from definition, One of the best ways to understand the nature of complexity is to find how it is different from other systems like e.g. simple and complicated. Complexity as a field of scientific inquiry was explained well by such a distinction made by American mathematician Warren Weaver in his paper called Science and Complexity(1948).

Weaver(1991) divided the problems of interest in science into three categories. The first category is called Problems of Simplicity. These are problems in which two things are related to each other. These problems are characterized by just a few variables and relations between them. The problems of simplicity were solved by science by developing experimental and analytical methods for handling problems in which one variable, say a gas pressure, depends primarily upon a second quantity, eg, the volume of the gas. These problems are characterized by the fact that the behavior of the first quantity can be described with some degree of accuracy by taking into account only its dependence upon the second variable and by ignoring the negligible influence of other factors. He calls the second category problems of Disorganized Complexity. According to him a problem of disorganized complexity “is a problem in which the number of variables is very large, and one in which each of the many variables has a behavior which is individually erratic, or perhaps totally unknown. However, despite the unknowns, the behavior of all the individual variables, the system as a whole possesses certain orderly and analyzable average properties. These are understood through taking averages over the large set of variables while ignoring or assuming very little interaction among the variables. Examples of such systems are ideal gas (as discussed in thermodynamics) and random coin tosses(as discussed in probability theory). In addition to “problems of simplicity” that are solvable with hard sciences and “disorganized complexity” that can be dealt with statistics and probability, Weaver introduced a third kind called problems of organized complexity. These are problems that involve a moderate to a large number of variables. But the key here is that, due to their strong nonlinear interactions, the variables cannot be meaningfully averaged. Weaver characterized these as "problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole", such as a biological or societal system. The third category "organized complexity" is the representation of the type of complex system we are talking about in this paper.

Another powerful model that gives a lot of clarity from distinction is a sense-making framework developed by Dave Snowden (Snowden, 2010) called Cynefin. It differentiates between three kinds of systems (Van Beurden et al, 2013): Ordered systems(simple/ complicated) in which cause and effect relationships are either clear or discoverable through analysis; Chaotic systems in which turbulence prevails and immediate stabilizing action is required; and Complex systems in which the only way to understand the system is to interact. In a complicated context, at least one right answer exists. "In a complex context, however, right answers can’t be ferreted out. It’s like the difference between, say, a Ferrari and the Brazilian rainforest. Ferraris are complicated machines, but an expert mechanic can take one apart and reassemble it without changing a thing. The car is static, and the whole is the sum of its parts. The rain-forest, on the other hand, is in constant flux—a species becomes extinct, weather patterns change, an agricultural project reroutes a water source—and the whole is far more than the sum of its parts" (Snowden and Boone, 2007).


Complexity and Entrepreneurship

It has to be noted that entrepreneurship has components of all decision types(ordered, complex and chaotic Or problems of simplicity, disorganized complexity, organized complexity). But the core aspect of venture creation comes mostly under Weaver's third category, organized complexity; or in other words a complex domain. Even though scholars have widely recognized entrepreneurship as a complex and highly uncertain process (Lichtenstein et al, 2007; Brouwer, 2000), it is still popularly depicted as an individual activity (Drakopoulou, 2007) that takes place in a perfect market. In most cases, the role of the individual entrepreneur is exaggerated in such representation (Gaddefors and Anderson, 2017). Complex domains like entrepreneurship exhibit several features which cannot be taken into account by most currently existing reductionist models and perspectives (Berger and Kuckertz, 2016).


Following are some of the major characteristics of a complex system(or complex adaptive system) with entrepreneurship as an example. It is important to keep in mind that these characteristics are not separate from each other, and each requires all others for any attempt to explain it.


1. A large number of heterogeneous agents/components/elements:

Complex systems consist of a large number of elements that in themselves can be simple. When the number of elements is relatively small, the behavior of the elements can often be given a formal description in conventional terms. However, when the number becomes sufficiently large, conventional means not only become impractical, they also cease to assist in any understanding of the system (Cilliers, 2002). Further, these elements can be heterogeneous, in that they differ in their behavior, location, history, or other properties. Such heterogeneity, in turn, can have far-reaching impacts on the functioning of the system. For example, different species within an ecosystem will have been in the ecosystem for different lengths of time, possess different functional traits, and occupy different niches (Fisher, 2020).

Heterogeneity of entrepreneurship (Spilling, 2008) may manifest in many dimensions, which are by themselves complex; e.g. entrepreneur, cognition, skills, disposition, idea, customer, teams, investors, venture capitalists, government, bureaucracy, consultants, location, networks, resources, market, technology, law, policy, connection, trust, etc. The importance and inevitability of heterogeneity in entrepreneurship is evident from scholarly works focusing on various perspectives to approach heterogeneity (Davidsson, 2007; Breitenecker and Harms, 2010; Dufays and Huybrechts, 2016).

2. Non-Linear dynamics and interaction among elements(agents/components/variables):

According to Cilliers(2002), a large number of elements are necessary, but not sufficient. In order to constitute a complex system, the elements have to interact, and this interaction must be dynamic. A complex system changes with time. The interactions do not have to be physical; they can also be thought of as the transference of information. This is also evident from Herbert Simon's definition of a complex system; ie. "one made up of a large number of parts that have many interactions" (Simon 1996). Secondly, the interactions are non-linear. A large system of linear elements can usually be collapsed into an equivalent system that is very much smaller. Non-linearity also guarantees that small causes can have large results, and vice versa. Further, even if specific agents may only interact with a few others, the impact of these interactions are propagated throughout the system. Through this interaction, agents strive to improve their fitness with the environment but the outcome of these attempts depends on the disposition and behaviors of other agents (Mitleton-Kelly, 2003).

In entrepreneurship, agents interact with others in a non-linear manner. For example, an entrepreneur at the same time has to interact with his team, investors, customers, partners, government, etc. He/She gets feedback from the interaction, which triggers more interaction. These interactions will not follow a certain format or order; in other words, it is non-linear. Further, studies have also found that successful entrepreneurship ecosystems outperform others due in part to more amplifying and less dampening of nonlinear interactions (Han et al,2021).


3. Feedback Loop:

Complex systems are characterized by feedback loops in which the product of the process is necessary for the process itself. A feedback loop is “a circular arrangement of causally connected elements, so that each element has an effect on the next, until the last “feeds back” the effect into the first element of the cycle” (Capra 1997, 56). Change in a variable result in either an amplification (positive feedback) or a dampening (negative feedback) of that change (Kastens et al., 2009). Both kinds(positive---enhancing, stimulating or negative---detracting, inhibiting) are necessary. The technical term for this aspect of a complex system is recurrency (Cilliers, 2002). Further, when the interactions of large numbers of components involve positive feedback loops, some behaviors self amplify, quickly crowding out others. Groups of components become locked into self-reinforcing feedback cycles that lead to predictable collective behavior (Anderson, 1999).

Many scholars also stress the non-linearity aspect of feedback loops, calling them non-linear feedback loops (Juarrero, 1999: 47-48). According to Ralph Stacey(1995) organizations are nonlinear network feedback systems and it, therefore, follows logically that the fundamental properties of such systems should apply to organizations. Examples from entrepreneurship include explicit feedback like customer feedback (Eisenmann et al., 2012), stakeholder feedback, and long-term positive or negative feedback. At the macro-level, entrepreneurship is impacted by monetary policy, social climate, etc, and will impact the same by looping back; Like that economic activity promotes entrepreneurship and innovation activities, and entrepreneurship enhances economic activity by looping back (Galindo and Méndez, 2014). Feedback is also discussed in entrepreneurship literature as the one aiding effective decision-making (Haynie and Shepherd, 2007).


4. Emergence or emergent property:

Complex systems show emergent property. Emergence is a systemic process through which properties and or structures come into being that are unexpected, given the known attributes of component agents and environmental forces (Lichtenstein and McKelvey, 2011). Emergent properties refer to a characteristic that is found across the system but which individual parts of the system do not themselves hold. Other-words, if a thing can have properties or capabilities that are not possessed by its parts, such properties are called emergent properties (Elder-Vass, 2014). The emergent higher-order behavior cannot simply be derived by aggregating behavior at the level of the elements (Irreducibility, Beckage, 2013). This is because the whole is more than the sum of its parts when it comes to complexity. Another feature of emergent property is non-proportionality (Lichtenstein and Mendenhall, 2002). The effect of input is not proportional to the strength of that input. Due to the non-proportionality, small inputs might have a large impact, whereas large inputs might hardly change the outcome.

Entrepreneurship scholars for long had identified the importance of emergence as a frame for understanding entrepreneurial complexity (Katz and Gartner 1988; Gartner, 1993; Lichtenstein et al, 2006; Fuller et al, 2008). An emergent property is manifested in entrepreneurship in many different ways. At the basic level entrepreneurial venture or a startup itself is an example of emergent property of a large number of factors like intention, cognition, various skills, various stakeholders, location, experts, policy, networks, technology, etc. Further, emergence is manifested at the macro level when innovations breed other innovations(e.g. internet, google by democratizing information access), technologies that result in new disruptive responses, the emergence of new markets and new business models(e.g. app store, android), and collaborative activities(social media, collaboration apps), etc.

The implication of emergent property is that an opportunity(product, organization, market) can come into reality in the absence of deliberate planning. Users often adapt products to support tasks that designers never intended (Johnson, 2006). E.g. Instagram was started as an HTML5 supported location-based service; Facebook was started as an app to compare two people’s pictures and the rate which one was more attractive.


5. Self-Organisation:

Self-organization refers to the emergence of stable patterns through autonomous and self-reinforcing dynamics at the micro-level (Kauffman, 1995; Ska˚r, 2003; Anzola et al., 2017). It is a bottom-up process where complex organization emerges at multiple levels from the interaction of lower-level entities. The final product is the result of nonlinear interactions rather than planning and design; and it is not known a priori (De Roo, 2016). This can be contrasted with the top-down approaches where planning precedes implementation, and the desired final system is known by design. In self-organization, there is no hierarchy of command and control and there is no planning or managing, but there is a constant re-organizing to find the best fit with the environment. The system is continually self-organizing through the process of emergence and feedback. It changes the relationships between the distributed elements of the system under influence of both the external environment and the history of the system (Cilliers, 2002). Further, emergence happens naturally from the interactions within a complex system; they do not need to be imposed top-down in a centralized way. For example, termites are known to construct the highest structures on the planet relative to the size of the builders. Yet there is no chief executive among termites, no architect termite, and no blueprint. Each termite acts locally, following only a few simple shared rules of behavior within a context of other termites also acting locally. The termite mound emerges from this process of self-organization (Plsek and Greenhalgh, 2001).

We can find self-organization as an important character of the market, business, and entrepreneurship. For example, we can see the venture as a self-organized system that emerged to satisfy customer demands. It can be the result of self-organization like Effectuation (Sarasvathy, 2001), Entrepreneurial bricolage (Baker and Nelson, 2005), User innovation (Hippel, 1988), etc. Further, according to Peltoniemi & Vuori(2004), even "the formation of a business ecosystem is a process where participants are gathered voluntarily and without external or internal leader. Goals are set in local interactions, where companies negotiate and create a new order."

6. Fuzzy boundaries and Nested-embedded complexity:

In a complicated system like a car or algorithm, boundaries are fixed and well defined. Complex systems are systems typically characterized by fuzzy boundaries. Membership of a complex system can change, and agents can simultaneously be members of several systems (Plsek and Greenhalgh, 2001). E.g. investor could be a family member or she might invest in other companies, or join as a startup team member.

Complex systems are also embedded/nested in other systems which are constantly interacting with one another. For example, Our planet is a complex system, as is our body, the organizations that we create, or our social and economic systems (Héraud et al. 2019). When it comes to the economic system, buyers and sellers form markets and markets form economies, whereas the buyers and sellers themselves can be people or organizations formed by people (Noell, 2007). All of these are complex systems by themselves. Because of this embedded nature, it is virtually impossible to accurately mark the boundaries of one system from other systems. Further, Since each agent and each system is nested within other systems, all evolving together and interacting, we cannot fully understand any of the agents or systems without reference to the others (Plsek and Greenhalgh, 2001).

An entrepreneur is a system nested/embedded itself with other complex systems like startup team, customers, market, investors, family, suppliers, partners, government, country, and society itself. In fields of study like entrepreneurship, business, organization, etc, scholars have been exploring the nested and embedded nature of multiple complex co-evolving systems (Holm,1995; Baum and Singh, 1994; Dieleman and Sachs, 2008; Ng, 2011). Scholars have also explored the contextually embedded nature of entrepreneurial actors (Baker and Welter, 2018; Autio et al, 2014; Lounsbury et al, 2019), suggesting the entangled and embedded nature of entrepreneurship to the context in which it is situated.


7. Dynamics of evolution: Evolution, Adaptation, Co-evolution, Path Dependence:

Complex systems follow the dynamics of evolution. The behavior of complex systems evolves from the interaction of agents at a local level without external direction or the presence of internal control (Kernick, 2006). This should not be viewed as local Darwinian processes and optimization alone (Pendleton and Brown, 2018), but also as dynamic exchanges between components and the system as a whole, impacting other complex systems. Following are some of the ways in which the dynamics of evolution manifests in complex systems. First of all, adaptation forms one of the fundamental features of evolution. All complex systems adapt—that is, change their behavior to improve their chances of survival or success—through learning or evolutionary processes (Melanie Mitchell, 2009). They can (re)organize their internal structure without the intervention of an external agent (Paul Cilliers, Emergence, March 2000). Secondly, In nature and complex systems, evolution is manifested in the form of co-evolution (Ehrlich and Raven, 1964). When organisms adapts in a reciprocal way, it will lead to Coevolution (Thompson and Cunningham, 2002). It refers to the simultaneous evolution of entities and their environments, whether these entities are organisms or organizations (Baum & Singh, 1994). It encompasses the twin notions of interdependency and mutual adaptation, with the idea that species or organizations evolve in relation to their environments, while at the same time these environments evolve in relation to them (Porter, 2006). Adding to this we can take Kauffman's(1993, 237) observation that, "all ‘evolution’ is really coevolution". According to him, "the true and stunning success of biology reflects the fact that organisms do not merely evolve, they coevolve both with other organisms and with a changing abiotic environment". He argues that coevolution is at the root of self-organizing behavior, constant change in systems, the production of novel macro structures, and non-linearities.

Further, according to Cilliers(2002), Complex systems have a history. Not only do they evolve through time, but their past is co-responsible for their present behavior. This is equivalent to ideas like path dependence (Liebovitz and Margolis, 1995; lock-in's(Arthur, 1989), or Imprinting (Levinthal, 2003; Johnson, 2007, etc.

An evolutionary perspective is indispensable from the complexity worldview of entrepreneurship. According to Cordes et al(2008), "firms are culturally variable and evolve new cultural forms as time passes. This evolution is partly driven by entrepreneurs and other business leaders in entrepreneurial roles (Penrose, 1959; Langlois, 1998), partly by the decisions made by rank-and-file members, partly by the firm’s competitive success or failure (Alchian, 1950), and partly by cultural evolution in the larger society within which firms are embedded". Acknowledging this centrality, evolutionary dynamics in entrepreneurship is one of the most widely explored topics among scholars. This includes meta-level exploration of the evolution of the field itself (Landström, 2020; Carlsson et al, 2013) to various other dimensions of evolutionary dynamics (Schaltegger et al. 2016; Pacheco et al., 2014; Tiwana et al., 2010; Inkpen et al., 2004; Lewin et al, 1999; Jones, 2001; Grodal et al., 2015).
.

8. Attractor behavior:

In a complex emergent system, an attractor is a set of states towards which a system will naturally gravitate and remain cycling through. They are islands of stability in a sea of chaos. Complex systems are inherently uncertain, but, they usually settle down into one of a number of possible steady states. These steady states are called "attractor basins".
As a universal feature of complex adaptive systems, the dynamics of attractors are active in entrepreneurship too. Complexity theory views organizations(or startups, new ventures, etc) as “complex adaptive systems” that coevolve with the environment through the self-organizing behavior of agents navigating “fitness landscapes” (Kauffman, 1995) of market opportunities and competitive dynamics (Coleman, 1999). Such self-organizing systems typically evolve towards a state of equilibrium, or an attractor state. Once there, the further dynamics and evolution of the system are likely constrained to remain near the attractor. This also means changing external and internal “attractors” influence the process of adaptation by agents (Coleman, 1999; Stacey, 1996). Since attractors are the most stable and robust elements in these systems, they are more feasible targets for foresight than the several variants that they configure and effectuate (Kuhmonen, 2017).

The word ‘attractor’ has the connotation that it attracts the system to a certain state. However, it is not a ‘thing’ out there, but an expression of the extent to which a system can change, indicated by its resilience against disturbances (Gerrits, 2012; Marion, 1999). Further, it is a model representation of a system’s behavior. It is not a force of attraction or a goal-oriented presence in the system; it simply depicts where the system is headed based on its rules of motion (Dolan et al, 2000)

There are 3 major types of attractors (Gerrits, 2012). When a system always returns to exactly the same state when under pressure, it can be expressed by a fixed point attractor (Otter, 2000). A periodic or torus attractor describes the alternation of systems between a limited number of states (Mackenzie, 2005). The third category is highlighted by the inability of a system to return to any of its previous states, in which--each new state is ever so slightly different from the previous one. This category is called the strange attractor (Byrne, 2002). It can remain in this current state because of existing feedback loops that maintain a particular situation. It limits the dynamics of the system but also makes it impossible to predict exactly where the system is going to be.

The strange attractor is very important in domains like entrepreneurship, business, sociology, etc, because many of the dynamics in these systems behave as if they were guided by these strange attractors. They don't go back to the old state, or jump between states, but are guided by the dynamics of strange attractors. New market opportunities are a good example of attractors. These attractors “pull” entrepreneurs to innovate within existing firms or found new enterprises (Miles et al., 1998).


9. Miscellaneous:

Apart from the above 8, the following are also core aspects of complex systems.

Complex systems have a history and history matters; According to Cilliers(2002), Complex systems have a history. Not only do they evolve through time, but their past is co-responsible for their present behavior. This is equivalent to ideas like path dependence (Liebovitz and Margolis, 1995); lock-ins (Arthur, 1989), or Imprinting (Levinthal, 2003; Johnson, 2007), etc.

Complex systems are open systems; They exchange energy or information with their environment—and operate at conditions far from equilibrium. It is also important to note that Self-organization only occurs in such open systems that import energy from the outside (Prigogine and Lefever, 1974). As a result of this, it is often difficult to define the border of a complex system. Further, closed systems are usually merely complicated (Cilliers, 2002). Eg. A venture started in India will be affected by dynamics of US or Chinese markets, Oil price, technology emergence, a crisis like COVID, etc. All these factors are not going to affect the functioning of a complicated system like a PC, car, or machine.

Complex systems evolve in the adjacent possible (Kauffman, 1996); a zone towards which change and evolution is more likely because of the current disposition of the system. The concept of “adjacent possible” was introduced by Stuart Kauffman(1996; 2000) in evolutionary biology and complex adaptive systems to explain how biological evolution can be seen as exploration and actualization of what is adjacent possible. It can be defined as “the set of possibilities available to individuals, communities, institutions, organisms, productive processes, etc., at a given point in time during their evolution” (Loreto, 2015). The concept of the “adjacent possible” is useful for understanding how entrepreneurial adjacent possibilities emerge, and how the new adjacent possible will lead to yet newer adjacent possibilities.

Complex adaptive systems like entrepreneurship display power-law distributions; Power laws and outliers are pervasively present across many types of variables that are central to most entrepreneurial activities (Crawford et al, 2015). It is also clear in the success and impact of entrepreneurial ventures. For example, 95% of all U.S. businesses are small--employing 20 people or fewer, and more than 60% of all new jobs are created by a mere .03% of all entrepreneurial start-ups. Further, most successful companies of our time, Facebook, Google, Apple, Amazon, Walmart, etc, are extreme outliers (Aguinis and Joo, 2015).

Finally, Complex systems may show behaviors like the Mathew-effect(those who begin with advantage accumulate more advantage over time and vice-versa for those who begin with disadvantage), Network-effect( increases in product value with increases in the number of users), Preferential attachment(more connected a node is, the more likely it is to receive new links). For E.g. Initial success improves access to deal flow in venture capital, which leads to accumulated advantage (Nanda,2020). These effects may explain the success of already successful or second-time entrepreneurs, etc. 

Domains like entrepreneurship exemplify all of the previously listed characteristics of a complex system. Following are some of the characteristics of decision-making in real-world complexity.

* Real-world decisions involve uncertainty (Knight,1921): This will limit the scope for identifying a rationally optimum decision and allow for a range of competing proposals and preferences to claim credibility (Lyles, 1981). Uncertainty can be viewed as part of the real world and it affects individuals at various levels (Folta, 2007; Platt and Huettel, 2008). The role of uncertainty and its various dimensions of it in entrepreneurship is also a widely observed phenomenon (Sarasvathy and Kotha, 2001; Bylund, and McCaffrey, 2017).

* Real-world decisions are affected or constrained by Human Bounded Rationality: In a complex and uncertain world, humans make decisions under the constraints of limited knowledge, resources, and time. Herbert Simon (1957) thus argued that humans have bounded rationality. He suggested that the complexity of the environment and humans’ limited cognitive system make maximization impossible in most real-life decision-making situations.

* Real-world decision contexts thus are mostly based on satisficing logic (Simon, 1947; Brown,2004): We rarely make a decision after possessing all the facts, and as a corollary, In practice, we rarely make a decision based on all the facts in our possession. This is Simon’s (March and Simon, 1958) notion of ‘satisficing’, which refers to the less than optimal choices due to decision makers’ bounded rationality or limited cognitive capacities and lack of time and energy. This involves using of simple rules than complicated models, otherwise known as heuristics (Gigerenzer, 2011).

* Real-world decisions are context-dependent (Trueblood et al, 2013; Hutchins and Klausen, 1996). As demonstrated by Herbert Simon's(1990) scissors analogy, real-world decisions must include cognition and context(environment). In other words, we can say, real-world cognition and decision-making are; ecological (Gibson, 1979); embodied, embedded, enactive (Varela et al., 1992), and extended (Clark and Chalmers, 1998) outside of our brain. This means it is essentially impossible to understand(or effect) real-world decisions by cognitive reductionism.

* Real-world decision-making in complex domains can be understood in different ways. Following are 3 of them (Campbell, 1988). Firstly, as a subjective psychological experience. Secondly as an interaction between task and person characteristics (March and Simon, 1958), in that, tasks can be said to be more or less complex relative to the capabilities of the individuals who perform the task. Thirdly, as a function of objective domain or task characteristics(climate change, Entrepreneurship).

* Real-world decision contexts don't have boundaries. Categories like Simple, complicated, complex, chaotic (Snowden and Boone, 2007), even though discussed and studied separately, may appear part of a real-world decision context. This means decision-makers deliberately or intuitively engage in continuous sense-making effort (Kurtz and Snowden, 2003) to identify the decision context they are in. For example, the core aspect of entrepreneurship is complex and uncertain. Even though this is the case, there are complicated jobs to be done like a lawyer's job, accounting, etc. Simple jobs like switching on the lights, cleaning the floor, etc.

* Real-world decision-makers use ambidextrous methods. Entrepreneurs use both causal and effectual approaches in a variety of combinations. Both causation and effectuation are integral parts of human reasoning that can occur simultaneously, overlapping and intertwining over different contexts of decisions and actions (Sarasvathy, 2009). According to Yang and Gabrielsson(2017), entrepreneurs use ambidexterity in methods. i.e. the capability to employ both exploration and exploitation methods, or simultaneously use the effectual process to explore and create a new market, and the causal process to exploit the existing market. Further,

* Real-world problems don’t care about disciplinary boundaries(Bendix, 2020). This asserts that interdisciplinary working is needed in order to explore the ‘real world’ problems (Dalrymple and Miller, 2006). This awareness may help the decision-maker to expand the search space. Real-world entrepreneurship is contextual and according to Welter’s (2011), "a contextualized view on entrepreneurship asks for an interdisciplinary perspective, as the solution cannot be to develop an overarching theory of entrepreneurship in all contexts, but rather working with disciplines like anthropology, sociology, and others, which possess some of the tools and concepts entrepreneurship scholars need to explore the variety, depths, and richness of contexts”. Scholars have also acknowledged this importance in entrepreneurship education (Penaluna and Penaluna, 2009; Janssen et al, 2007)

* Real-world predictions about the future cannot be accurate even with all the knowledge about the past. Out past and history is important because of the path dependence, but it can only suggest insights about the current disposition, not an accurate future state. This can be explained by the butterfly effect which occurs when a very small change in one part of a complex adaptive system may initially go unrecognized results in a massive disruption, surprise, or turbulence. The results may be impossible to predict (Bennet and Bennet, 2008). This is because we can't get the accurate and massive amount of necessary data in its dynamics. This can also be seen as an implication of unavoidable uncertainty in the initial condition (Boffetta et al, 2002).

* Real-world decisions may involve trade-offs between exploit(efficiency) and explore(evolvability). Many decisions in the lives of animals and humans require a fine balance between the exploration of different options and the exploitation of their rewards (Mehlhorn et al, 2015). Exploitation maximizes rewards in the near term, while the information obtained during the exploration can later be used to maximize rewards in the long term (Barack and Gold, 2016). Too much exploitation could promote rigidity and optimization in a local niche, and too much exploration may result in becoming a situation like "a jack of all trades, but master of none". Here also the concept of satisficing is important.

* Real-world decisions may have unintended-consequence (Merton, 1936), particularly in domains of many interacting agents. E.g. The Hawthorne effect (Sedgwick and Greenwood, 2015; Wickström and Bendix, 2000), Cobra Effect (Warczak Jr, 2020), etc. The unintended consequences are the result of complexity, and the inability of decision-makers to understand and anticipate the emergent realities.

* Real-world decisions may directly affect other people in the network. Further, people in one's network affect his/her decision making and are in turn affected by decisions of other agents (Christakis and Fowler, 2007; Edelenbos and Klijn, 2007; Sadovykh et al, 2015; Ingold and Leifeld, 2016).

* Real-world complex contexts may not have clear measures of performance or success. It may be absent, conflicting, or vague. Who can say person x made the better fatherly decision for his child than person y. It is totally relative. Even the output, i.e. the child's success may or may not have anything to do with the parenting decision in a long run. This is not to say that there is no measurable performance in the real world; there are. In domains where performance can be objectively measured, such measures drive success, but when performance can’t be measured, it is possible that networks, people or society drive success" (Barabási, 2018). Success is thus a collective endeavor, independent from individual performance. Besides performance, success requires building trust and reputation in order to shape others’ perceptions regarding one’s achievements (Day, 2019).

* Real-world decisions are not just individual driven but distributed (Rapley, 2008; Schneeweiss,2012). Decision making in the real world is distributed across various stakeholders(eg. Investors, co-founders, family), institutions(government agencies, partner companies), artifacts(software, notebook, Mobile), etc. A real-world example is patient and doctor decision making, in which both patient and doctor have a legitimate investment in the treatment decision; hence both declare treatment preferences, the rationale for such preference, etc while trying to build consensus on the appropriate treatment to apply (Charles et al, 1997, 1999). In real-world contexts like entrepreneurship, there are multiple stakeholders with diverse and conflicting beliefs, preferences, and goals. This idea is enacted in the "Crazy Quilt Principle" of effectuation (Sarasvathy, 2009), where partnerships determine, to a great extent, which product or market the company will eventually end up entering or creating.

* Real-world decision problems are interactive and dynamic. In a complex domain, decision-makers(entrepreneurs or managers) are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other. These problems are labeled by Ackoff as messes (Ackoff 1978; Bennet et al, 2008).

* Real-world decisions involve an active role for emotions (Hermalin and Isen, 2008). Emotion is an intrinsic part of human decision-making in the real-world context. Research reveals that emotions constitute potent, pervasive, predictable, sometimes harmful, and sometimes beneficial drivers of decision making(Lerner et al. 2015). Many scholars now view emotions as one of the dominant drivers of most meaningful decisions in life. This is well observed in entrepreneurship also (Foo, 2011; Cardon et al, 2005; Arpiainen et al, 2013).

* Real-world decisions are path-dependent (Koch et al, 2009; Bindler and Hjalmarsson, 2019). Path dependence means that where we go next depends not only on where we are now but also upon where we have been; Thus, "History Matters" (Liebowitz and Margolis, 1999). Our decisions of the past and present will influence the future decision landscape, opening up some choices, and constraining others. In the same way, It is also influenced and framed by the path-dependence of other agents.

* Real-world decisions and success are influenced by timing. The right timing is one of the most important aspects of decision making in complexity and it is true in the entrepreneurship context too (Wadhwani et al, 2020). According to Bill Gross(2008), the single biggest reason why startups succeed is timing. Since complex domains exhibit dynamics of interaction and evolution (& co-evolution) the opportunity structure changes all the time. We must act when the evolutionary potential of other systems around us is primed perfectly for a change. For E.g. Facebook was started when a basic infrastructure was already evolved enough to support it, like the internet, connectivity(user base), previous models that showed the way(Orkut, myspace, etc.).

* Real-world decisions may be impulsive. Most human decisions in everyday life are not based on elaborate planning and research. Even in the case of important life decisions, people may rely more on emotions and impulsiveness. Simple heuristics are the most preferred decision-making device human beings use, and they mostly work (Gigerenzer, 2008). Studies have also shown that a significant part of entrepreneurial behaviors may also occur without ex-ante reasoning (Wiklund, 2019). According to Lerner et al(2018), non-deliberative impulse-driven behavioral logics can also be the basis for business venturing.

* Real-world decisions may be based on intuition. Researchers have discovered and acknowledged that decision-makers in real-world decision contexts rely heavily on intuition (e.g., Klein, 2015; Gigerenzer and Murray, 2015; Kruglanski and Gigerenzer, 2011). Further, according to William Duggan(2013), decision-makers may also use different types of intuition, namely; ordinary(every day), expert(fast judgment based on experience and practice), and strategic(a flash of insight that works in new situations).

* Real-world decisions are based on selective perception and use of information (Dearborn and Simon, 1958; Walsh, 1988; Beyer et al, 1997). There is always far more information in our objective environments than we can perceive or attend to. Thus, perceptions must be strongly guided by anticipations. As March and Simon (1958) noted, under conditions of complexity and imperfect information, decisions are made on the basis of selective perception and identification with sub-goals.

* Real-world decisions may have heterogeneous reasons. Bringing all of it together, it is absolutely clear that real-world decisions are massively complex and it can have a lot of different dimensions. This makes it difficult to pinpoint specific causes because you can only see the dimensions that you are looking at. Thus it can be, cognitive biases (Stanovich & West, 2008), past experience (Juliusson et al, 2005), escalation of commitment (Sleesman et al, 2012), age and individual differences (Bruin et al, 2007), belief in personal relevance (Acevedo, & Krueger, 2004), etc. To conclude, an economics model-driven view of decisions historically assumed the existence of a straightforward, linear thinking, rational decision-maker (Brzezicka and Wiśniewski, 2014). But in real-world complex contexts, decisions are often not what they appear to be. A decision presented as a technical decision may in fact be tactical, political and strategic, or egocentric, emotional and personal, or any or all of them, or more.

In the previous chapters, I have discussed the nature and features of complex systems with reference to the domain of entrepreneurship. I have also discussed the decision making implications in complex systems by listing several types of decision making features that are typical in complex systems. Over that understanding, in this chapter I will discuss the expertise dimension of complex systems with reference to the domain of entrepreneurship.

Expertise under complexity

While expertise is possible in complex domains, when it comes to ill-structured complexity domains like entrepreneurship, expertise in its originally recognized form is not applicable. The understanding of expertise applied to the domain of entrepreneurship was primarily shaped by Effectuation theory and the work of Saras Saraswathy. The empirical evidence for effectuation came from the study of expert entrepreneurs conducted by Saraswathy. She contrasts her study on entrepreneurial expertise with entrepreneurial performance that has been traditionally studied either (1) as a set of personality traits of the entrepreneur that explains the success or failure of the firms he or she creates (Llewellyn and Wilson, 2003), or (2) as a set of circumstances or attributes of the project and its environment that contains the seeds of its success or failure (Thornton, 1999). In that, she conducted a cognitive science-based study of entrepreneurial expertise using think-aloud verbal protocols. Included in that, a 17-page problem set of 10 typical decisions in a startup firm and had a representative sample of 27 expert entrepreneurs. Following are some of the key problems and features of expertise in complex domains with special reference to entrepreneurial expertise.

Firstly, when it comes to the possibility of expertise development, entrepreneurship can be considered a low validity domain (Kahneman and Klein, 2009) with extreme levels of complexity. To have genuine expertise to develop, the domains must be of high validity. i.e. "Skilled intuitions will only develop in an environment of sufficient regularity, which provides valid cues to the situation" (Kahneman and Klein, 2009). Thus, the problem with the understanding of entrepreneurial expertise is that, entrepreneurship is an ill structured or unstructured complexity domain. In a complex ill structured domain like entrepreneurship, there can be no stability in the state space, environment, structure, or goals. Even goals continually evolve and co-evolve, lacking stability. According to Kahnemann & Klein(2009), two conditions must be satisfied for a judgment to be recognized as coming from real expertise(expert intuition); First, the environment must provide adequately valid cues to the nature of the situation. The second one is that people must have an opportunity to learn the relevant cues. The determination of whether intuitive judgments can be trusted thus requires an examination of the environment in which the judgment is made and of the opportunity that the judge has had to learn the regularities of that environment. A crucial conclusion is that skilled intuitions will only develop in an environment of sufficient regularity, which provides valid cues to the situation. Thus, If an environment provides valid cues, prolonged practice opportunity with rapid and unequivocal feedback,---skill and expert intuition will eventually develop in individuals of sufficient talent. Such an environment also helps true experts develop skills to know when they don’t know something, something which non-experts certainly do not know (Kruger and Dunning, 1999). To be more specific about the contrast, Immediate Feedback, Repeatability & Regular environment are the fundamental conditions to develop expertise. Entrepreneurship can be characterized with the opposite; Delayed feedback, Non-Repeatability, Irregular complex, and emergent environment. Finally, It is also important to understand that other scholars (before Kahnemann & Klein, 2009) have also tried to bring out this case. For example, James Shanteau(1992) used the classification of Type 1 Type 2 domains to analyze the complexity and learnability of various environments, in which he also confirmed the importance of predictable environments and opportunities to learn them, in order to develop real expertise. Hogarth's(2015) concept of the kind and wicked learning environments is another model representing a similar idea. He described wicked as situations in which feedback in the form of outcomes of actions or observations is poor, misleading, or even missing. By contrast, in learning environments that are kind, feedback links outcomes directly to the appropriate actions or judgments and is both accurate and plentiful.


Secondly, the effectiveness of deliberate practice as claimed by effectuation might not work in ill structured complexity domains like entrepreneurship. Saraswathy(2008) defines an expert as someone who has attained a high level of performance in a domain as a result of years of experience and deliberate practice (Ericsson et al, 1993). Against this, Baron (2009) raised the important problem, ie “In what tasks or activities do successful entrepreneurs demonstrate expert performance?”. Advancing that point, Baron and Henry (2010) argued that deliberate practice may not be possible in entrepreneurship and that entrepreneurs instead either learn vicariously or transfer skills learned through practice in other domains into their new ventures. Frankish et al(2013) specifically questioned the idea of learning from experience. They pointed to the lack of repetition opportunities (owing to task diversity) and the difficulty of interpreting the various causes of new venture survival, suggesting that entrepreneurs improve performance only partially based on their experience at running new ventures. Further, in recent scholarly works it has been demonstrated that deliberate practice may not guarantee better performance in extremely complex unstructured domains. A 2014 meta-analysis (Macnamara et al, 2014) has shown that deliberate practice only explained 26% of the variance in performance for games, 21% for music, 18% for sports, 4% for education, and less than 1% for professions. This further demonstrates a low connection between deliberate practice and performance in complex unstructured domains.

Thirdly, expertise in complex social domains is distributed (Edwards, 2010). It is not necessary that an entrepreneur must be an expert in finance, accounting, programming, law, etc. Such expertise is distributed(and or extended) across various individuals(lawyer, doctor) institutions(law enforcement, companies) and artifacts(tools, software). etc. It is not even necessary that the entrepreneur know the entrepreneurial core activities. He can still win in-case he is in the right high network place(e.g. Harvard, Stanford, etc.), get good people to mentor and work(e.g. Facebook case of Sean Parker, Peter Thiel), get access to specialized institutions(e.g. YC in the case of Dropbox), have a rich family to support, etc. He can also fail despite all of this(see next).

Fourthly, complex domains like entrepreneurship are subjected to various complexity laws like power laws, Mathew effects, reputation effects, preferential-attachment, etc. This invalidates success as a metric of expertise. Core events in complex systems like entrepreneurship never repeat in originality(strange attractor effect), feedback is delayed, and since complex systems are governed by power laws, small things(e.g. Harvard dorm Facebook) can result in huge success, and resource rich interventions can fail(google plus). A tangent is that the emergent property of a system may not be the result of the expertise of a particular agent or agents, but because of the dynamics of the whole system co-evolving with the ecosystem as a whole. This may prevent us from establishing any valid causal relationship between expertise and performance in a domain like entrepreneurship. Thus in complexity, high performance may not guarantee success, in that, the success of an individual does not depend uniquely on the quality of performance (Barabási, 2018).


Fifthly, I believe that, like the personality view of entrepreneurial achievement (McClelland,1951, 1961; Llewellyn and Wilson, 2003), the expertise view may also have some unintended counter-productive effects. It can legitimize the hubris among successful entrepreneurs, and at the same time make the aspiring entrepreneurs think that they may require deliberate practice to become a successful entrepreneur, while in-fact success in many cases could be the result of complexity-effects like an accidental event powers by power laws, mathew effects, reputation effects, preferential attachment, etc. If anyone becomes successful in such a system, she(he) acquires what I call ecosystem embedded accumulated advantage(e.g. PayPal mafia), a Mathew effect which makes it easy for the agent to be successful in any substituent pursuits. This makes it difficult to identify the causation behind any success. This also makes it difficult to compare an expert with non expert by looking at performance as Barabási (2018) pointed out.

Finally, a very important question to ask about the idea of entrepreneurial expertise as proposed by effectuation theory is that; Is it even desirable to start multiple ventures than making one single venture successful. Why does people start multiple ventures? Is it because they see it like playing chess or golf? Will they start another venture if they are incredibly successful in the first business? Will a few outlier cases like Elon Musk ethically suffice us to prescribe it as a standard scientific way of thinking about the world? Does multiple successful marriages make someone marriage expert, or unlucky and bad at marriage?. The key point I am trying to make here is that in domains like chess, multiple success maybe a sign of expertise. In many extremely complex ill structured questions of life it maybe undesirable.

Adjoined with the technology entrepreneurship boom and the glamorization of entrepreneurship as an ideal career choice, there has been a growing interest among various actors in the entrepreneurial ecosystem to provide/get valid guidance in entrepreneurial action. This is also because many have grown increasingly skeptical of the usefulness of traditional methods like business plans. Both scholars and practitioners have responded to this demand by suggesting a variety of heuristics (Manimala, 1992) and prescriptive models (Mansoori, 2017).

Entrepreneurial heuristics includes thumb rules guiding the decisions involved in the start-up and management of a new venture. E.g. Product-market fit, Do Things that Don't Scale, 1000 true fans(or 100), Mom Test, Jobs to be done, etc.

Prescriptive methods or models are principles of thought and action that guide the theoretical and practical aspects of human action (Mansoori 2017). Some examples are; Business Planning (Sahlman, 1997, Delmar and Shane, 2003), Contingency planning (Honig, 2004, Marc Gruber 2017), Discovery-driven planning (McGrath and MacMillan, 1995), Probe-and-Learn approach (Lynn et al., 1996), Lean start-up approach (Blank, 2013; Ries, 2011), Theory-Based View (Felin et al, 2020), Disciplined Entrepreneurship (Sull, 2004), Design thinking (Brown, 2008; Plattner, 2013), Effectual entrepreneurship (Sarasvathy, 2001), etc.

Weakness of existing prescriptive models.

Prescriptive methods of entrepreneurship have been criticized for their lack of rigor and relevance by various scholars (Garbuio et al., 2018; Frank and Landström 2016, Arend et al. 2015, Wolf and Rosenberg 2012; Yordanova, 2022; York and Danes, 2014). Scholars have also shown that entrepreneurship is a naturally complex system, and why it is necessary to study entrepreneurial phenomena by applying the frame of complexity science. This is a major challenge because complex systems are affected by the emergent property, evolutionary dynamics, non-linearity, etc. In complex systems like entrepreneurship past behavior may not predict its future behavior. A small change in any of the initial condition variables can result in a huge difference in the end result.

I argue that most prescriptive models are reductionist in origin and most are ignoring (or ignorant) of complexity by omission or prescription. Most of the models listed above are originated or validated by using traditional linear scientific methods, transforming the results into prescriptive insights for entrepreneurs, suggesting how should they act. Further, it has been found that regression analysis is rated as the most fundamental method in entrepreneurship research scholars should be familiar with, but is inappropriate for studying complex systems (Berger and Kuckertz, 2016). This is especially important when scholars have exposed various types of complexity-effects that cannot be understood by traditional methods. For e.g. Crawford et al(2015) have identified complexity effects in entrepreneurship in the form of power laws, suggesting the use of complexity informed methods.

Another source is anecdotal and idiosyncratic experiences of ecosystem actors like entrepreneurs or institutions like VCs or accelerators. A widely used example that fits the category is the lean startup (Ries 2011), which is often criticized for its explicit demonstration of survivorship bias, a bias of focusing on a successful sample and claiming it to be representative of the entire group.

Following are some of the significant weaknesses of existing methods/models when considering the nature of entrepreneurial complexity.

1. Ignoring context

Entrepreneurship is still a highly decontextualized research field, which is also reflected in the popular prescriptive models we use, and also one-size-fits-all advice received to entrepreneurs. Most of the dominant perspectives still assume that entrepreneurship is the same all over the world, regardless of historical, cultural, institutional, social, and spatial contexts. This is clear from the growing homogeneity in mentorship, accelerator, and entrepreneurship education programs. This has been described as a McDonaldization of entrepreneurship education (Hytti, 2018), a highly standardized menu of activities is served up for student consumption, such as competitions and mini-company creation. According to Baker and Welter(2018), the messiness and complexity of entrepreneurship derive from contextual questions like; Who is driving it?; A community? A family? An individual?. What are the different types of social networks invoked?; friends, family, community, various stakeholders, etc. These are embedded within, and in turn, affect regulatory, and normative contexts at the community, regional, national, and broader levels. Thus entrepreneurship can only be understood within its historical, temporal, institutional, spatial, and social contexts, as these contexts provide individuals with opportunities and set boundaries for their actions. Most prescriptive models promote a model-centric view of the world and are not flexible or intelligent enough to capture crucial contextual insights.

2. Lacks Evolvability

This is a truism. Evolution is a fundamental feature of nature, biology, and human social life. In comparison to biological evolution, the human social system is capable of adapting to change at a faster rate. It is particularly significant in the case of entrepreneurship in that entrepreneurs often act as catalysts of social evolution and also are affected by the smallest of changes in the environment. One of the major sources of weakness in existing entrepreneurship models is the lack of evolvability. Once proposed and written down the model never changes. There is very little scope for contextualization. Even with this vulnerability, most ideas and models in the entrepreneurship domain are self-aggrandizing and self-perpetuating. This often works against the agent's evolutionary potential because it gives a false sense of comfort and certainty, blinding the agent from ever realizing the true emergent nature of entrepreneurial complexity.

3. Fixation Errors and Design Fixation

According to De Keyser and Woods (1990), many critical real-world human problem-solving situations take place in dynamic event-driven environments, where the evidence comes over time and situations can change rapidly. In these situations, people must amass and integrate, uncertain, incomplete, and changing evidence. A major source of human error in dynamic domains seems to be a failure to revise situation assessment as new evidence comes in. I argue that most of the commonly suggested methods like a business plan and lean-startup are highly vulnerable to what Keyser and Woods call Fixation errors. Prescriptive models are especially vulnerable to another type of fixation called design fixation (Jansson and Smith,1991), which refers to the designer’s inability to consider multiple strategies to formulate and solve a design need. It is the direct result of knowledge representation, with human knowledge argued to be organized categorically. These categories are defined by prototypes that exemplify the category (Condoor & LaVoie, 2007). Since linear approaches like Lean startup and business model canvas etc. are based on structured and step-by-step processes, they may add to the rigidity and hence may act as counterproductive in dynamic high-stress environments. This rigidity may lead to design fixation (Garbuio et al., 2018).

4. GRE'isation of entrepreneurship

Eric Ries(2011) introduced the concept of Actionable metric and Vanity metric by taking the example of a test preparation company called Grockit. This made me think about the formidable role Lean-startup played in the matricization of entrepreneurship culture. According to Eric Ries, “When cause and effect is clearly understood, people are better able to learn from their actions. Human beings are innately talented learners when given a clear and objective assessment”. As a combo, the lean-startup has introduced many other concepts like Validated learning, Innovation accounting, etc as part of their metricization drive. Because of this, I thought it is more than appropriate to call the role played by the lean-startup in bringing measurement culture to the entrepreneurship domain as the “GRE’isation of entrepreneurship”.

The problem here is the complete ignorance of the complexity of the real world. In complex domains, you can't have the perfect objective answer or action. There is not even an objective goal. Everything is dynamic and co-evolving. Secondly, In a complex domain like entrepreneurship, emergent property is a key feature. For example, the idea you initially had can emerge into a radically new formation, an emergent property that you could have never imagined before. For E.g. Instagram was started as an HTML5 supported location-based service; Facebook was started as an app to compare two people’s pictures and the rate which one was more attractive. When you introduce a metric to a radically changing, complex, dynamic, and emergent system like a startup, it will amount to the manifestation of Goodhart's Law that suggests that "when a measure becomes a target, it ceases to be a good measure" (Strathern, 1997; Manheim and Scott, 2018). When we set one specific goal, people will tend to optimize for that objective regardless of the context and consequences. It is extremely troublesome if the metric is introduced as a target in a domain that is dynamic and emergent like that in entrepreneurship. Here the measurement based on metric itself is not a problem, but the effect of such metric fixation(Muller, 2021) will stagnate the startup and uncouple the venture from ecological realities and opportunities.
 
5. The Know it all and Goal-directed visionary entrepreneur.

Most of the prescriptive models are driven by the idea of objective goal-driven entrepreneurs who know the particular direction they want to take. Saras Saraswathy articulates such goal-directed behavior as causation. According to her, “Causation processes take a particular effect (a goal, direction or action) as given and focus on selecting between means to create that effect”(Sarasvathy, 2001, p.245). The causal goal-driven nature of Lean-startup and customer development was also attested by Steve Blank. According to him, he invented the Customer Development process trying to solve two startup problems. "First, most Silicon Valley startups were (and primarily still are) technology-driven. They are founded and funded by visionaries who already have products (or product ideas based on technology innovation) and now need to find customers and markets. (Think of the early days of Intel, Apple, Cisco, Google, Facebook, Twitter, etc.). Second, burn rate and dwindling cash meant startups had to find these customers and the attendant product/market fit rapidly – before they ran out of money" (Blank, 2014).

First of all, as a descriptive account, none of the example companies he listed, like Intel, Apple, Cisco, Google, Facebook, Twitter, etc, were the product of visionary goal-driven linear action. There is no evidence of it, and the stories of these companies paint a different picture----a picture of unforeseen direction change, growth and opportunities. This was not the case of an algorithmic pivot, but a nonlinear emergence that is dependent on contextual intelligence. It was further demonstrated by the works of Saras Sarasvathy(2001), in which she revealed how entrepreneurs prefer effectual logic over causal logic. Effectuation processes take a set of means as given and focus on selecting between possible effects that can be created with that set of means. Greg Fisher in his comparative study of effectuation, causation, and bricolage(Fisher, 2012) has shown that there were no cases where only the behaviors associated with causation were responsible for the development of the venture. Studies have also ascertained that a substantial part of entrepreneurial behaviors occurs without ex-ante reasoning (Wiklund, 2019). According to Lerner et al(2018), non-deliberative impulse-driven behavioral logics can also be the basis for business venturing.

Secondly, in a prescriptive sense, a know-it-all goal-driven visionary, who wants to implement his ideas as efficiently as possible is troublesome. It reduces exploration. It can negatively affect openness to emergent serendipitous opportunities. Further, according to Penaluna and Penaluna(2021), clear goals and definitions may hinder opportunities to develop divergent thinking that takes account of multiple perspectives and alternative views. The ‘rush’ to answers may lead to premature articulation, with a basic iterative solution or first idea taking priority over the potential range of alternatives.

6. Possessive individualism

Most prescriptive models are based on the idea of the sole entrepreneur making decisions. This relates to the idea of Possessive individualism, which is the assumption that capacities, beliefs, and desires, etc. are possessions of an individual (Macpherson, 2010). In this approach, the individual is viewed atomistically as ‘essentially the proprietor of his own person or capacities; which include: intelligence; cognitive capacities such as memory; the ability to process information; and such personality characteristics as desires and wants, crucially ‘owing nothing to society for them’(MacPherson, 1962, p. 3). The reality is far from the case. According to Clark and Chalmers(1998), real-world cognition and decisions are extended outside of our brain. They present the idea of active externalism in which objects within the environment function as a part of the mind. They argue that the separation between the mind, the body, and the environment is an unprincipled distinction. This suggests that entrepreneurial cognition and decisions are not simply happening inside the entrepreneur's brain, but extended outside. 

Another way to view it is the distributed nature of real-world decisions(Rapley, 2008; Schneeweiss,2012; Charles et al, 1997, 1999). In contexts like entrepreneurship, there are multiple stakeholders with diverse and conflicting beliefs, preferences, and goals. They all are part of entrepreneurial cognition and decisions. This distributed nature of decisions in entrepreneurship is partially influenced by the distributed nature of expertise in complex social domains (Edwards, 2010). It is not necessary that an entrepreneur must be an expert in finance, accounting, programming, law, etc. Such expertise is distributed(and or extended) across various individuals(lawyer, doctor) institutions(law enforcement, companies), artifacts(tools, software), etc. It is not even necessary that the entrepreneur know the entrepreneurial core activities. He/She can still win in-case he/she is in the right high network place, get good people to mentor and work, get access to specialized institutions, have a rich family to support, etc. Further, In a complex domain, decision-makers(entrepreneurs or managers) are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other. These problems are labeled by Ackoff as messes (Ackoff 1978; Bennet et al, 2008).

7. Structure and Issue of Agency

Agency is described as an individual’s ability at any given point in time, to act independently in order to change the internal or external environment (Bandura, 2001; Campbell, 2009; Hitlin & Elder, 2007). Scholars have long observed that the structure of society that includes other agents and artifacts will have influence over the individual agency. Thus it can be observed that once designed and introduced into the interactional scene by humans,----texts, artifacts, and objects of any kind make sense and have an agency on their own (Caronia and Mortari, 2015; Bruno Latour, 1996). I argue that most of the popular prescriptive models have agency of their own, and most of them are designed to highjack entrepreneurial agency by providing a model-centric view of the world. This can result in developing commitment to one-size-fits-all models that may result in the agent's evolutionary, learning, adaptive potential being seriously compromised. Further, In the descriptive sense, most of the prescriptive models assume that entrepreneurs possess excessive powers of agency (Kitching and Rouse, J.2020). This is evident from the inherent assumption that regular entrepreneurs are using exactly what the model prescribes, almost like an algorithmic precision. In the practical sense, the models take control of the entrepreneur's agency by prescribing things to do and think. It is suggestive of what to see, what to do, what not to do, etc.

8. Local optima, Bounded rationality, and Local search

Research has found that bounded rationality challenges new ventures differently than it does to established firms and that entrepreneurs appear to systematically satisfice (Simon, 1955) prematurely across many decisions (Cohen et al 2019). Most of the prescriptive models assume(by omission or prescription) that the entrepreneurs are able to review all information and make a rational decision in their best interest. Research on human bounded rationality suggests these assumptions are wrong. Another dimension of this issue is that entrepreneurs generally start their venture with a preference to ‘local search’, whereby they primarily explore opportunities that fit with their existing disposition and knowledge. This leads to the identification of local optima rather than global optima (Stuart & Podolny, 1996; Keinz & Prügl, 2010; Rosenkopf & Nerkar, 2001). An empirical study done by Shane(2000) has also shown that entrepreneurs tend to identify opportunities that were either already known to them in the past or are closely related to their existing stock of knowledge. This phenomenon is also known as local search bias (Stuart & Podolny, 1996; Rosenkopf & Nerkar, 2001). The local search behavior is of inevitable importance but the problem is that focusing on existing knowledge and expertise alone can Impede the entrepreneur from exploring distant solutions. I argue that most of the existing entrepreneurship models reinforce and augment bounded rationality and local search behavior. They are optimized for local search behavior in which they may make entrepreneurs more susceptible to being blinded by their own bounded rationality. For a design to be sustainable it must acknowledge that the end-users are fallible humans with bounded rationality. It must take into account cognitive limitations, bias, and dispositions. Models must anticipate the potential issues that could arise from bounded rationality and compensate for them with built-in design solutions.

9. In exclusion of others by omission or prescription.

In complexity and evolution, heterogeneity is an asset. The more the relevant elements of variation present(requisite variety) in a system, the more resilient and evolvable a system will become. Despite that, most models are proposed as solutions for the problems of the existing one. It is often pitched as better than the previous one. E.g. Plan Vs Lean startup. Using the antecedent as a frame to marginally improve the model is also the standard practice. While this is the reality, studies have shown that real entrepreneurs don't behave according to the dictum of any prescriptive models. Fisher(2012) for example investigated behavior underlying the venture founding process using effectuation, bricolage, and causation has found that entrepreneurs often use elements of all the 3 theoretical models and that there were no cases in which only the behaviors associated with causation were responsible for the development of the venture. Mansoori and Lackéus(2019) in their analysis stressed the possibility of using multiple methods complementing each other, which is a standard practice in weather prediction(ensemble models). Sarasvathy (2001) also asserted the point that both causation and effectuation are integral parts of human reasoning that can occur simultaneously, overlapping and intertwining over different contexts of decisions and actions” (p. 245). This shows that real-world decisions in a complex adaptive system cannot be understood with linear thinking or binaries. Since it is impossible to know various emergent decision contexts beforehand, it is always ideal to design solutions for such variables and contingencies. That means not excluding ideas, models and theories, etc by prescription or omission.

In this part I have discussed:

*The nature of complexity and demonstrated why entrepreneurship is called a complex domain.

*The nature of decision making and expertise in complex domains like entrepreneurship.

*The weakness of existing thinking, particularly the dominant prescriptive models used in the domain of entrepreneurship.

Taking all of this into consideration, in the following part, I am introducing a complexity-based design solution(a framework) that embodies dispositions like ecological perspective, self-organization, evolvability, diversity, adaptability, sense-making, learnability, agency, praxis, feedback, etc. 

Part 2


Building Of A Complexity Friendly Framework

Since entrepreneurship is a complex domain, proposed design solutions must recognize complexity as a core design challenge. It must also consider the disposition and bounded rationality of the decision-making agent in question. Following my analysis of entrepreneurial complexity and weakness of contemporary thinking, I propose the incorporation of some important dispositional capabilities necessary for a sustainable model for entrepreneurial action.

They;

*Must begin with the right view: The right worldview (Dent, 1999) is a necessary precondition to effective design. If the world view is reductionist or simplistic, ignoring important unknowns, the design will reflect this weakness.

*Must acknowledge and promote self-organization: Self-organization (Heylighen, 2008) refers to the feature of systems that appear to organize themselves without external direction, manipulation, or control. This promotes ecologically grounded evolution and dynamics. A sustainable, complexity-friendly model must promote self-organization instead of one-size-fits-all models, rigid prescriptions, or top-down order.

*Must promote exploration: While self-organization may promote local optimization, the dispositional capability for exploration (Gupta et al, 2006) is necessary to facilitate the journey towards global optima. A sustainable complexity friendly model must have the capability for continuous multidimensional exploration.

*Must promote diversity (Page, 2010) integration (Martin, 2009), and inclusiveness (Pless and Maak, 2004): Decisions in a complex domain like entrepreneurship cannot be understood with linear thinking or binaries. Since it is impossible to know various emergent decision contexts beforehand, it is always ideal to design solutions for such variables and contingencies. That means a design must promote diversity and not exclude ideas, models, theories, etc., by prescription or omission.

*Must be contextually adaptive: Every context is unique, and thus we need solutions that are adaptable and usable for various contexts, as it emerges. Adaptive systems involve designing the elements of a system to find by themselves the solution of the problem. Like this, when the problem changes, the elements are able to dynamically find a new solution. We can say that such a system self-organizes (Gershenson, 2007).

*Must provide evolvability to the agent and must have evolvability of its own: Evolution and evolvability (Pigliucci, 2008; ScienceDaily, 2013) are a fundamental feature of nature, biology, and human social life. A sustainable entrepreneurship framework must have the evolutionary potential of its own, and also must enable evolvability to the entrepreneurial agents.

*Must protect agency from hijack: Prescriptive models have agency of their own, and are designed to hijack entrepreneurial agency by providing a model-centric view of the world. Commitment to one-size-fits-all models may result in the agent's evolutionary, learning, adaptive potential being seriously compromised. Thus, a sustainable complexity-friendly model must protect and promote appropriate levels of entrepreneurial agency (Garud and Karnøe, 2003) and autonomy.

*Must acknowledge the dispositional state, including ignorance and weaknesses: Most of the existing entrepreneurship models reinforce and augment bounded rationality. A sustainable complexity-friendly model must acknowledge the possibility for bounded rationality, ignorance, and weaknesses. It must have inbuilt dispositional capabilities like distributed sense-making (Weick, 2005; Fisher, 2012) and descriptive self-awareness (Snowden, 2002), etc. to continually scan inwards and outwards for issues like ignorance, weakness, or bounded rationality.

*Must encapsulate various complexity-friendly functional dispositions: A complexity-friendly model must also be designed with a usability perspective in mind. It must encapsulate various complexity-friendly functional dispositions(above listed) into a single framework so that an ecology of ideas, in its dynamics, will transfer to the users, not just one or two ideas.

*Must acknowledge provisionally imperative nature of solutions, models and frameworks: This means no one-size-fits-all solutions can exist in complexity. According to Cillers(2002), In complexity, interpretations are contingent and provisional, pertaining to a certain context and a certain time frame(p. 121-122). This leads to the idea of Provisional imperative (Preiser and Cilliers, 2010; Woermann and Cilliers, 2012).

*Must strive forward as a continuous work in progress in a perpetual construction (Prigogine, 1997): Once proposed and written down, most models or prescriptive methods never change. This is a weakness when considering the speed of radical changes happening around us. Even with this vulnerability, most ideas and models in the entrepreneurship domain are designed to be self-aggrandizing and self-perpetuating. A sustainable complexity friendly model must have the capability to change itself according to challenges.

In the following part, I will demonstrate the design of this framework as a complexity-based design framework to aid entrepreneurial action. This includes;


1. The right world view 
2. Self-organization.
3. Constraints(context-sensitive and context-free constraints)

Part 2(l)


The world view

The right worldview (Dent, 1999) is a necessary precondition for sustainable and effective design. If the world view is reductionist or simplistic, ignoring important unknowns, the design will reflect this weakness. As discussed previously, entrepreneurship is a naturally complex system, and it is necessary to study entrepreneurial phenomena by applying the frame of complexity science (McKelvey, 2004). Despite this reality, most entrepreneurship models and perspectives are reductionist in their origin, prescription, or omission. To solve this weakness this framework adopts an ecological world view, i.e a complex, dynamic, connected, and evolving ecology.

In order to understand the ecological view, let's first go through some of the existing world views or alternatives. After that, I will introduce the ecological view to show how it is different from other world views.

1. Single Model world view: This is the world view based on a single model, tool, or method which assumes superiority over all others. This can be equated to what Charlie Munger(1994) calls, “Man with a Hammer” syndrome, which is the idea that if an individual has only one tool or model(e.g. hammer), he'll approach all of his problems with the same solution, i.e. a hammer. For a man with a hammer, everything around him will seem like a nail. Most models in entrepreneurship come under this category. They are proposed as solutions for the problems of the existing one and are pitched as better than the previous one. E.g. Lean startup (Blank, 2013; Ries, 2011) Vs Business planning or any other model that is proposed as vastly superior to others. 

2. Multiple-Model Ensembles: This world view suggests the complementary use of multiple models or methods. This is the same idea as suggested by Scott Page(2018) and the ensemble forecasting model (Leutbecher and Palmer, 2008) used in weather prediction. In entrepreneurship, multiple model world view can be found in many scholarly works. For e.g. Sarasvathy(2001)stressed the importance of both effectuation and causation. Mansoori and Lackéus(2019) suggested using multiple methods complementing each other. This world view is by default inclusive of the previous one. A very common place in which multiple models are used in this way is the university curriculum. Note that works of scholars like Page, Sarasvathy, Lackéus, Mansoori, etc are generally not exclusionist, and the above analysis should be viewed only in the context of specific works or comments cited

3. Cognitive-Diversity world view: This world view suggests using not only formal models or methods but all kinds of cognitive diversity. E.g. Models, methods, theories, heuristics, etc. Scott Page defines Cognitive Diversity as “Differences in information, knowledge, representations, mental models, and heuristic, to better outcomes on specific tasks such as problem-solving, predicting, and innovating (Page, 2017,14-15)". To me, the weakness of this view is in its cognitive reductionism. Even though this world view is by default inclusive of all the previous ones, any cognitive alone worldview can be criticized for lack of ecological basis (Gibson, 1979; Varela et al., 1991; Clark, 1997).

4. Holistic Diversity worldview: Apart from cognitive diversity, Page(2017) also talks about identity diversity, i.e. the differences in race, gender, age, physical capabilities, and sexual orientation. There are other frames to view diversities like; Biodiversity, which can be defined as variety in all forms of life—from genes to species to ecosystems (Wood, 1997); Cultural diversity, which can be defined as differences, such as in language, religion, dress and moral codes that exist between people according to race and ethnicity (Leveson et al, 2009; Kossek and Zonia, 1993). Tool diversity suggesting for the use of multiple tools for the same task to increase output accuracy by reducing systematic errors (Song et al, 2018). Artifact diversity refers to the number of different classes of artifacts and their relative proportions (Lyman and O'Brien, 2000). In short, this world view suggests the existence of not only Cognitive-Diversity but also, all other kinds of diversity, i.e. diversity in people, biology, identity, networks, information sources, relational-expertise, institutions, culture, location, specialization, artifacts, tools, etc. This world view is by default inclusive of all the previous ones.

5. Ecological View: This includes a continuous effort to capture the true ecological reality in its dynamics. In addition to the previously listed holistic diversity, this view is inclusive of realities like complexity, dynamics, evolution, connectedness, interaction, etc. Complexity involves features and interactive dynamics as in a complex adaptive system. The idea of evolution suggests that existing elements interact, evolve, co-evolve into new diversities, variations, etc. It must also involve cumulative cultural evolution(and intelligence) as proposed by human cultural evolution studies (Tennie et al, 2009; Mesoudi and Thornton, 2018). In addition, an ecology is connected, hence no clear local-global separation is possible. This is particularly significant in human social ecology. Finally, the ecological view involves the use of contextualized sense-making that suggests that every human context is an emergent property and hence each context has unique types of characters. This world view is by default inclusive of all the previous ones.

 
The Ecological world view of the framework has three important dimension.

a) The Macro-ecological dimension

The ecological world view fundamentally implies the inseparable embeddedness, interconnection, and interaction between various parts and components of a system. Apart from our previous discussion based on diversity, the Ecological worldview can be better understood scientifically by differentiating it from the Newtonian and Darwinian worldviews or window (Pendleton and Brown, 2018, P. 35). While the Newtonian worldview represented a reductionist approach with an eternally unchanging universe, the Darwinian worldview focused on processes involved in evolution and suggests that the characteristics and behavior of something is contingent on its history. Even though the Darwinian worldview introduced a very important shift in perspective, it cannot explain the more radical changes that structure our universe (Pendleton and Brown, 2018; Ulanowicz, 2009). To Pendleton and Brown(2018, P. 35) ecological window represents complex systems processes that are dependent upon the vibrant exchange and flow of energy and matter between many organisms, their communities, and their environment.

According to Capra(1996, p.3), the ecological worldview grew out of an awareness that contemporary problems cannot be understood in isolation. They are systemic problems, which means that they are interconnected and interdependent. He uses the term ‘deep ecology’ to highlight the inescapable connections and mutual influences among the human, natural, physical, and constructed environments implicit in this view of reality (Mitchell and Sackney, 2011, p.2). The deep ecology(Arne Naess, 1973) is different from shallow ecology, which is human-centered. Shallow ecology views humans as above or outside of nature, as the source of all value, and ascribes only instrumental value to nature. Deep ecology does not separate humans--or anything else-from the natural environment. It sees the world not as a collection of isolated objects, but as a network of phenomena that are fundamentally interconnected and interdependent (Capra, 1996). 

This is a perspective to ecological complexity that involves properties like holistic interconnection, interdependence, heterogeneous agents, non-linear dynamics, feedback loops, emergent property, self-organization, dynamics of evolution, attractor behavior, open exchange of energy and information with their environment, etc.

b) The Micro-ecological dimension

A practical or working definition of ecology can change with the unit of observation, domains or problems, etc. Thus, ecology can be defined in a biological perspective as the scientific study of the interactions between organisms and their environment, while for e.g. if we are looking at the disease ecology, it can be defined as the ecological study of host-pathogen interactions within the context of their environment and evolution (Kilpatrick and Altizer, 2010). Similarly, information ecology may mean the interaction and co-evolution of technologies, human beings and the social environment (Pekkarinen et al, 2020). Further, ecology can also be of a subdomain(problem or topic), like for e.g. the phenomena of human suffering which comes under mental health. Thus, “ecology” can refer to the network of forces acting on and by the people suffering and those around him or her (Jadhav, 2015).

With this practical understanding, this framework operates in the entrepreneurial ecology, where the entrepreneur interacts with multiple agents, institutions, artifacts, problems, etc, to form a complex adaptive interdependent system that emerges in the human socio-cognitive-material space. Thus, on a practical level, the worldview can be viewed perfectly aligned with what J. J Gibson calls the inseparability of animal and the environment. Here animal means entrepreneur, organization, artifacts, etc. According to Gibson(2014), “it is often neglected that the words animal and environment make an inseparable pair. Each term implies the other. No animal could exist without an environment surrounding it. Equally, although not so obvious, an environment implies an animal (or at least an organism) to be surrounded".

c) The enactive ecological dimension(via Self-organization and Constraints)

The enactive ecological dimension of the framework is for acting in the world which involves the application of 2 important concepts, Self-organization, and Constraints. This is based on the understanding that complex adaptive systems like entrepreneurship have a tendency to self-organize (Kauffman, 1995) under various constraints. The framework uses constraints-based dynamic design, an idea adapted from the Constraints-Led approach (Davids et al, 2008). The design uses Alicia Juarrero's(1999) conception of Context-free and Context-sensitive constraints, to enable the entrepreneur to self-organize to find better solutions by themselves.

These ideas will be expanded in part 2(ll) and part 2(lll). The enactive ecological worldview(via Self-organization and Constraints) forms the core praxis of the framework. 

Part 2(ll)


 Effectual Self-organization

As I have mentioned in part 1, Self-Organization refers to the feature of systems that appear to organize themselves without external direction, manipulation, or control. This promotes ecologically grounded evolution and dynamics.

In this part I will discuss following core ideas;

First of all, I will explore the idea of self-organization and list down some of the core feature of self-organizing systems as discussed by various scholars.

Secondly, I will discuss the autonomy problem in human social self-organizing systems, and I will introduce intermediary enabling constraints(heuristics) to solve this problem, which I call Effectual self-organization.

Thirdly I will talk about effectuation theory in entrepreneurship that was proposed by Sarasvathy(2001). Here I will discuss my hypothesis that effectuation as a theory might be compatible with the idea of self-organization in complex systems.

The concept of self-organization in a modern sense was first introduced by Ashby in 1947. It was further developed by many scholarly works like Haken(1977), Prigogine(1973), Kauffman(1993), and many others. Self-organization refers to the emergence of stable patterns through autonomous and self-reinforcing dynamics at the micro-level (Kauffman, 1995; Ska˚r, 2003; Anzola et al., 2017). In other words, self-organization is a phenomenon in which local interactions lead to global patterns emerging. The self here suggests autonomy and self-maintenance. It is also important to note that self-organization is a ubiquitous feature of open complex adaptive systems.

According to complexity scholar Dirk Helbing, what makes complex systems so difficult to control is that they have a natural tendency to self-organize driven by the inherent forces between the system components(Helbing, 2014). Once scholars started to realize that self-organization is a ubiquitous feature in open complex adaptive systems, they started to study this phenomena and found that self-organization is an intelligent way in which nature organizes itself to solve complex adaptive system problems in a distributed and adaptive manner; which cannot be replicated by top-down order or top-down prescription.

According to Cilliars(2002), "the capacity for self-organisation is a property of complex systems which enables them to develop or change internal structure spontaneously and adoptively in order to cope with or manipulate, their environment. It is a bottom-up process where complex organization emerges at multiple levels from the interaction of lower-level entities. The final product is the result of nonlinear interactions rather than planning and design; and it is not known a priori (De Roo, 2016). This can be contrasted with the top-down approaches where planning precedes implementation, and the desired final system is known by design.


This suggests that, in self-organization, there is no hierarchy of command and control and there is no planning or managing, but there is a constant re-organizing to find the best fit with the environment. The system is continually self-organizing through the process of emergence and feedback. It changes the relationships between the distributed elements of the system under influence of both the external environment and the history of the system (Cilliers, 2002). This emergence happens naturally from the interactions within a complex system; they do not need to be imposed top-down in a centralized way. For example, termites are known to construct the highest structures on the planet relative to the size of the builders. Yet there is no chief executive among termites, no architect termite, and no blueprint. Each termite acts locally, following only a few simple shared rules of behavior within a context of other termites also acting locally. The termite mound emerges from this process of self-organization (Plsek and Greenhalgh, 2001).

We can find similar patterns across all kinds of complex adaptive systems; whether it is the dynamics of ants, whether it is the dynamics of bees in a beehive, or people walking in a street, or in the case of bird murmuration or in the market dynamics.

In order to get a systematic understanding of what constitutes self-organization, it is ideal to understand what various scholars consider as core and common characteristics of self-organization. Two recent scholarly works De Wolf and Holvoet(2004) and Gilbert et al(2015) have discussed these commonly found characteristics/factors across various definitions. Both of them found 4 factors each. De Wolf and Holvoet(2004) listed Increase in Order, Autonomy, Adaptability or Robustness w.r.t. changes, Dynamical i.e. far-from-equilibrium. Gilbert et al(2015) listed four factors that are common across definitions; Pattern formation, Autonomy, Robustness and resilience, Dynamics.

Combining both will give the following 5 characteristic features.

1. Increase in Order

Self-organizing systems have the ability to spontaneously increase their order and complexity over time. This means that they can generate patterns and structures that are not present in their initial state. This increase in order is driven by the interactions and feedback between the components of the system. According to De Wolf and Holvoet(2004), one important characteristic of self-organisation is the ‘organisation’ part of the concept which refers to the arrangement of selected parts so as to promote a specific function. This restricts the behaviour of the system in such a way as to confine it to a smaller volume of its state space. In essence, this organisation or increase in the order of the system behaviour enables the system to acquire a spatial, temporal, or functional structure.


2. Pattern formation.

The feature of pattern formation is closely associated with the first key feature, "increase in order". This suggest that self-organizing systems are capable of forming patterns and structures at different scales, from the microscopic level to the macroscopic level. To Gershenson et al(2020), Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems. The phrase “self-organizing system” is used when an external observer perceives a pattern in a system with many components, and this pattern is not imposed by a central authority among or external to those components, but rather arises from the collective behavior of the elements themselves.

Self-organizing systems are also capable of forming patterns and structures at different scales, from the microscopic level to the macroscopic level.

3. Autonomy

This refers to the absence of external control. A system needs to organise without interference from the outside. Further, the lack of external control and autonomy does not mean that such a system can have no input at all. Input is still possible as long as the inputs are no control instructions from outside the system. The decision on what to do next should be made completely inside the system, i.e. the system is autonomous.

To Gilbert et al(2015), Social systems pose two particular challenges for this feature of self-organizing systems. First, there are asymmetric power relations between and within levels. Humans are intentional actors and can have designs on controlling or influencing a system through individual or institutional action, for example, by the implementation of public policy. Second, the boundaries of social systems and subsystems are difficult to draw. Social science often ignores or is vague about boundaries, both for theoretical and methodological reasons.

4. Robustness, Resilience, and Adaptability.

Self-organizing systems are robust, resilient, and adaptable. This means that they can maintain their functionality and stability even in the face of internal and external disturbances, failures, or changes. Even if some agents fail or are removed, the overall patterns or functions still persist. This robustness and resilience can be attributed to the diversity and redundancy of the components, as well as the distributed nature of control and regulation in a self-organizing system.

To De Wolf and Holvoet(2004), in self-organizing systems, robustness is used in terms of adaptability in the presence of perturbations and change. A self-organising system is expected to cope with that change and to maintain its organisation autonomously. This adaptability implies the need for the system to be able to exhibit a large variety of behaviours.

On the other hand, according to Gilbert et al(2015), although robustness and resilience are two terms, they are often used interchangeably. Robustness refers to a system’s ability to resist change, whereas resilience refers to a system’s ability to adapt or recover from change. Further, both concepts could be used for the same process, depending on the observer’s perspective, meaning it is not always clear whether a social system is robust or resilient.

5. Dynamics

Self-organizing systems are dynamic, which means that they operate far from thermodynamic equilibrium. The non-equilibrium state is characterized by continuous exchange of energy, matter, and information between components, and the generation of complexity. It is important to note here that equilibrium is death. Self-organization occurs in systems that are far from thermodynamic equilibrium and operate at the edge of chaos. The far-from-equilibrium dynamics provide energy and resources needed for self-organization and complexity generation, while the edge-of-chaos dynamics can be seen as providing criticality and flexibility for adaptation in case of perturbation or significant changes.

According to De Wolf and Holvoet(2004), an essential property of self-organisation is that it is a process. Over time, there is an increase in order, i.e. a dynamic towards more order. This can otherwise be viewed as a far-from-equilibrium system that is always in dynamics in order to maintain its organised structure(Glansdorff and Prigogine, 1978). A far-from-equilibrium system is more sensitive to changes in the environment, but also more dynamic and capable to react. That is why complex systems are understood as dynamical systems.

In a complex self-organizing system like entrepreneurship, we cannot know the causal structures a priori. Only after something happens we can even attempt to understand the causal structures in a complex system. This is because we are dealing with a complex system where exist multiple causes, emergent causes, cumulative emerging causes, and circular causation. In this kind of system, input doesn't equals to output, indicating non-proportionality.

Additionally, there exist multiple levels of hierarchy of structure and causation in such systems, where sociological and particle physics levels are simultaneously in dynamics, which adds to the complexity. Furthermore, the temporal dimension poses a significant challenge that It is impossible to determine the extent of historical analysis required to unravel the causal nature of a complex adaptive system. Attempting to comprehend the intricate causal structures within such a system is simply unattainable. Our understanding thus is inherently limited, and we can only grasp certain observable aspects, which are also inherently complex because of high levels of non linear dynamics and emergence.

This is why in a complex adaptive system like entrepreneurship, causal structures cannot be known a priori. Anyone claiming to have complete knowledge of causal structures in a complex system is either misleading you or misunderstanding the nature of complexity or assuming an open system to be a closed structured system. However, this difficulty doesn't imply that there is no causality in a complex adaptive system. Causality is fundamental and important, but the nature of causality in a complex adaptive system is dispositional, i.e. we can only know the current dispositional propensity of the system in relation to the disposition of the state space. Both of these dimensions are crucial to understanding the nature of complexity and entrepreneurial complexity.


Dispositional propensity(Mumford, 2003; Anjum and Mumford; 2018).

In a complexity perspective, the 'phase space'(or state space) is the representation of all possible instantaneous states that can occur in a particular system (Butkovskiy 1990, Sayama 2015). It can be thought of as the space within, around, or adjacent to which a complex adaptive system can self-organize and emerge. This points to the fact that, while we may not be able to know precisely how a system might change, we do know that it will be most likely within the phase space. Since a self-organizing system is in continues dynamics and since the dynamics is non-linear and emergent, we cannot predict specific dynamics that is going to take place in each contexts. Instead, the idea of phase space suggests that we can only know the current dispositionality or dispositional propensity of a particular system in relation to the disposition of the state-space. This was very well articulated by Philosopher Alicia Juarrero(1999, 2000) when she says that, "The probability that a system will do x next depends on its present location in the current overall landscape, which in turn is a function both of its own past and of the environment in which it is embedded".


Dispositionality or dispositional propensity refers to the inherent potential or propensity or tendency of the system to behave in a certain way. According to philosopher Stephen Mumford (Mumford, 2003), "disposition is a term used in metaphysics usually to indicate a type of property, state or condition. Such a property is one that provides for the possibility of some further specific state or behaviour, usually in circumstances of some specific kind. Terms such as causal power, capacity, ability, propensity, and others, can be used to convey the same idea". Further, the idea of dispositional propensity has deep foundations in metaphysics and the philosophy of science, which can be traced back to ancient philosophers such as Aristotle and Saint Aquinas. When it comes to modern philosophy, we can observe this line of thinking reflected in the works of philosophers like Charles Sanders Peirce and Karl Popper. Charles Sanders Peirce believed in probabilities as propensities, emphasizing the inherent tendencies of certain processes or events to occur. As for Karl Popper, according to Robert Ulanowicz(2002), "To Popper, the world is not closed. More precisely, it is not a deterministic clockwork, as Descartes would have had us believe. Rather, it is composed of "propensities" — the tendencies that certain processes or events might occur".

This idea captures the inherent potential for change, adaptation, and interaction within complex systems. By acknowledging the concept of dispositionality, we recognize that complex systems are not static entities but rather dynamic entities with a predisposition towards certain dynamical features, patterns, behaviors, and responses. Thus the nature of dispositional propensity is inclusive of key dynamical features of self-organizing systems like increase in order, pattern formation, adaptability, robustness and resilience w.r.t. changes, emergence, non linearity, feedback loops, criticality, fluctuations, attractor states, phase transitions, co-evolution, degeneracy, coherence, heterarchy, spontaneous order, synergy, etc. Further, It is important to recognize that in human systems like entrepreneurship, dispositional propensities are reflected in the distribution of influence, preferences, and dynamics among the actors, institutions, and artifacts involved. These factors collectively contribute to the overall dispositional tendencies of the system.


                                                                                  Examples

Let's explore some examples to shed light on the idea of dispositionality or dispositional propensity. These examples will demonstrate the fact that in complex self-organizing system like entrepreneurship, we cannot know the causal structures a priori. We can only know the current dispositional propensity of the system, and actors must base their actions based on these propensities. Let me demonstrate this using some examples.


Parental Intervention Based on Dispositional Propensity:

A great instance of taking action based on dispositional propensity is a father intervening when a toddler climbs a chair to reach a table. Imagine a curious toddler eyeing a chair, trying her hands to reach the table which is slightly out of reach for the toddler. The attentive father, sensing the child's natural inclination to climb, quickly steps in. He knows the potential risks involved and carefully assesses the situation. Should he let the little one continue their adventure, or intervene to keep them safe? With a mix of concern and love, he makes a decision based on his understanding of the child's disposition. Safety comes first, always. The father recognizes the inherent propensity of the toddler to climb and predicts the propensity of a potential fall. By closely monitoring the situation, the father can take appropriate actions to ensure the child's safety. The decision to intervene or allow the child to continue climbing depends on the potential for harm in the given environment. This example beautifully illustrates the application of dispositional propensity in everyday life. It highlights the fact that understanding and responding to the inherent tendencies and inclinations is natural part of life and requires no external coach. The father have no ways to know the causal structures a priori. If he could know for a fact that the child will never fall based on causal structure identified by elite researchers, he could avoid being concerned about the toddler altogether. But unfortunately that can never happen.

Gaze Heuristic and the ball

The Gaze heuristic is another demonstrable example of taking action by understanding the current dispositional propensity of a system. In this, a player adjusts their running speed to maintain a constant angle of gaze by monitoring the angle between the eye-to-ball line and the ground. By doing so, the player can anticipate and adapt their movements accordingly. Ultimately the perceived dispositional propensity of the ball is what drives the application of gaze heuristics. This embodied heuristics requires the ability to fixate on an object, run, and adjust running speed—a skill developed early in human development. This action is also not powered by knowing any kind of future certainty that is based on identifying specific causal structures.

Propensity of Lions

We are aware that lions possess a natural inclination to hunt and feed on other animals, but this disposition is not an absolute certainty. It is not a pre-determined causal structure that can be known in advance; rather, it is a dispositional propensity. This is because Lions can also fall victim to other animals. Nature presents scenarios where a lion may encounter unforeseen circumstances. For instance, it could be attacked by a larger animal like an elephant, resulting in paralysis, making it vulnerable to being preyed upon by smaller creatures or even facing aggression from a group of hyenas. Such events are common occurrences in the natural world. Although lions have a strong propensity to consume other animals, it is crucial to understand that this propensity is not an inflexible causal structure. Its manifestation depends on the current dispositional propensity of the lion in relation to the disposition of the state space it finds itself in. To illustrate, in a typical forest habitat, a lion can fulfill its predatory nature by hunting and feeding on other animals. However, if placed in a Jurassic Park-like environment where dinosaurs reign as the dominant predators, the dynamics would inevitably change. Therefore, the key point to remember is that while the disposition of a particular system, such as a lion's inclination to consume other animals, holds significance, it is also relative to the disposition of the state space it interacts with.

What is the dispositional propensity of Cristiano Ronaldo scoring a goal in the next few seconds?

Consider the question: What is the dispositional propensity of Cristiano Ronaldo scoring a goal in the next few seconds? Similar to the previous examples, we cannot ascertain the causal structures in advance. Instead, our focus lies on understanding the current dispositional propensity of the system. Ronaldo, being an exceptionally skilled player, undoubtedly contributes to the current dispositional propensity for scoring a goal. However, it is important to recognize that this disposition is influenced also by the disposition of the state space he operates within. Factors such as the positioning and actions of other players, the position of the ball, the goal post, and the goalkeeper are all equally crucial in determining the outcome. The interaction between Ronaldo's propensity and the disposition of the state space collectively shapes the likelihood of him scoring a goal in the next few seconds.


Bird Murmuration

In bird murmuration, the inherent tendencies of the flock are manifested through the application of three simple rules: separation, alignment, and cohesion. Additionally, other factors such as wind, rain, friction, predators, and physical barriers like trees or mountains can also influence the flock's behavior. The birds avoid crowding their neighbors, align their movements with the average heading of their neighbors, and steer towards the average position of their neighbors. By following these rules and responding collectively to external factors, the flock exhibits an incredibly realistic and complex motion, forming mesmerizing patterns that would be difficult to achieve otherwise. While we cannot predict the exact shape or form of a bird murmuration in advance, we can try to understand the current dispositional propensity of the flock based on these rules and environmental influences.


Ant foraging behavior

Like in the case of bird murmuration, ant foraging behavior is another fascinating example of action that can be explained based on current dispositional propensities. This refers to ant's innate tendencies and responses when searching for food. For instance, ants uses chemical trails left by other ants to locate food sources. When an ant discovers a rich food source, it leaves a trail of pheromones, indicating a positive outcome. Other ants in the colony, following their dispositional propensity, sense these chemical cues and follow the trail towards the food. The strength of the trail and the number of ants that join in the foraging process depend on factors such as the quality and abundance of the food source. Similarly, if a food source becomes depleted, the trail weakens, and ants adjust their foraging behavior accordingly, displaying their dispositional propensity to explore new areas in search of fresh resources in such situations. This remarkable behavior showcases how ants, driven by their inherent tendencies, employ identifiable dispositional propensities to optimize their foraging strategies and ensure the survival and prosperity of their colonies. Here also we cannot know the specific contextual dynamics and the causal structures before hand, because even common factors like rain, wind, heat or a predator can disturb the dynamics.


                                                    Dispositional propensity in Entrepreneurship

Let's now bring all these examples together to shed light on the dispositional propensity manifesting in a startup context, ultimately targeting the dispositionality of entrepreneurship. As we explored various examples, we discovered that in complex systems like startups, causal structures cannot be known in advance. Instead, judgments are made based on the current dispositional propensities of the system, in relation to the disposition of the state-space. This holds true for entrepreneurs, the startups itself, the CEO's and also for agents like partners, vendors, employees, investors.

Consider the startup investor who faces a myriad of opportunities. They cannot foresee the causal structures that will determine success. Rather, they evaluate the startup's current dispositional propensity, which encompasses product-related propensities. These include factors like product-market fit, scalability, the potential for network effects, as well as legal and strategic advantages. Additionally, they take into account person-related dispositional propensities, such as the entrepreneur's access to resources, social skills, leadership abilities, and ethical character. The investor also considers environment-related propensities, evaluating the entrepreneurship-friendly nature of the ecosystem and the presence of a growing industry and market.

These elements collectively shape the current dispositional propensity of the startup, and the investor's judgment is based on understanding these propensities. However, it's important to note that no matter how successful individuals may be, they cannot know the causal structures in advance. They can only grasp the current dispositional propensities within the complex system of entrepreneurship.

In the case of successful entrepreneurs and investors, the advantage lies in the distributed nature of their operations. They have a network of individuals working for them, each tracking and pursuing dispositional propensities in different areas. For instance, someone working for a renowned investor like Paul Graham might analyze the dispositional propensities in the Chinese education market, the Indian education market, or the Indian artificial intelligence market. This accumulated advantage leads to preferential attachment, as people want to connect with successful individuals and benefit from their expertise. This advantage does not solely rely on cognitive superiority but on the ability to track and act upon dispositional propensities. The causal structures remain elusive to all.

In conclusion, the essence of understanding dispositional propensity in a complex system like entrepreneurship lies in acknowledging that we can only understand the current dispositional propensities at play. The causal structures remain unknown, and success stems from the ability to navigate and leverage these propensities effectively.



A self-organization inspired model.

The dispositional propensity of a system is indeed constantly evolving and dynamic. The current dispositional propensity serves as the foundation for the emergence of a new and evolving dispositional propensities. Evolutionary dynamics play a significant role in shaping these changes, where certain traits or characteristics might become more prevalent over time. Additionally, in domains like entrepreneurship the dynamical property of emergence plays a crucial role in shaping the system. Emergent properties refer to characteristics that arise from the interactions and relationships among the components of the system, rather than being inherent in individual parts. These properties thus contribute to the evolving dispositional reality of the system. As the system undergoes self-organization, the emergent and evolving reality will become the current dispositional reality. This ongoing process of self-organization reflects the continuous evolution and adaptation of systems, as they respond to internal and external factors.

According to Dave Snowden(2017a), in complex adaptive systems, “at a system level, we have no linear material cause but instead we have a dispositional state, a set of possibilities and plausibilities in which a future state cannot be predicted.” This is particularly important because, in a complex system, phase space disposition, is what decides on the evolutionary potential of the system, not any specific fixed goal. If a system is complex, “you can’t set outcome targets a priori, but you can define a vector target (direction and speed of change from the present against intensity of effort). You can’t manage to a desired future state but have to manage the evolutionary potential of the situated present. You can’t predict the future, but you can increase resilience in there the here and now which will allow you to manage that uncertainty” (Snowden, 2017b).


Adjacent possible

Now that we understood the nature of dispositional propensities and nature of dynamics that is dependent on the evolutionary potential of the present, it helps us to understand the micro manifestation of the dynamics by using the concept of adjacent possible(Kauffman, 1996), i.e. a kind of zone of proximal development (Vygotsky, 1978), towards which change and evolution are more likely because of the current disposition of the system. The concept of “adjacent possible” was introduced by Stuart Kauffman (1996; 2000) in evolutionary biology and complex adaptive systems to explain how biological evolution can be seen as exploration and actualization of what is adjacent possible. It can be defined as “the set of possibilities available to individuals, communities, institutions, organisms, productive processes, etc., at a given point in time during their evolution” (Loreto 2015, p. 9). It expands our comprehension of how systems can explore and manifest new trajectories based on their existing disposition and interactions. The concept of the “adjacent possible” is useful for understanding how entrepreneurial adjacent possibilities emerge, and how the new adjacent possible will lead to yet newer adjacent possibilities.



With that understanding, a simplified articulation self-organization dynamics include;

1) The system that has a dispositional state characterized by a multitude of possibilities and plausibilities, making it impossible to predict a specific future state. This state takes into account of the present state of dynamics of self-organizing systems, which is inclusive of features like increase in order, pattern formation, adaptability, robustness and resilience w.r.t. changes, emergence, non-linearity, feedback loops, criticality, fluctuations, attractor states, phase transitions, co-evolution, degeneracy, coherence, heterarchy, spontaneous order, synergy, etc. These factors collectively contribute to the overall dispositional tendencies of the system.

2) Current dispositional propensity(Disposition 1): The dispositional propensity of a system is dynamic and constantly evolving. Current dispositional propensities refer to the existing state and tendencies of a system at a given moment. They represent the foundation and starting point for the system's behavior.

3) Emergent and evolving dispositional propensity(Disposition 2): On the other hand, emergent future dispositional propensities are the new set of possibilities and tendencies that arise as the system dynamically evolves and adapts. They reflect the changes and advancements from the current disposition to a future state, driving the system's trajectory and potential for further evolution. This emergent dispositional will become the future current dispositional propensities and because of emergence, unexpected things can happen.

4) Evolutionary potential: Rather than striving for a desired future state, the focus shifts to managing the evolutionary potential inherent in the present context. It emphasizes the need to work with the inherent disposition and evolutionary potential of the system rather than imposing fixed goals or attempting to predict the future.

5) Adjacent possible: Adjacent possible can be seen as an important part of the micro dynamics of the evolutive process of the current dispositional propensities. It is the set of possibilities and potential developments that are more likely accessible and attainable from the current disposition of a system. The idea of adjacent possible helps us understand how systems, including entrepreneurs, can explore and actualize new possibilities.


Conclusion:

In conclusion, the above model of self-organization highlights how local interactions and distributed internal structures give rise to global patterns and dispositional propensities without top-down control. However, it is important to acknowledge that this is not a comprehensive design. Instead, it can be categorized as a "satisficing" design. Satisfying is a concept developed by by Herbert Simon where systems seek satisfactory solutions rather than engaging in a maximizing search or design.

Secondly, this model is an attempt to articulate a simplified understanding of the dynamic and evolving nature of dispositional propensities. The concept of self-organization emphasizes that systems adapt and evolve through continuous interactions and feedback loops, resulting in the emergence of new tendencies and dispositional propensities. This helps us in acknowledging the dispositional nature of causality in complex systems like entrepreneurship and allows us to move away from rigid goal-driven approaches and embrace the inherent uncertainties and possibilities that arise from the system's dispositional propensities.

Finally, the concept of adjacent possible elucidates the micro dynamics of system evolution, emphasizing the possibilities and developments that are more likely accessible from the current disposition of a system.  

Effectual self-organization as a primary constraint was built as a result of exploring 3 important things things simultaneously. They are; the Constraints-Led Approach(CLA) to sports coaching, Self-organization theory, and thirdly the Effectuation theory

First of all, because I was very much interested in learning science, education, etc., I got exposed to the constraints-led approach(CLA) to sports coaching, which fundamentally relies on self-organization as its core foundation. I also discovered that applied complexity models that use the idea of self-organization are getting a lot of attention from the scholarly community, and because this approach is based on natural science, I thought that there is a possibility of developing something for the domain of entrepreneurship based on this thinking, and that was the beginning of my effort.

Secondly, because of the inspiration i got from the constraints-led approach(CLA), I started exploring the nature of self-organization because I realized that self-organization is a ubiquitous feature in open complex adaptive systems. I also got exposed to approaches like guided self-organization, synergetics, etc., which are matured applied self-organization approaches.

Thirdly, I was exploring effectuation theory and i find it extremely intuitive and impressive. This approach by Saraswathy felt to me like a fresh air. However, due to my background and interest in learning science and education, as well as my passion for expertise and skill acquisition, I began to explore the theory more critically. This led me to identify several theoretical issues(Check Part 1, Chapter 4 for more details) that made me skeptical of some of its key assumptions.

One of the issues I found is that expertise in ill-structured complex domains like entrepreneurship may not align with the traditional understanding of expertise. While expertise is attainable in structured domains with regular patterns, entrepreneurship as an ill-structured complexity domain, lacking stability in goals, environment, and structure. Genuine expertise requires an environment that provides valid cues and ample opportunities to learn, which contrasts with the unpredictable and emergent nature of entrepreneurship. Furthermore, studies on deliberate practice, often emphasized in effectuation theory, show limited effectiveness in complex unstructured domains. Another concern is that expertise in complex social domains like entrepreneurship is distributed across various individuals, institutions, and artifacts. Additionally, complex domains like entrepreneurship are subject to various complexity laws(power laws, Mathew effects, etc), making success in a venture or multiple ventures a poor metric of expertise.

Furthermore, viewing entrepreneurial achievement solely through the lens of expertise may have unintended consequences. It could validate the hubris among successful entrepreneurs while leading aspiring entrepreneurs to believe that deliberate practice of some kind is necessary for success, when in reality, success in such domains can be influenced by accidental events and complexity effects. Lastly, the idea of entrepreneurial expertise raises questions about the desirability of starting multiple ventures compared to focusing on a single successful venture. Starting multiple ventures may not necessarily indicate expertise, and in highly complex domains, it might even be undesirable. Chess, for example, may lend itself to multiple successes as a sign of expertise, but this may not apply to important questions of life like family, marriage, entrepreneurship, etc.

Despite these theoretical issues, a quote attributed to Herbert Simon by Saraswathy caught my attention during my exploration of effectuation theory. i.e. According to him (Saraswathy, 2008), Near Decomposibility is an astonishingly ubiquitous principle in the architecture of rapidly evolving complex systems, and effectuation appears to be a preferred decision model with entrepreneurs who have created high-growth firms, we should be able to link Near Decomposibility to the processes these entrepreneurs use to create and grow enduring firms–whether in an experimental situation or in the real world (Saraswathy, 2008, p.163). The quote hinted at a potential deeper foundation underlying the intuitive sense of effectuation principles, suggesting a connection to the dynamics of complex self-organizing systems. Despite the presence of theoretical issues, this insight provided me a possible explanation for the intuitive appeal of effectuation.


Complexity, Entrepreneurship and Expertise

After exploring these foundations I realized that there is a serious problem in the domain of entrepreneurship, that is, it is an unstructured complexity domain with high levels of emergence and evolutionary dynamics. Because of this I cannot directly apply the constraints-led approach, as applied to sports, to the domain of entrepreneurship. In the case of sports, there generally exist structural constraints that limits infinite possibilities. For example, a football ground limits the geographical boundary. No matter what level of complexity happens, all kinds of dynamics will happen within the boundary of that football ground. There are also rules and regulations that further constrain the behavior of the players. However, this is not the case when it comes to the domain of entrepreneurship. The domain of Entrepreneurship lacks clear boundary conditions in most cases, like a running track or football ground in sports. Further, entrepreneurship lacks goal stability; goals also evolve and co-evolve. There is no state space stability. Today I might be operating in a local market, tomorrow I might be operating at a national level or shifted the entire operation to a foreign market. Furthermore, since entrepreneurship is a social system, technology-enabled global access to affordances is a possibility, e.g. I can hire an oversees employee and also sell goods without the constraints of physical boundaries. This means that the adjacent possible in social domains like entrepreneurship is not geographically bound but has a global scale. Knowledge and perception asymmetry regarding the available affordances in the adjacent possible is a key issue in the case of the domain of entrepreneurship.

Scholars have extensively studied the issue of unstructured domains, employing various concepts to explore this phenomena. E.g. low validity domain by Kahneman and Klein(2009), Left-side- & Right-side domains by Shanteau, (1992), ill-structured by Spiro(1988), domains of complex indeterminate causation by Hoffman, Klein, & Miller, (2011), Kind and wicked domains by Hogarth(2015), etc.

Further, since entrepreneurship is a highly dynamic and unstructured domain with high levels of emergence, the development of expertise is challenging or impossible in entrepreneurial core activities. Traditional understanding of expertise (Ericsson et al, 1993) cannot be applied to unstructured complexity domains like entrepreneurship (Macnamara et al, 2014; Baron, 2009; Baron and Henry, 2010; Frankish et al, 2013).

Watch Videos(Anders Ericsson ; Daniel Kahneman)


Autonomy problem

Autonomy refers to the absence of external control. A system needs to organize without interference from the outside in-order to be self-organizing. Further, the lack of external control and autonomy does not mean that such a system can have no input at all. Input is still possible as long as the inputs are no control instructions from outside the system. The decision on what to do next should be made completely inside the system, i.e. the system should be autonomous. Since it is important to maintain autonomy to facilitate self-organization, without the maintenance of autonomy, self-organization will not happen.

The problem is that, because of extreme levels of complexity and uncertainty in the domain of entrepreneurship, external agents, people, institutions and artifacts will try to affect our autonomy, resulting in a kind of agency hijack. According to Gilbert et al(2015), self-organizing systems in social domains pose key challenges. There are asymmetric power relations between and within levels. Humans are intentional actors and can have designs on controlling or influencing a system through individual or institutional action, for example, by the implementation of public policy.

This is further evident from the fact that social domains like entrepreneurship are full of decontextualized advice, one-size-fits-all models, rigid prescriptions, etc. Starting from the local level, people will start to influence our judgment based on their own anecdotal theories and cliché advice. A great example might be the prescriptive models that are prevalent in the domain of entrepreneurship like the Lean Startup. These models are in-effect attempts to hijack the autonomy (and/or agency) of the entrepreneur. Ecologically grounded self-organization cannot happen under conditions of autonomy hijack. Thus, If we try to follow their advice or use prescriptive models like lean-startup that may directly go against the self-organization principle. On the other hand, if we try to shut down ideas and information coming from the environment, that will go against our meta-disposition like diversity, exploration, and enskillment, etc. Most of these ideas may be good and useful in another context. These ideas will enrich our understanding of the taskscape.


Solution for the autonomy problem

Autonomy and agency hijacking is a serious issue. As we observed, self-organization cannot happen without autonomy, and autonomy in human social systems is closely connected to agency. As I was thinking and reflecting on the issue of autonomy, I thought about designing a primary constraint(heuristics) that removes or reduce the possibility of external agents from taking over the agency of the entrepreneur.

This idea was primarily influenced by scholarly works on fast and frugal heuristics by scholars like Gerd Gigerenzer(1999), as well as the application of heuristics as enabling constraints in the field of applied complexity, exemplified by the works of Dave Snowden(2015). Heuristics are indispensable for good decisions under uncertainty. They are not the product of a flawed mental system.(Gigerenzer, 2020). They are simple decision-making rules that are efficient and effective in situations where time, resources, or cognitive capacity are limited. They rely on specific cues or rules of thumb to simplify complex problems and yield reasonably good decisions. The idea behind fast and frugal heuristics is that humans generally speaking rely on simplified strategies rather than exhaustive analysis when making decisions. For instance, medical practitioners might use fast an frugal heuristics like "Chest Pain? Think Heart Attack," which prompts them to consider urgent tests or interventions when a patient presents with chest pain. Similarly, "Start with the Simplest Explanation" encourages considering common causes before diving into complex investigations. "Follow Evidence-Based Guidelines" provides practitioners with a heuristic framework based on established recommendations for decision-making. The heuristic "Consider the Patient's Medical History", emphasizes using previous medical information in current decision-making. "Engage in Shared Decision-Making" involves patients in the process, considering their preferences.

In the domain of entrepreneurship, individuals can also employ heuristics to make effective decisions. For example, one heuristic is "Jobs-to-be-Done (JTBD)," which involves understanding the underlying needs and motivations of customers to develop innovative solutions. Another heuristic is "Product-Market Fit," which focuses on aligning the product or service with the specific demands and preferences of the target market. Additionally, entrepreneurs can use the heuristic "Do Things That Don't Scale," which emphasizes the importance of finding initial success and validating ideas on a small scale before scaling up operations. Another heuristic is "Minimum Viable Product (MVP)," where entrepreneurs create a basic version of their product to test and gather feedback from early adopters. Lastly, the "MOM Test" heuristic encourages entrepreneurs to evaluate their business ideas from the perspective of their target customers'. This include a set of simple rules for asking good questions that even your mom can't lie to you about.

The application of heuristics is natural to human life. It is a necessary part of decision-making under uncertainty, which is the nature of human life. They offer simple yet effective decision-making rules that accommodate limited time, resources, and cognitive capacity. By relying on specific cues and rules of thumb, it simplify complex problems and lead to reasonably good decisions.

While the general nature of heuristics that we discussed till now is extremely context specific, my agenda is to think about the possibility of having a general heuristics that can be applied to all complex self-organizing systems, that enables the system or decision maker to adhere to the dynamics of self-organization, safeguarding it against the influence and hijacking attempts of external agents, institutions and artifacts.

Effectuation and Near-Decomposiblity

The effectuation theory by Saraswathy further triggered me to think about the possibility of designing such a simple flexible heuristics or primary enabling constraints to reduce the potential of autonomy hijacking in social complexity. This was the direct result of encountering the quote that was attributed to Herbert Simon that i was discussing earlier. According to him Herbert Simon, Near Decomposibility is an astonishingly ubiquitous principle in the architecture of rapidly evolving complex systems, and effectuation appears to be a preferred decision model with entrepreneurs who have created high-growth firms, we should be able to link Near Decomposibility to the processes these entrepreneurs use to create and grow enduring firms(Saraswathy, 2008, p.163).

If near-decomposibility is truly a ubiquitous principle, it logically follows that entrepreneurship can be classified as a near decomposable complex system. This, in turn, suggests that the heuristics and principles of effectuation might have some relation to the universal nature of various near-decomposable systems. This lead me to think about the possibility of reverse engineering the self-organization dynamics that I discussed earlier do develop a list of simple heuristics or primary enabling constraints.


Intermediary heuristics or primary constraints

As we observed previously, in a complex self-organizing system, the system has a dispositional state characterized by a multitude of possibilities and plausibilities, making it impossible to predict a specific future state. These factors can be collectively called the dispositional propensities of the system.

We have further observed that the dynamics of current dispositional propensities can be seen as the manifestation of the evolutionary potential of the present, and the Adjacent possible can be seen as an important part of the micro dynamics of the evolutive process of the current dispositional propensities. It refers to the set of possibilities and potential developments that are more likely accessible and attainable from the current disposition of a system at a given point in time during its evolution.

So, this idea can be represented as the following;

Current dispositional propensity(Disposition 1)
                1.Evolutionary potential
                       a. Adjacent possible


The next key point we discussed was the Emergent and evolving dispositional propensity(Disposition 2). The emergent future dispositional propensities are the new set of possibilities and tendencies that arise as the system dynamically evolves and adapts. This emergent disposition will become the current dispositional propensities of the future. The key point to understand here is that because of higher levels of emergence, unexpected things can happen at every levels. The evolutionary potential of the present, and the micro dynamics in Adjacent possible will drive the next Emergent and evolving dispositional propensity from here (Disposition 3, 4, 5, etc..).

This can be represented as the following;

Current dispositional propensity(Disposition 1)
                   1.Evolutionary potential
                         a. Adjacent possible

Emergent and evolving dispositional propensity(Disposition 2)
                  1.Evolutionary potential
                         a. Adjacent possible



As we have previously discussed, these keystone dynamical features, despite their simplicity, offer an explanation for self-organizational behavior. This understanding has been further demonstrated through various examples, including parental intervention (toddler climbing a chair), the gaze heuristic and the ball, the propensity of lions, the dispositional propensity of Cristiano Ronaldo scoring a goal, bird murmuration, ant foraging behavior, and most importantly, in the context of entrepreneurship. Because of this reason, I developed the primary constraints or simple flexible heuristics based on these dynamical features.

Thus, dispositionality or dispositional propensity becomes the heuristic, "Understand your current dispositional propensity." The adjacent possible becomes the heuristic, "Act in the adjacent possible." Evolutionary potential of the present becomes the heuristic, "Make use of the coevolutionary potential of the present," and the fourth one, emergent and evolving reality will become the heuristic, "Accept and leverage the emergent and evolving reality." Don't cry about it; whatever is the current reality, that's the only place you can take action.


List of heuristics or primary constraints (Check Image 5.1)


Principle: You are already a self-organizing system.

Meta Heuristics or Prime Primary constraint: So Act like self-organizing systems

Primary constraints

1. Understand your current dispositional propensity(Disposition 1)
2. Act in the adjacent possible
3. Make use of the co-evolutionary potential of the present
4. Accept and leverage the emergent and evolving dispositional propensity (Disposition 2).
5. The propensity of emergent and evolving dispositional reality will become current dispositional propensity i.e. Disposition 1.


Explanation


Principle: You are already a self-organizing system.

This principle suggest that we are already a self-organizing system. According to complexity scholar Dirk Helbing(2014), "What makes complex systems so difficult to control is that they have a natural tendency to self-organize, driven by the inherent forces between the system components". It is very important to mention that the following constraints are not conceived as a rigid prescriptions, but primarily serves as a reminder that you are a complex adaptive system with self-organizing tendencies.


Act like self-organizing systems(Meta Heuristics or Prime Primary constraint)

This is the prime primary constraint that follows the principle i.e. You are already a self-organizing system and you have an inherent tendency to self-organize under various constraints. This heuristics suggest that, You must embrace your inherent nature by acting like self-organizing system, instead of being subjected to be hijacked by an external agent, to act like a robot.

This prime primary constraint can be considered the enaction of what can be called a mindful application of self-organization principles observed in nature. Here, the entrepreneur doesn't employ any top-down approaches prescribed to them, but uses the context as the central constraint from which action emerges. This heuristics also involves a continues quest to know about the nature of acting like self-organizing systems, so that the actor/entrepreneur can initiate adaptations to the primary constraints, if required.

Primary constraints

Self-organization is often called the invisible hand of nature and economy. A kind of supremely intelligent natural process that is ubiquitous in the universe. Entrepreneurs as the first principle or primary constraint act like self-organizing systems using the following constraints.


1. Understand your current dispositional propensity(Disposition 1)

This corresponds to the idea of acting based on the current dispositional propensity of the system. You start by sensemaking about your current dispositional propensity in relation to the disposition of the state space. Self-organizing entrepreneurs thus start taking action by knowing their disposition.

2. Act in the adjacent possible

Adjacent possible can be seen as an important part of the micro dynamics of the evolutive process of the current dispositional propensities. It is the set of possibilities and potential developments that are more likely accessible and attainable from the current disposition of a system. Self-organizing entrepreneurs focus on what they can do within the adjacent possible space, acting without excessive concern for determining the optimal course of action.

3. Make use of the co-evolutionary potential of the present

Self-organizing entrepreneurs make use of the co-evolutionary potential of other systems, agents, and things. This is not conceived as an imposing top-down order, but as a distributed co-evolutionary and co-adaptive process that makes use of the inherent intelligence in complex adaptive systems. The entrepreneur influences the evolution of others and they are in turn influenced by others. It encompasses a broader scope that extends to all systems, encompassing the realms of human, social, cognitive, and material domains. This includes the dynamic interplay and evolution of ideas, artifacts, tools, and various elements within these systems.

For the enactment of co-evolution at all these levels, I use American pragmatist John Dewey's instrumentalism, which I term Co-evolutionary Instrumentalism or Deweyan Co-evolutionary Instrumentalism (Johny, 2022). Dewey distinguished his philosophy from earlier philosophical pragmatists by calling it instrumentalism, which indicated that knowledge, things, artifacts, tools, language, cognition, etc. are instruments that we use to act in the world. His instrumentalism was primarily inspired by evolutionary theory. According to Dewey, “The entire significance of the evolutionary method in biology and social history is that every distinct organ, structure, or formation, every grouping of cells or elements, has to be treated as an instrument of adjustment or adaptation to a particular environing situation. Its meaning, its character, its value, is known when, and only when, it is considered as an arrangement for meeting the conditions involved in some specific situation” Dewey(1903. p.15).

Deweyan instrumentalism is Co-evolutionary Instrumentalism which is bi-directional(or multidirectional). Stuart Kauffman once observed that "all ‘evolution’ is really coevolution"(1993, 237). There is much evidence to suggest that John Dewey conceived an almost similar idea with his application of instrumentalism. It was not proposed as a top-down authoritarian and imposing model of pragmatism. Instead, it is an active, plural, historical, and participatory way of living and acting in the world that contradicts the spectator theory of knowledge. His instrumentalism is inherently based on democratic ideals. This is why I prefer to view this instrumentalism as co-evolutionary and co-adaptive instrumentalism stressing the lack of imposing top-down order (Johny, 2022).

Instrumentalism removes the conflict between instruments. Like a carpenter using his toolbox, all instruments are considered useful and complementary. Using one doesn't mean you have to sacrifice another. The emergent context decides what instrument works and how much it works. This makes models like Business Planning (Sahlman, 1997, ), Contingency planning (Honig, 2004,), Discovery-driven planning (McGrath et al, 1995), Probe-and-Learn approach (Lynn et al., 1996), Lean start-up approach (Blank, 2013; Ries, 2011), Disciplined Entrepreneurship (Sull, 2004), Theory Based View(Felin et al, 2020), Design thinking(Stanford/Diamond model), Design Cognition (Garbuio et al., 2017), Effectuation (Sarasvathy, 2001), Bricolage (Baker, T., & Nelson, 2005), User Entrepreneurship (Shah, S. K., 2007), etc. compatible with the framework. This also works the same in the context of Heuristics/Simple rules. For example, Adhocism, Bricolage, Convergent/Divergent, Distributed, Do Things that Don't Scale, Effectuation, Experimentation, Improvisation, Jobs to be done, Product market fit, 100 true fans, 80/20, etc, will get equality of opportunities.

Deweyan instrumentalism here is conceived as exaptive instrumentalism. Exaptation refers to (biological/social) characters evolved for other usages and later ‘coopted’ for their current role (Gould and Vrba; 1982). This means that a tool or instrument that was explicitly designed for a unique purpose in the past can be used for a radically new purpose. Since every context is unique and emergent, every use of an instrument is actually exaptive. This manifests in the mutual evolution of artefacts and their uses for specific purposes in specific environments. This is an idea discussed by the name of "Instrumental Genesis" by many scholars in design scholarship (Carvalho et.al, 2019; Lonchamp 2012; Rabardel and Bourmaud 2003).


4. Accept and leverage the emergent and evolving dispositional propensity (Disposition 2).


This refers to accepting and leveraging change or dynamical nature of the dispositional propensity that includes evolutionary dynamics and also emergence, where unexpected properties might manifest. Complex systems like entrepreneurship show emergent property. It is a process through which properties and or structures come into being that are unexpected, given the known attributes of component agents and environmental forces (Lichtenstein and McKelvey, 2011).

Self-organizing entrepreneur accepts and leverages emergent property, unexpected results, and contingencies that form part of the dynamic and evolving nature of current dispositional propensity. They should not cry about the current reality; whatever is the current dispositional propensity, that's the only place you can take action. So there is no point in regretting or worrying about an alternate universe. They view it as a reality to be embraced. This is about viewing the entrepreneurial process as a continuous one, but always being grounded in the current emergent disposition.


5. The emergent and evolving dispositional reality will become current dispositional reality i.e. Disposition 1.


Repeat.

Understand your current disposition
Act in the adjacent possible.
Make use of the co-evolutionary potential of the present.
Leverage and make use of emergent/evolving reality.



Conclusion

The current disposition is the only reality that you have access to act on. All of the four above-discussed dynamical aspects are happening simultaneously. None of them are separate from the other. They are part of an indivisible, inseparable process. As Anjum and Mumford(2018) point out, "In no real sense does a process have parts at all; for they can be formed only through the abstraction of a viewer who considers the process. The process is, in reality, an indivisible unity."  

In the last chapter I have introduced a list of primary constraints or heuristics for entrepreneurs to act like self-organizing systems. I call these primary constraints as Effectual Self-organization, which was inspired from Effectuation theory of Saras Saraswathy. In this chapter I intent to clarify the logic behind that inspiration.

It must be noted in the outset itself that this chapter is intended not as a necessary part of my thesis, but must be considered as a complimentary piece about an unproven hypothesis about effectuation being connected to self-organization.

My claim here is that most of the effectuation principles might correspond to the dynamics of self-organizing complex system. As discussed earlier, this intuition was triggered by Herbert Simon. According to him (Saraswathy, 2008), Near Decomposibility is an astonishingly ubiquitous principle in the architecture of rapidly evolving complex systems, and effectuation appears to be a preferred decision model with entrepreneurs who have created high-growth firms, we should be able to link Near Decomposibility to the processes these entrepreneurs use to create and grow enduring firms–whether in an experimental situation or in the real world (Saraswathy, 2008, p.163). This points to the fact that effectuation principles might have fundamental complexity science based explanation of entrepreneurial behaviour. This is despite the fact that Saraswathy used expertise theory to build the theory of effectuation. I hypothesize that effectuation theory if developed as a self-organization heuristics might have generalizable qualities applicable in other domains.


Theory of Effectuation

The theory of effectuation suggests that under conditions of uncertainty, entrepreneurs adopt a decision logic that is different from that explained by a traditional, more rational model of entrepreneurship called “causation”. It is described by Sarasvathy (2008) as “a logic of entrepreneurial expertise, a dynamic and interactive process of creating new artifacts in the world" (Sarasvathy and Dew, 2005; Sarasvathy, 2008). According to her, causal logic is based on the premise that; ‘To the extent we can predict the future, we can control it.’ An effectual logic is based on the premise that; ‘To the extent we can control the future, we do not need to predict it’ (Saraswathy, 2001).

Effectuation dictates that; In highly uncertain and dynamic environments, target customers can only be defined ex-post through whoever buys a product or service. Goals change, are shaped and constructed over time and are sometimes formed by chance. Instead of focusing on goals, the entrepreneur exerts control over the available set of means—the things over which the entrepreneur has control (Fisher, 2012).

Saraswathy developed the theory of effectuation from her research on so called expert entrepreneurs. In that, she claim to have found the following process elements of entrepreneurial expertise (Saraswathy, 2008. p.15). According to her; Expert entrepreneurs begin with who they are, what they know, and whom they know, and immediately start taking action and interacting with other people; They focus on what they can do and do it, without worrying much about what they ought to do; Some of the people they interact with self-select into the process by making commitments to the venture; Each commitment results in new means and new goals for the venture; As resources accumulate in the growing network, constraints begin to accrete. The constraints reduce possible changes in future goals and restrict who may or may not be admitted into the stakeholder network; Assuming the stakeholder accumulation process does not prematurely abort, goals and networks concurrently converge into a new market and a new firm.

She further adds that, at each step of the process, expert entrepreneurs use the following principles (Saraswathy, 2008, p.15).

1. The bird-in-hand principle: This is a principle of means-driven (as opposed to goal-driven) action. The emphasis here is on creating something new with existing means rather than discovering new ways to achieve given goals.

2. The affordable-loss principle: This principle prescribes committing in advance to what one is willing to lose rather than investing in calculations about expected returns to the project.

3. The crazy-quilt principle: This principle involves negotiating with any and all stakeholders who are willing to make actual commitments to the project, without worrying about opportunity costs, or carrying out elaborate competitive analyses. Furthermore, who comes on board determines the goals of the enterprise. Not vice versa.

4. The lemonade principle: This principle suggests acknowledging and appropriating contingency by leveraging surprises rather than trying to avoid them, overcome them, or adapt to them.

5. The pilot-in-the-plane principle: This principle urges relying on and working with the human agency as the prime driver of opportunity rather than limiting entrepreneurial efforts to exploiting exogenous factors such as technological trajectories and socioeconomic trends.


Effectuation and Self-organization

In the previous chapter, I explored five key features commonly discussed by scholars that characterize self-organizing systems: Increase in Order, Pattern Formation, Autonomy, Robustness Resilience and Adaptability, and Dynamics. By evaluating Saraswathy's effectuation theory in relation to these features, we can assess its compatibility with the fundamental qualities of self-organization as recognized in scholarly research. With that in mind let me demonstrate that there might in-fact exist valid foundation for my hypothesis by examining each of these features in relation to Saraswathy's effectuation theory. Let's see;

Increase in order: An increase in order characterizes self-organizing systems as they spontaneously enhance their complexity and structure over time. This order arises from the interactions and feedback among system components. We can see venture creation process via effectuation by default as the dynamics towards Increase in order.

Pattern formation: Pattern formation closely aligns with increase in order, as self-organizing systems exhibit the ability to generate patterns and structures across various scales. Once an effectual entrepreneur self-organize to develop a venture, it can be objectively observable as a pattern. Various patterns emerge solely as the result of the interactions among the entrepreneur, stakeholders, institutions, artifacts, etc. More importantly, this pattern is not imposed by a central authority or by those external to those components, but rather arises from the collective behavior of the elements themselves.

Autonomy principle: When we look at the autonomy principle it is evident that a key tenet of effectuation is the absence of external control, granting entrepreneurs a significant level of autonomy in their decision-making processes.

Robustness, resilience, and adaptability: The next feature identified by scholars is the robustness, resilience, and adaptability of self-organizing systems. Self-organizing systems can maintain their functionality and stability even in the face of internal or external disturbances, failures, or changes. The diversity and redundancy of components, along with the distributed nature of control and regulation, contribute to the system's robustness and resilience. I argue that all of the principles of effectuation might embody features of robustness, resilience, and adaptability. The bird-in-hand allows the entrepreneur to understand his dispositional propensities than worrying about unachievable things or past failures. Effectual entrepreneurs begin by trying to understand who they are, what they know, and whom they know, and immediately start taking action and interacting with other people; This is not a one time deal but an ongoing process that warrants incredible adaptive behaviour. This itself might have the demonstrable manifestation of robustness, resilience, and adaptability. Further, the lemonade principle points to the idea of embracing the emergent realities whether it is good or bad. It can be understood with the famous expression in mind, "when life gives you lemons make lemonade". This might also involve elements of robustness, resilience, and adaptability. Finally, the crazy-quilt principle which involves negotiating with any and all stakeholders who are willing to make actual commitments to the project. Who comes on board determines the goals of the enterprise, suggesting the co-evolutionary and co-adaptive nature of self-organizing systems that might be contributing to robustness, resilience, and adaptability of the system.

Dynamics: When it comes to dynamics or far from equilibrium dynamics, we can say that this is one of the most obvious aspect of effectuation. It is all about taking action and continuously be in dynamics. Further, according to De Wolf and Holvoet(2004), an essential property of self-organisation is that it is a process. A process perspective points to the fact that the system is not static and in continues dynamics. We have observed that effectuation principles are also based on process elements proposed by Saras Saraswathy, thus by default based on the dynamic paradigm .

Apart from these key features identified by scholars, we can also find several other kind of similarities between self-organization dynamics and effectual dynamics. For example;

(1) Interaction between the system and its environment:
Self-organizing systems do not emerge from a predetermined design but rather arise from the interaction between the system and its environment. Similarly, effectuation rejects the notion of top-down control or external delegation. It focuses on the interaction between the entrepreneur and the environment, utilizing the means available to create new effects. Effectuation operates in the inverse of causation, where the effect to be created emerges from the interaction with the environment, rather than being predetermined.

(2) Emergent behavior from local interactions:
Self-organization is an emergent property of a system as a whole, arising from the collective interactions of its individual components. The system's macroscopic behavior emerges from the microscopic interactions, which themselves contain limited information. I hypothesize that Effectuation recognizes the emergent nature of complex systems and implicitly incorporates it into its principles. The lemonade principle within effectuation could be seen as a heuristics that addresses how to respond to surprises generated by emergent properties, embracing and leveraging them as opportunities.

(3) Local information and general principles:
Self-organizing systems operate based on local information and general principles. I hypothesize that the same principle could be applied to understand effectuation, where the entrepreneur acts based on the means available and interacts with stakeholders, but more or less follow self-organization logic as general principles.

(4) Lack of explicit goals:
Self-organizing systems do not operate with specific goals in mind but rather adapt to survive in complex circumstances. Effectuation embraces this philosophy by being means-driven rather than goal-driven. The goals of the venture are determined by the means available and the co-evolutionary potential of the interacting agents.

In conclusion, based on the analysis presented, it can be hypothesized that effectuation theory embodies certain attributes of self-organizing systems, thereby offering potentially generalizable praxis model for human and social complex systems.
(Check Also Paul Cillier's General attributes of self-organizing systems compared with effectuation; Click This Link)


With that point in mind, let me move on to the concept of Effectual self-organization. Effectual self-organization is a set of simple and flexible heuristics or primary constraints that I have designed to enable entrepreneurs to intentionally act like self-organizing systems and counteract the forces that may impede autonomy.


Effectual Self-Organization

As I have been hinting throughout this part, effectual dynamics could be the dynamics of self-organization. Self-Organization refers to the feature of systems that appear to organize themselves without external direction or control. Self-organization has been used to describe swarms, flocks, traffic, and many other systems where the local interactions lead to a global pattern or behavior (Camazine et al., 2003; Gershenson, 2007). Intuitively, self-organization implies that a system increases its own organization. Self-organization of the effectual entrepreneur can be seen as being initiated with an examination of the means available to an entrepreneur. The questions “Who am I?”, “What do I know?”, and “Whom do I know?” allow for an examination of the means available to an entrepreneur, which allows him or her to consider what he or she can do (Sarasvathy & Dew, 2005). Through interacting with others and engaging with stakeholders, the entrepreneur discovers new means and establishes new goals that allow for revaluation of means and possible courses of action (Fisher, 2012).

With regard to effectual self-organization I hypothesize that;

Trying to figure out dispositional propensity might have some relationship effectual idea of trying to figure out the bird in hand, i.e. means available to an entrepreneur by asking questions “Who am I?”, “What do I know?”, and “Whom do I know?

Acting in the adjacent possible: The concept of “adjacent possible” was introduced by Stuart Kauffman (1996; 2000) in evolutionary biology and complex adaptive systems to explain how biological evolution can be seen as exploration and actualization of what is adjacent possible. It can be defined as “the set of possibilities available to individuals, communities, institutions, organisms, productive processes, etc., at a given point in time during their evolution” (Loreto 2015, p. 9). My hypothesis here is that, acting in the adjacent possible might have some parallel to the effectual idea of immediately start taking action and focusing on what they can do and do it, without worrying much about what they ought to do.


Acting in the adjacent possible might also have some parallel with the affordable-loss principle of effectuation This principle prescribes committing in advance to what one is willing to lose rather than investing in calculations about expected returns to the project. This could be seen an an enabling constraint that was designed it to support action in the adjacent possible space, which not risking system death. Any failure inside the zone of adjacent possible will not likely result in system disorganization, but likely help the development of system resilience.


Making use of the co-evolutionary potential might have some parallels with the crazy-quilt principle: This is about making use of the co-evolutionary potential of other systems, people, things, artifacts, agents, institutions, etc. So, whatever the current co-evolutionary or co-evolutionary potential is, we make use of that to take action. This is not conceived as an imposing top-down order but a distributed co-evolutionary and co-adaptive process that makes use of the inherent intelligence of complex adaptive systems. The entrepreneur influences the evolution of others, and they, in turn, are influenced by others.  As I already discussed earlier, co-evolution here in this framework is used in a wider perspective, inclusive of micro manifestations like co-adaptation, cooperation, co-creation, co-dynamics, etc. Co-dynamics might be the dynamics between a tool and a tool user, for example. This aspect has some equivalency with the Crazy-quilt principle, which involves negotiating with any and all stakeholders who are willing to make actual commitments to the project, without worrying about opportunity costs, or carrying out elaborate competitive analyses. I hypothesize that this can be seen as part of co-evolutionary dynamics in which all participants have a contributing role in setting the direction and co-evolving goal of the venture. It is not based on a top down order. Further, It is important to note that the co-evolutionary potential in this framework is envisioned as having a much broader scope than the one conceived by Saraswathy's effectuation theory. The "crazy quilt" principle in effectuation is thus viewed as only one among many ways to enact the co-evolutionary dynamics. While the crazy quilt in effectuation is limited to stakeholders, the co-evolutionary dynamics extend to all systems, including human, social, cognitive, and material. This includes ideas, artifacts, tools, etc.

The emergent and evolving disposition might have some parallel with the lemonade principle: This lemonade principle suggests acknowledging and appropriating contingency by leveraging surprises rather than trying to avoid them. In a similar vein, the emergent and evolving disposition encourages individuals to adapt and navigate their path based on the unfolding circumstances. Instead of rigidly adhering to preconceived plans, this approach suggests being open to the opportunities and possibilities that arise along the way.

Meta-heuristics or Prime primary constraints involves deliberately acting like self-organizing systems using all the above. They are;(1) Understand your current dispositional propensity. (2) Act in the adjacent possible. (3) Make use of the co-evolutionary potential of the present. (4) Accept and leverage the emergent and evolving dispositional propensity.This might have some parallel to the pilot-in-the-plane principle. The pilot-in-the-plane principle urges relying on and working with the human agency as the prime driver of opportunity rather than limiting entrepreneurial efforts to exploiting exogenous factors such as technological trajectories and socioeconomic trends. This can be viewed to some degree as an equivalent to elements of Lichtenstein's(2016) concept of generative emergence that views entrepreneurial emergence as intentional, and agency, even if distributed, as the source of successful organizing. To the framework, entrepreneurial intention can be considered the primary attractor around which self-organisation takes place. In order for effective self-organization to take place, the agent must have autonomy and must use agency, not to exert control that is driven by his/her own bounded rationality, or the rules of perceived local optima, but use the kind of agency that is distributed and embedded as well (Garud and Karnøe, 2005, 2003).


The emergent and evolving dispositional reality will become current dispositional reality. In a complex emergent domain like entrepreneurship, we can only know the dispositional propensity of the system(Popper, 1990; Ulanowicz,2002; Burch, 2001). This is because the system is always in non-linear dynamics and we can only know the system by knowing how it is disposed in relation to the disposition of the state space. This warrants continuous reappraisal of the situated present. As I already discussed, the emergent and evolving dispositional propensity will become the current dispositional propensity of the system. This calls for the repetition of the same process continuously, but in a non-linear manner. I.e, Repeating, Understand your current disposition, Act in the adjacent possible, Make use of the co-evolutionary potential of the present, Leverage and make use of emergent/evolving reality. I hypothesize that the effectual cycle in effectuation theory might have a parallel here. The effectual cycle suggests always looping back and cycling through five core principles(bird-in-hand, affordable-loss, crazy-quilt, lemonade, pilot-in-the-plane). More specifically there are two types of converging cycles mentioned; expanding means and converging goals. The expanding-means cycle looks for increases in resources, and the Converging goals cycle adapts the goals. "It accretes constraints on the venture that converge into specific goals that get embodied in an effectual artifact over time” (Sarasvathy et al, 2014; Sarasvathy & Dew, 2005, pp. 543–544). This is also a feedback about emergent realities that will lead to estimation of the new phase space disposition, new adjacent possible, new co-evolutionary potential, new action, etc. While this is the hypothetical similarity between these two ideas. The difference here might be that I don't see effectual self-organization primarily as a cycle; I see this as a non-linear dynamical process that feeds-forward to act based on agent level estimation of dispositional propensities. All of these four dynamical features are simultaneously happening all the time.

The key difference is that what I propose here in effectual self-organization is based on natural science and self-organization theory, while what Saraswathy proposes is based on cognitive science and also proposes it as an expertise theory. With that remark, I am concluding this part. In the next part , I will be discussing deeply about the idea of constraints itself, and i'll be discussing the fundamental nature of constraints in complex systems.

The representation of self-organization dynamics in Google's history presented here is a simplification based on known events and factors.


Disposition 1; Current dispositional propensity

Environment related propensities.
* In the second half of the 90's, internet existed and emerged as a platform for accessing information and connecting people globally.
* Search engines were there, but existing search engines were not efficient. They were relying on simplistic keyword frequency based ranking methods to provide search results.
* The Rankdex algorithm developed by Robin Li could potentially address contemporary search challenges, but unfortunately, it was patented.

Person related dispositional propensities.
Larry Page and Sergey Brin, both specialists in computer science, were students at Stanford University.
Their paths crossed in 1995 during a campus tour when Page joined a group that Brin volunteered to guide.
* Page and Brin were involved in the Stanford Digital Library Project (SDLP), funded by the National Science Foundation, aiming to create a comprehensive digital library.
Larry Page's exploration of the mathematical properties of the World Wide Web, guided by his supervisor Terry Winograd, led him to focus on the importance of backlinks as valuable information for page relevance, akin to citations in academic publishing.

1(1.1). Coevolutionary potential of the present

* The inefficiency of conventional search engines points to a potential coevolutionary need in the market or among users for a superior search engine.
* This coevolutionary potential aligns with the capabilities of the Rankdex algorithm developed by Robin Li.
* Coevolutionary potential of technology talent market was existent.
* Coevolutionary potential of legal talent market were existent.

Note: Coevolution and coevolutionary instrumentalism in human dynamics can encompass multiple levels, such as neuronal, cognitive, verbal, physical, artifactual, institutional, and social realms, all contributing to a complex and interconnected process. This means the above depiction is an over-simplification.


1(1.2). Act in the Adjacent possible.

* In 1996, Larry Page and Sergey Brin initiated the Backrub project to address the inefficiencies of conventional search engines.
* Collaborating with additional technology talents like Scott Hassan and Alan Steremberg, they developed the PageRank algorithm to rank web pages based on backlinks.
* While the Rankdex algorithm was patented, they creatively worked around this limitation to achieve a similar effect, potentially seeking specialized legal help in the process.



Disposition 2; Understand(reappraisal) your current dispositional propensity


* As Page and Brin delved deeper into the development of BackRub, they realized the immense potential of their algorithm. By analyzing the relationships between web pages, BackRub could determine the importance and relevance of a page more comprehensively than keyword-based methods. The algorithm grew in effectiveness as the web expanded, leveraging the increasing interconnectedness of online information.
* With this project, they proved the concept that the conventional search engines are less efficient.

2(1.1). Coevolutionary potential

*  There exist a coevolutionary potential for page rank algorithm, underlying and supporting technologies, and users that need better and relevant search engine.
* There exist Coevolutionary potential of mentorship, technology talent market, funding market.

Note: Coevolution and coevolutionary instrumentalism in human dynamics can encompass multiple levels, such as neuronal, cognitive, verbal, physical, artifactual, institutional, and social realms, all contributing to a complex and interconnected process. This means the above depiction is an over-simplification.

2(1.2) Act in the Adjacent possible.

On September 15, 1997, the domain name Google.com was registered, marking the official establishment of their venture.
* This pivotal moment laid the foundation for Google's evolution into a global leader in information access and search technology. 



Conclusion

It must be noted that all of the 4 dimensions of the effectual self-organization process are part of the same dynamics.


Disposition 1; Current dispositional propensity
                    a. Coevolutionary potential of the present
                                   a1. Act in the Adjacent possible. 
Disposition 2; Emerging and evolving dispositional propensity



And this is an ongoing process

Disposition 1; Current dispositional propensity
                  a. Coevolutionary potential of the present
                                    a1. Act in the Adjacent possible.

Disposition 2; Emerging and evolving dispositional propensity
                  a. Coevolutionary potential of the present
                                    a1. Act in the Adjacent possible.

Disposition 3; Emerging and evolving dispositional propensity 
                 a. Coevolutionary potential of the present
                                   a1. Act in the Adjacent possible.

Disposition 4; Emerging and evolving dispositional propensity 
                 a. Coevolutionary potential of the present
                                   a1. Act in the Adjacent possible.

Disposition N; Emerging and evolving dispositional propensity 

I would like to conclude this section by pointing out some key takeaways.

1. Effectual self-organization is proposed as a primary constraint (or think of it as simple flexible heuristics) for entrepreneurial self-organization. It is not conceived as a rigid prescription, but primarily as a reminder that you are a complex adaptive system with self-organizing tendencies. You are already good enough.

2. Effectual self-organization is different from effectuation theory. Effectuation is based on cognitive science and expertise theory and Effectual self-organization was inspired by self-organization process. Even though this is the case, the name effectual in the Effectual self-organization was inspired by effectuation theory.  

3. In Part 3(praxis), I will discuss the key practical details of effectual self-organization and I will demonstrate how it fits in with the entire ESO-Loop Framework. 

Part 2(lll)


Constraints: Theory and Design

Self-organization happens under various constraints. E.g Human body self-organizes with the constraints of genetics and skeleton system. This means constraints are an integral part of self-organization. This also suggests that the self-organization of an entrepreneurial system is influenced by various kinds of constraints, internal and environmental. Thus such self-organization can be positively influenced by introducing various constraints.

In this part;

1. Firstly, I will demonstrate the reasons why simple self-organization alone may not be ideal in many cases.

2. Secondly, I will give a brief overview of the science of constraints and a few existing successful models that use constraints in an applied way.

3. Thirdly, I will introduce two kinds of constraints to enable better entrepreneurial self-organization; they are Context-sensitive constraints & Context-free constraints (Juarrero, 1999)

In the previous part I have discussed effectual self-organization as an ideal praxis for entrepreneurship. Self-organization is a ubiquitous phenomena and like all self-organizing systems, effectual self-organization is also driven by local interactions. Complex adaptive systems have a tendency to self-organize, but the deliberate use of effectual self-organization as a logic makes all the difference.

While effectual self-organization is adopted as a heuristics to act in the complex entrepreneurial ecology, It is not free from various constraints. The outcome will be mostly determined by constraints by which, through which, and around which self-organization takes place. Like the idea suggested by the bible parable of "the sower of seed", only the seeds that fell on the good soil will self-organize to produce great corn. Most seeds may self-organize if provided with basic conditions., but the thriving of the seed to corn or tree depends on many constraints and conditions.

This become obviously apparent when we examine constraints like business geography and socio-economic status. Consider the contrasting scenarios faced by entrepreneurs in a war-torn African nation and those in bustling cities like San Francisco, Miami, or Bangalore. In the former, entrepreneurs contend with the aftermath of conflict, limited infrastructure, political instability, and economic challenges. In contrast, entrepreneurs in thriving urban centers benefit from established ecosystems, access to capital, advanced technology, and a supportive business environment. Similarly, socio-economic status plays a crucial role. Entrepreneurs hailing from disadvantaged backgrounds often confront additional hurdles, such as limited access to funding, networks, education, and market opportunities, compared to their counterparts from more affluent backgrounds.

Further, It is crucial to acknowledge that alongside these macro-level social constraints, there are also individual unique constraints that shape entrepreneurial experiences. These constraints may include personal circumstances, prior experiences, education, skills, and resources. For instance, an entrepreneur with a disability may encounter physical or accessibility constraints, which can impact their ability to navigate and thrive in the business landscape.

There are also other types of constraints like cognitive, artifactual and methodological. The Eso-loop framework is developed to enable sense-making of all these types of constraints. But before that we are going to attend to the design challenges that motivated the development of Eso-loop framework. Let me start with certain key challenges faced by unstructured complexity domain like entrepreneurship.

The adjacent possible in a hyper-connected and complex social world

The adjacent possible is not locally or geographically bound in the social world. Paul Selden and Denise Fletcher(2015) articulated this aspect by quoting works by people like Herbert Simon and Prigogine. They argue that complex social systems are different from biological and physical systems (Prigogine and Stengers, 1984), where hierarchies are distinguished by spatial organization,i.e. ‘relative spatial propinquity’ (Simon, 1962: 469). For example, the subsystems that constitute the complex system of the human body, are spatially organized within the body as a physical entity. In contrast, the interaction of subsystems and subsystem components in social systems is unconstrained by spatial proximity because interaction is mediated by ‘symbolic meaning systems’ (Simon, 1962: 469–470)". I prefer to call ‘symbolic meaning systems’ as a kind of socially available affordances in order to maintain the centrality of ecological basis. Thus in social systems, the local optima may not be located in the geographical adjacency. Further, this aspect is extended and amplified by the technology-mediated connectivity revolution happening around us. This makes it very interesting to study the issue of adjacent possible in a hyper-connected and dynamic social world. In a hyper-connected world, the local-global separation is fast disappearing. Even the local-optima is globally distributed; the adjacent possible is not physical adjacent possible. This means there will always be the possibility of a perceived adjacent possible and there will be an actual adjacent possible. It is possible that the entrepreneurial agent gets stuck in the perceived adjacent possible for long periods of time.


Self-organization under bounded conditions(exposure, environment, rationality)

Research has shown that bounded rationality challenges new ventures differently than it does to established firms and that entrepreneurs appear to systematically satisfice prematurely across many decisions (Cohen et al, 2018). This satisficing (Simon, 1955) behavior in the adjacent possible is absolutely ideal, but the real problem is whether the entrepreneur is driven by attractors of the local optima or global optima, and whether the entrepreneur will be stuck in the local optima for many years before realizing the existence of an adjacent global-optima. As demonstrated by effectuation, entrepreneurs generally start their venture with a preference to ‘local search’, whereby they primarily explore opportunities that fit with their existing disposition and knowledge. This could lead to the identification of local optima rather than global optima (Keinz & Prügl, 2010; Rosenkopf & Nerkar, 2001; Stuart & Podolny, 1996). This was also confirmed by an empirical study done by Shane(2000) that has also shown that entrepreneurs tend to identify opportunities that were either already known to them in the past or are closely related to their existing stock of knowledge. This local search behavior is of inevitable importance but the problem is that focusing on existing knowledge and expertise alone can Impede the entrepreneur from exploring distant solutions or even the real adjacent global optima.

Further, environmental constraints, including lack of exposure to relevant ideas or models may prevent the travel to global optima. According to innovation historian Anton Howes, "Absent any exposure to inventors, people simply don’t become inventors. Knowing about invention as an activity is a necessary precondition to becoming an inventor yourself. The vast majority of people never innovate, for the very simple reason that it never occurs to them to do so" (Howes, 2021). His historical evidence has exposed the importance of proximity, mentoring, community, and reputation in the development of new innovators (Howes, 2017; Howes, 2017a; Howes, 2016). If a person who never have got any exposure to business models other than local brick-and-mortar retail( if he is about to start a business), he will never be able to imagine the possibilities of the internet, platform model, network effect, etc. He will most likely start a business in his local optima, i.e. retail.

Following are key points that I want to point from this discussion.

1. There is no clear local-global separation in the social world. This makes it important to have some visibility towards the dynamics of global state space. Effectual self-organization by default doesn't provide any dispositional capabilities for sense-making or exploration towards the global phase space.

2. For a design to be sustainable it must acknowledge that the end-users are fallible humans with bounded rationality. It must take into account cognitive limitations, bias, and dysfunctional dispositions. Models must anticipate the potential issues that could arise from bounded rationality and compensate for them with built-in design solutions. Effectual self-organization doesn't provide dispositional capabilities for awareness about bounded rationality or limitation.

3. Effectual self-organization model cannot by default have functional dispositions as discussed in part 2. This requires deliberate design.

 Introduction

Designers who operate in complex domains must need to constrain the design-space(state-space). For this purpose, they often impose a (top-down) primary generator (Darke, 1979) or use bottom-up opportunistic design strategies or combinations of both (Kelso et al, 2016). By doing so designers explore the ‘design territory’ in which the design problem and solution co-evolve (Dorst and Cross 2001). The approach employed by this framework is also based on balancing the contradictions(design and self-organization). I.e., I have already introduced the idea of effectual self-organization in the last part. This part is about constraints-based design for enabling better self-organization.

Self-organization and design

Self-organization depends mostly on the constraints that affect it. This warrants the question, is it possible to design for self-organization?. Is it possible to increase the likelihood of a system moving to an optimal direction than staying in a sub-optimal location, or prevent the system from moving to a dysfunctional state? According to Gershenson(2007), "designing for self-organization can be very useful in complex systems where the observer cannot a priori conceive of all possible configurations, purposes, or problems that the system may be confronted with. Examples of these are organizations (corporations, governments, communities), traffic control, distributed robotics, allocation of ecologic resources, self-assembling nanotubes, and complex software systems (Heylighen and Gershenson, 2003) such as the Internet". These examples suggest that domains like engineering are also increasingly using this ambidextrous approach where bottom-up and top-down approaches are used. In engineering, elements are designed to dynamically and autonomously solve a problem or perform a function at the system level(self-organizing). In other words, the engineer will not build a system to perform a function explicitly, but elements will be engineered in such a way that their behavior and interactions will lead to the system function. Thus, for e.g., a car is not a self-organizing system because the parts of a car are designed to drive. Such parts of a car do not change their behavior in time. On the other hand, a swarm of robots (Dorigo et al., 2004) will be conveniently described as self-organizing, since each element of the swarm can change its behavior depending on the current situation (Gershenson, 2017).

One of the key concerns about this approach is that self-organization generally implies that order emerges spontaneously as a purely bottom-up process. On the other hand, design implies that order and organization come into being by virtue of a designer in a top-down manner. According to Kelso et al(2016), this dichotomy is the wrong way to view this because, self-organization may require both bottom-up and top-down processes, and design that ignores bottom-up collective effects are at its own peril. Likewise, self-organization that happens in human domains can gain a lot from design, since it can deliberately prevent issues like lock-ins in local optima, global blindness, etc. With that insight in mind, this part is about using various constraints to bias the self-organization towards relevant contextual and functional dispositions above mentioned(in part 2).


Introduction to Constraints

Constraints in a general sense may be taken as restrictive. But in a complexity perspective constraints have a different understanding. Here, it is important to understand constraints are enabling too. They create opportunities for action, thoughts, and creation. Order in human life is primarily created through constraints. "It is not something which merely limits possibilities, constraints are also enabling. By eliminating certain possibilities, others are introduced” (Cilliers, 2001). For example, take the case of road traffic. Without conventions that shape how we drive(left or right), as well as our expectations of other drivers, smooth road transport will not be possible. Take the example of HTML, a constrained protocol that allows us to use the web. A cricket game works as a game because of various constraints like cricket ground, various constrained roles like a bowler, batsman, fielder, keeper, etc, and various associated rules that govern the game. Without this, we wouldn't have cricket as a game (examples adapted from Blignaut, 2021). Therefore, constraints not only remove or limit options but also create or enable order and new possibilities.

The mentioning of constraints as enabling can also be found in mainstream entrepreneurship research, for e.g. The idea of entrepreneurial Bricolage (Baker and Nelson, 2005) shows how entrepreneurs exploit opportunities despite resource constraints. Further, constraints have found an important place in the research on creativity, in that the concept of "constraints" is built into the construct of creativity itself (Sternberg and Kaufman, 2010). Research also has consistently found that without constraints, there can be no creativity (Dyer, Gregersen, and Christensen, 2009; Johnson-Laird, 1988). Both theoretical and empirical contributions investigating the entwinement of creativity and constraints exemplify the dual role of constraints, as constraints can be both limiting and enabling in creative processes (Joyce, 2009; Stokes, 2008, 2005; Negus and Pickering, 2004; Onarheim and Wiltschnig, 2010; Onarheim and Biskjaer; 2013).


The Science Of Constraints.

The concept of constraints has a rich background in science. It has been studied for over a hundred years, particularly in the pure sciences like chemistry, physics, biology, and evolution. According to Stuart Kauffman, "astonishingly simple rules, or constraints, suffice to ensure that unexpected and profound dynamical order emerges spontaneously" (Kauffman, 1995, p. 1). To Guerin and Kunkle(2004), scientific theories that claim to resolve fundamental issues of organization(self-organization) seek to explain the emergence of organization as an expected consequence of driving constraints forcing systems far from thermodynamic equilibrium. Listed theories being; Atkins, 1984; Brooks & Wiley, 1988; Kauffman, 2000; Kugler & Turvey, 1987; Prigogine, 1962, 1984; Schneider and Kay, 1994; Swenson, 1989; Tsallis, 1998; Ulanowicz, 1986. This shows that one of the fundamental questions in science is the emergence of order in complex physical, chemical and biological systems as they change over time? That is: how do they evolve, adapt, develop, mature, alter, modify, adjust and (re)organize, etc. And as an answer to these questions, the role of constraints has been studied.

It has been observed also that the surrounding energy patterns in an environment act as information that constrains complex systems to adapt over different timescales, resulting in different patterns over time. When a complex adaptive system encounters different conditions in the environment, it will self-organize to generate new structures to ‘represent’ those conditions. Self-organization under such constraints is considered ideal because it can develop a distributed form of internal structure (Cilliers, 2002). In this, the structure is neither a passive reflection of the outside, nor a result of active, pre-programmed internal factors, but the result of a complex interaction between the environment, the present state of the system, and the history of the system. In such a system, constraints can shape the emergence of self-organization; of events like movement, cognition, decision making, interactions, social organizations, etc. Further, there must be conditions in the environment that offer such enabling constraints that can limit what the system can do,-- to prevent it from being overwhelmed, but at the same time provide evolutionary potential.

Application of constraints: 3 models from which ESO-Loop took inspiration from.

Complexity-based design approaches are gaining a lot of interest nowadays. The use of constraints in designing for complex domains is an ideal practice because of the scientific strength of the concept. For the design of my framework, I am adopting insights from 3 of the most influential perspectives that use constraints-based approach to theory and design. They are;

1. Alicia Juarrero's context-sensitive constraints and context-free constraints, which is developed from the work of Lila Gatlin (Juarrero, 1999; Lila Gatlin, 1972).

One of the recent philosophical influences that attempted to set clarity on the idea of constraints and use of it as enablers is Alicia Juarrero's "Dynamics in Action"(Juarrero, 1999). Juarrero identifies formal uses of the concept of constraints in many places. Two examples she uses are in physical mechanics and information theory. In physical mechanics, she quotes Lindsay(1961, 239) who notes that "some of the most important cases of constrained motion are those in which particles are connected by rods and strings" and cannot, therefore, move any which way. Thus in physical mechanics, constraints are said to "compel " and "force" behavior. The term constraints here suggests not as an external force that pushes, but a thing's connection to something else by rods, cords, strings, and the like as well as to the setting in which the object is situated. She suggests that, in opposition to Newtonian science and modern philosophy's dismissal of relational properties as subjective, Lindsay's "constraints" mean something other than Newtonian causality, i.e., as features either of an object's connections with the environment or of its embeddedness in that environment. Constraints are therefore relational properties that parts acquire in virtue of being unified, not just aggregated into a systematic whole. It thus has shown that constraints not only reduce the number of alternatives, they simultaneously create new possibilities.

Secondly, Juarrero introduces information theory, primarily the work of Lila Gatlin to clarify the importance of constraints when transmitting or receiving a message, which requires a clear demarcation between message and background noise. Constraining the number of ways in which the various parts of a system can be arranged reduces randomness by altering the equiprobable distribution of signals, thereby enabling potential information to become actual information. Constraints thus turn the amorphous potential into the definite actual. Constraints work, then, by modifying either a system's phase space or the probability distribution of events and movements within that space. Constraints thus create coherence.

Further, according to Juarrero, Lila Gatlin (1972) distinguishes between two types of constraints: context-free constraints, which take a system's components far from equiprobability, and context-sensitive constraints, which synchronize and correlate previously independent parts into a systemic whole. When organized into a complex, integral whole, parts become correlated as a function of context-dependent constraints imposed on them by the newly organized system in which they are now embedded. A context-free constraint can be seen as a bias, assumption, preference, shared vision, intentions, etc. A context-sensitive constraint is something that is conditional on a state in the context. In entrepreneurship, it can be changing end-user preference, technology penetrations, government policy, infrastructure, etc.


2. Constraints-led approach(CLA) in sports coaching

The CLA is based on the fundamental concept of the mutuality of the performer and environment (Gibson, 2014) in which a learner will self-organize to generate effective movement solutions through the interaction of the three core categories of constraints– task, environment, and individual (Renshaw et al., 2010). The practitioners are therefore encouraged to design learning activities that allow individuals or groups of individuals to self-organize and co-adapt to changing constraints. The CLA has developed and evolved over the years by incorporating key ideas from multiple theoretical perspectives like ecological dynamics theory, ecological psychology, dynamical systems theory, evolutionary biology, and complexity science. It considers athletes and sports teams as complex adaptive systems--a network of highly integrated, interacting sub-components (Renshaw et al., 2010).

CLA acknowledges that Constraints both limit and enable the number of behavioral trajectories that the system can adapt. Complex systems are able to exploit the constraints that surround them in order to allow functional patterns of behavior to emerge in specific contexts. Secondly, CLA views constraints as either physical or informational. Physical constraints can be structural or functional in the human movement system. Functional physical constraints include processes such as reactions and perceptual abilities, which support movement performance. Informational constraints, on the other hand, are the various forms of energy flowing through the system. In complex adaptive systems, the multitude of parts continually forms coordinated patterns(synergies), which are shaped by surrounding such informational constraints. Thus states of order emerge under constraints. This idea has been imported into human movement science from physics and biology, where scientists have been engaged in studying the emergence of movement behaviors under constraints. Some example noted by Davids et al(2007) are Kelso(1995), Kugler(1986), Kugler & Turvey(1987).

The constraints-led approach's fundamental design concept has originated in the work of Newell (1986) who identified the interaction of three core categories of constraints – task, environment, and Organismic(individual), through which a learner will self-organize in his attempt to generate effective movement solutions (Renshaw et al., 2010). The coaching model thus involves manipulating, changing, designing these three core categories of constraints(task, environment, and individual) to promote self-organized capabilities and skills. The specific focus of practitioners is therefore on designing learning activities that allow individuals or groups of individuals to self-organize and co-adapt to changing constraints.

The three core categories of Newell's constraints can be elaborated as follows(Davids et al, 2007, p.40);

Organismic Constraints: Organismic constraints refer to a person’s characteristics, such as genes, height, weight, muscle–fat ratio, connective strength of synapses in the brain, cognition, motivations, emotions, and all other features that can be added to this category. A crucial difference between other biological organisms and humans is that we can intentionally constrain our actions to achieve goals or desired outcomes.

Environmental Constraints: Environmental constraints are global, physical variables in nature, such as ambient light, temperature, or altitude. On Earth, gravity is a key environmental constraint on movement coordination for all tasks Additionally, some environmental constraints are social rather than physical, including family support, peer groups, societal expectations, values, and cultural norms.

Task Constraints: Task constraints are usually more specific to performance contexts than environmental constraints are. It include goals, specific rules associated with an activity, activity-related implements or tools, surfaces, ground areas, and pitches and boundary markings such as nets, line markings, and posts. Even with apparently stable activities, such as playing a musical instrument or swinging a golf club, motor behavior can fluctuate because the task constraints may vary from performance to performance.

Interacting Constraints: When athletes engage in goal-directed action, the interaction of organismic, environmental, and task constraints on the neuromuscular system results in the emergence of different states of coordination that become optimized with practice and experience. Because of the interactive nature of these constraints, it is sometimes difficult to distinguish among them. This can be called interacting constraints.



3. Cynefin framework.

When it comes to the application of constraints in social and organizational contexts, Dave Snowden's Cynefin is one of the most comprehensive. Cynefin is built as a sense-making meta-framework that uses constraints as the key concept in defining and distinguishing its core typology, ie. It views the constraints of a system as informing and shaping the behavior of that system by modifying its phase space, i.e. its range of possible actions and movements within that space. The construct and evolution of Cynefin are centered on the understanding that complexity in human society is different from other types of complexity. The term Anthro-complexity is used to represent such complexity, which is a way of framing the ordinary, everyday landscape of human interaction.

In Cynefin, constraints play a key role in helping us understand the differences between its key domains in its typology. These typologies are; Clear, Complicated, Complex, and Chaotic. The Clear domain is associated with Rigid constraints, Complicated domain is associated with Governing constraints, Complex domain is associated with Enabling constraints, Chaotic domain is associated with the absence of constraints.

Rigid Constraints are rigid without any possibility or need for breaking it. Deadlines are an example of constraints that are usually intended to be rigid. Governing Constraints give a sense of stability but are sensitive to change. Examples are laws, rules, codes, etc. Enabling constraints are meant to enable us to do something by constraints certain possibilities. For eg, Heuristics and principles provide guidance while allowing for distributed decision-making. And finally, the absence of constraints is viewed as chaotic. Unlike in ordered systems (where the system constrains the agents), or chaotic systems (where there are no constraints), in a complex system, the agents and the system constrain one another, especially over time. This means that we cannot forecast or predict what will happen (Snowden and Boon, 2007 ).

Cynefin also has other types of constraints like; Internal/External Constraints, Connecting/Containing Constraints, Flexible/Permeable Constraints, Dark Constraints, Mutating Constraints, Loose Constraints, Elastic Constraints (Snowden, 2016; Cynefin, 2021).


Distilled from these 3 theoretical and practical models, following are the key insights that can be used to think about and design the ESO-Loop Framework.

Firstly, the core design of the framework is based on Juarrero's idea of Context-sensitive constraints and context-free constraints. This is taken as proposed by Lila Gatlin's information theory, and as articulated by Alicia Juarrero's philosophical framing in her book Dynamics in action(1999). Context-sensitive constraints are designed based on some of the persisting dimensions of the entrepreneurial state space. Context-free constraints are introduced as desirable functional dispositions that are globally applicable to both the user and the framework itself. This should not be viewed as a permanent structure but as a malleable scaffold (Pendleton and Brown, 2018; Cynefin, 2022a; Bruner, 1978).


Secondly, from the constraints-led approach to sports coaching, Newell's(1986) three categories of constraints are preferred to think about various context-sensitive constraints that are in operation in the entrepreneurial ecosystem. Apart from the three core categories of constraints–task, environment, and Organismic(individual), in entrepreneurship, it is possible to think about the constraints of or by the socio-material artifact(idea, image, prototype, MVP, startup, etc). Thus apart from the interactive constraints that are an emergent combination of all of the constraints, 4 core categories of constraints can be identified in entrepreneurship; Task, Environment, Organismic(individual), and Socio-material artifact(idea, prototype, MVP, organization, etc). Further, the constrain based coaching model can be used by ecosystem actors to mentor entrepreneurs, in which, when it comes to complex issues, mentors don't give or prescribe solutions, but provide or design constraints that enable the emergence towards a better state depending on the current disposition.

Thirdly, the Cynefin framework can be viewed as a globally applicable context-free constraint to design constraints itself according to various dynamic contextual demands. Entrepreneurs may encounter all kinds of situations including contexts like; clear, complicated, complex, and chaotic. All require different actions. Cynefin is specifically insightful when it comes to designing constraints in social domains like entrepreneurship.

It must be highlighted here that, while Juarrero's idea of constraints will form the fundamental structure of the framework, the insights generated from CLA and Cynefin are not explicitly applied on the structure of the framework, but form part of design logic of the framework in an implicit manner.


Context-sensitive constraints and context-free constraints

According to Juarrero(1999), the context-free constraints takes a system's components far from equiprobability, and context-sensitive constraints, synchronize and correlate previously independent parts into a systemic whole.

When organized into a complex, integral whole, parts become correlated as a function of context-dependent constraints imposed on them by the newly organized system in which they are now embedded. A context-free constraint can be seen as a bias, assumption, preference, shared vision, intentions, etc. It is something which biases the system even before the contextual factors comes into picture. The context-free constraints(CFC) takes a system's components far from equiprobability. With CFC, anything cannot happen. It positively biases the probability of something happening and at the same time reduces the probability of something else happening. Juarrero(2004) articulates the role of intention as a context free constraint. According to her, "new intention reorganize the earlier state space into a more differentiated and complex set of qualitatively novel options. This means that once an agent formulates a prior intention, every possible behavioral alternative no longer requires consideration; only a partitioned subset does".

A context-sensitive constraint is something that is conditional on a state in the context. In entrepreneurship, it can be entrepreneurial actions, team members, partners, mentors, end-user preference, technology penetrations, government policy, infrastructure, etc. .


Let us try to examine certain examples;

Example of a baby

A baby's genetics serve as a context-free constraint. It determines their inherited traits, physical characteristics, and predispositions. These genetic factors are predetermined and cannot be altered. On the other hand the interactions and experiences a baby has with their caregivers, peers, and environment serve as context-sensitive constraints. These interactions shape their social, emotional, and cognitive development. The language they are exposed to, the toys they play with, and the nurturing they receive all influence their growth and behavior.

Example of Crop Seed

The genetic makeup of a crop seed represents a context-free constraint. It determines the crop's inherent traits, such as its growth pattern, yield potential, and resistance to pests or diseases. These characteristics are determined by the seed's genetic code. On the other hand, the growth of a crop seed is influenced by various context-sensitive constraints. Factors such as soil quality, nutrient availability, water supply, sunlight exposure, and climate conditions impact the seed's germination, growth rate, and overall productivity. The crop adapts to these contextual factors to optimize its growth and yield.

Example of Language:

The grammar, vocabulary, and syntactical rules of a language represent context-free constraints. These constraints provide a structured framework for communication and understanding. They are predetermined and shared among language users. On the other hand, the context in which language is used represents context-sensitive constraints. Different people, social situations, cultural norms, and interpersonal dynamics shape the language choices, tone, and style of communication. Additionally, individual variations, regional dialects, and evolving language trends reflect the influence of context on linguistic expression.


Example of Traffic Signal

The standardized rules and regulations governing traffic signals serve as context-free constraints. These constraints establish the fundamental principles for traffic management and safety, such as red for stop, green for go, and yellow for caution. On the other hand, the dynamic traffic conditions, pedestrian movements, and specific road intersections represents context-sensitive constraints. The timing of signal changes, the synchronization of signal patterns, and the responsiveness to real-time traffic flow adapt to the current context, ensuring efficient traffic control and minimizing congestion.

Example of Money

The established monetary system and currency serve as context-free constraints. They provide a standardized medium of exchange, store of value, and unit of account within a particular economic system. The market forces, economic policies, and financial transactions introduce context-sensitive constraints to money. On the other hand, factors like inflation rates, interest rates, exchange rates, and government regulations impact the value, circulation, and flow of money within the economy, influencing economic decisions and transactions.

Example of Internet Protocol Languages (IP, HTTP, etc.)

The standardized protocols and languages used in internet communication, such as IP (Internet Protocol) and HTTP (Hypertext Transfer Protocol), serve as context-free constraints. They establish the fundamental rules and structures for transmitting data over the internet. On the other hand, our interaction through social media platforms like Twitter, Facebook, or Instagram, the specific applications we use, network configurations, and other forms of user interactions represent context-sensitive constraints.

These examples demonstrated that these two types of constraints are natural part of existence and all complex systems have this kind of manifestation of constraints.

What exactly are we going to do.

Our goal here is to use the frame of context-sensitive and context-free constraints to introduce various enabling constraints that significantly increase the dispositional propensity of positive outcomes for entrepreneurs, while simultaneously reducing the dispositional propensity of negative outcomes. These enabling constraints draw inspiration from various sources, including complexity theory, self-organization theory, and particularly the applied side of self-organization inspired from successful models like the Constraints-led approach to sports coaching, guided self-organization approach, Cynefin, and more.

To understand the nature of default enabling constraints more comprehensively, we can examine them from three analogies.

Firstly, these constraints can be viewed as a temporary default scaffold that supports entrepreneurial agency. They provide entrepreneurs with a solid structure that enables them to navigate and act effectively within their environment. Just as scaffolding provides stability and facilitates construction, these constraints offer a similar foundation for entrepreneurs to build upon, empowering them to achieve their goals.

Secondly, an analogy can be drawn between the constraints and the iconic Iron Man suit. Just as the Iron Man suit comes pre-equipped with advanced capabilities that enhance the user's own abilities, the default capabilities offered by these constraints become an integral part of the entrepreneur's own skill set.

Furthermore, these context-sensitive and context-free constraints can be likened to smartphones like Android or iPhone. Much like how new apps can be added, modified, updated, or removed on smartphones, these constraints possess a similar quality of adaptability and evolvability. The framework itself can be customized and refined to cater to the changing needs and circumstances of entrepreneurship, allowing entrepreneurs to leverage new functionalities and adapt to emerging challenges


Lets go to the design part starting with Context-Free constraints.  

A context-free constraint is something which biases the system even before the contextual factors comes into picture. The context-free constraints takes a system's components far from equiprobability. It reduces randomness of a system. That means anything and everything cannot happen. It positively biases the probability of something happening and at the same time reduces the probability of something else happening.

The role of intention as a context free constraint is a very interesting example. According to Juarrero, "new intention reorganize the earlier state space into a more differentiated and complex set of qualitatively novel options. This means that once an agent formulates a prior intention, every possible behavioral alternative no longer requires consideration; only a partitioned subset does" Juarrero(2004). Other examples are genetics, Internet protocol languages, Traffic rules, etc.

It is important to note that elements preceding the context are classified as context-free. This includes the Eso-loop framework itself, which functions as a context-free constraint. This may seem paradoxical, but it is an accurate representation. When the entrepreneurial agent actively interacts with the world, the framework transforms into an embodied reality that manifests within the actual context. At this point, context-sensitive constraints play a crucial role in facilitating coherent self-organization of the entire system, integrating both context-free and context-sensitive constraints into a unified self-organizing system.

Further, It is vital to understand that the core context-free constraints or default functional dispositions of this framework were developed mainly based on the ideas that I have discussed the second part(Building Of A Complexity Friendly Framework). These ideas were selected and introduced based on their relevance to the framework and also based on confirmation from various applied complexity models. For example, the "guided self-organization approach, co-developed by Carlos Christensen, highlights key concepts such as adaptation, robustness, fragility, mediators, slower is faster affect, and heterogeneity. Many of these ideas have been adapted as context-free constraints within this framework.

A very important feature to note here is that every human being or entrepreneur comes with a lot of unique types of context-free constraints. An entrepreneur with programming background may have a preference, bias or intention to start a machine learning startup. This is a context free constraint. Thus it may help to understand the difference between context free constraints that are already part of the entrepreneurs disposition and the context free meta-dispositions that I am going to introduce in this part. While, both are relevant context-free constraints with real impact on the entrepreneurs probability structure, the default context free constraints that I am going to introduce are much more fundamental.

Let's take the example of a car to further illustrate the concept of context-free constraints. Imagine a car with certain features that impose limitations on its performance. For instance, this car has a maximum speed limit of 40 miles per hour and produces loud noise while running. It's important to note that these constraints are not the fault of the car owner, but rather inherent in the design and manufacturing of the vehicle. These features act as context-free constraints that cannot be easily altered or modified.

Now, consider a scenario where the car is being driven in a situation where the government's traffic rules dictate higher speeds in certain lanes, and the car's loud noise exceeds the permissible limits set by sound pollution laws. In this situation, regardless of the circumstances, the traffic rules and pollution laws take precedence over the car's default constraints. The regulatory requirements and legal frameworks have a significant impact on the behavior and operation of the car, just as our framework's default constraints shape the entrepreneurial probability structure. This example highlights the interplay between context-free constraints and external factors, demonstrating that certain constraints, even if inherent to the system, must be adapted to align with the prevailing context and regulations.

Let's explore another example to further illustrate the concept of context-free constraints. Imagine a skilled python programmer who is highly proficient in the python programming language. This expertise and preference for python can be considered a context-free constraint that is part of the programmer's disposition. However, this programmer has limited familiarity with Android app development, which can be seen as another context-free constraint. Now, let's consider the context-free constraint of Android development itself. Developing apps for the Android platform requires adhering to specific standards and protocols established by the Android ecosystem. These standards, such as coding conventions, user interface guidelines, and compatibility requirements, are context-free in the sense that they apply universally to Android app development. In this scenario, the programmer's specialization and preference for python programming are context-free constraints that exist independently of the Android development context. However, when.if the programmer decides to venture into Android app development, they must navigate and adapt to the default context-free constraints imposed by the Android platform. The example emphasizes the distinction between the programmer's inherent context-free constraint (python specialization) and the default context-free constraints of Android platform. It illustrates the need for the programmer to reconcile their existing constraints with the additional constraints imposed by the specific context in which they aim to operate.

These examples highlight the notion that within the framework, the default context-free constraints hold primacy and influence the dynamics. It is important to note that this emphasis on default context-free constraints does not imply an imposing order. Rather, it signifies a nudging effect or enabling effect.


In the initial provisional design of the core context-free constraints or default functional dispositions, I have identified six key necessary default context free constraints. They are;

Disposition 1: Agency
Disposition 2: Diversity
Disposition 3: Affordances
Disposition 4: Adaptiveness
Disposition 5: Evolvability
Disposition 6: A permanent work in progress.


Let me elaborate this.  

The objective of the disposition

Agency is the first key default dispositional propensity of the framework. Here it is suggested that agency should be free, devoid of external control and must be contextually sensitive via developing relational agency. This disposition is about enabling the cultivation of an appropriate type of agency and also to prevent agency hijack by people, processes, models, tools, etc.

Explanation

Agency is described as an individual’s ability at any given point in time to act independently in order to change the internal or external environment (Bandura, 2001; Campbell, 2009; Hitlin & Elder, 2007). In a complex and uncertain world, we make decisions under the constraints of limited knowledge, resources, and time. This results in the tendency of the human brain to solve problems in the most simple and straightforward way rather than utilizing a more sophisticated and effort-intensive way. This human nature is often exploited by external agents who come up with confident advice and one-size-fits-all models.

The impact of such environmental factors on the human agency is being discussed extensively in the sociological debate on structure vs agency (Hays, 1994). It is an issue of control exercised by the environment against autonomy in determining whether an individual acts as a free agent or in a manner dictated by socio-material structure. A clear understanding of such environmental control must also involve the debate on the effect of human artifacts. According to Bruno Latour (Latour 1996), once designed and introduced into the interactional scene by humans---texts, artifacts, and objects of any kind make sense and have an agency on their own (Caronia and Mortari, 2015). The entrepreneurial implication is that artifacts and existing models like Lean startup, business plan, etc, are that, if used as a prescriptive model will have a take-over-effect on the entrepreneur's agency. They make a significant difference in the way we see and act in the world. We can design, create or use them, but in a way, they also design, create or use us. We are designed by our designing and by that which we have designed i.e., through our interactions with the structural and material specificities of our environments.


This framework doesn't have any one-size-fits-all solution for the agency problem. Instead, it has the following pointers:

To the disposition, an entrepreneur must be capable of behaving adaptively, freely, and effectively in a given situation, exercising his agency to the fullest. Agency here doesn't mean the ability to agenda-setting or control things, but is a catalyst for self-organization. This is to use to the best the evolutionary potential of the present. This means considering other agencies, co-adaptive potential, co-evolutionary potential, path-dependence, etc. It is an attempt to make the entrepreneur aware of agency and its importance in complex domains where ground realities change rapidly. It enables and reminds the agent of its agency. It nudges or biases the entrepreneur to deal with the agency problem and agency exercise. So it is up to the entrepreneur alone to find solutions to his problems.

This default context free constraint suggests that no matter what you do, you should be concerned about your agency. Another thing to note here is that agency is very much connected to the idea of autonomy. In-order for self-organization to take place autonomy is necessary. Without autonomy, self-organization cannot happen.


For the framework, I propose the following understanding of agency;

1. Agency here means relational agency (Edwards, ,2005) that sees the world as a network of interacting agencies rather than a collection of independent objects subjected to external forces (Heylighen, 2022).

2. This agency is also located in the social relationship people have with the material world(e.g. places) and material objects(Sillar, 2009). This suggests that the material also have social identities and agency of their own. This can otherwise be called social agency of things.

3. Agency is not only distributed across actors (Garud and Karnøe, 2005) but it is embedded as well (Garud and Karnøe, 2003). I.e. Actors become embedded in paths that they try to shape in real time.

4. Such agency has a kind of awareness of its(his/her) own Intention, dispositional propensities, Adjacent possible. Such agency has a kind of awareness(connection, entanglement) of the Intention(attractor) of other systems/agents, Phase space disposition of other systems/agents, Adjacent-possible of other systems/agents. 

The objective of the disposition

This disposition or context-free constraint is about enabling cultivation of diversity. More specifically, a diversity that is specific to entrepreneurship ecology.


Explanation

This context-free constraint focuses on enabling the cultivation of a specific kind of diversity that is relevant to the entrepreneurial ecology in which the entrepreneur operates. It involves striking a balance and remaining attuned to the contextual nuances, avoiding both insufficient diversity and excessive diversity that may compromise the system's coherence and survival. Thus, diversity needs to be regulated. Furthermore, it involves fostering a continuous effort to capture the true ecological reality of the entrepreneurial world in its dynamics. For instance, entrepreneurs should be encouraged to explore the multidimensional state space for functional affordances related to diversity, such as diverse individuals, their skill sets, networks, information sources, relational expertise, institutions, specializations, artifacts, tools, and more. Both explorations at the conceptual-cognitive level and in the real world must align to cultivate the type of diversity that contributes to entrepreneurial success.

As I have previously discussed, it is important to adopt a context-specific ecological worldview. I have previously examined how this is different from the Single model, Multi-Model, Cognitive-Diversity and Holistic diversity view points. The ecological world view of the framework includes a diversity approach that is not only inclusive of all the above views, but also, of realities like complexity, dynamics, evolution, connectedness, nonlinear interaction, etc. This view thus promotes;

* Cognitive diversity
* General diversity
* Diversity in Its dynamics(elements of diversity will interact, mutate, evolve and die)
* Diversity that is uniquely in relation to the entrepreneurial agent in question


Importance of diversity

According to Paul Cilliers(2002), Complex systems are constrained, they have an organized structure, but within those constraints, the system has to diversify maximally in order to thrive. Thus, In this section, I am introducing the dispositional element of diversity to enable the cultivation of diversity. Evidence on the importance of diversity is overwhelming and can be found in many different disciplines. It is viewed as advantageous because it can help a system to consistently reach and sustain desirable settings for a single system property by providing multiple distinct paths to a particular state (Whitacre and Bender,2010).

When considering biological evolution, genetic diversity helps maintain the health of a population by including alleles that may be valuable in resisting diseases and other stresses. When and If the environment changes, a population that has a higher variability of alleles will be better able to evolve to adapt to the new environment. Thus genetic diversity serves as a way for populations to adapt to changing environments. With more variation, it is more likely that individuals in a population will possess variations of alleles that are suited for the environment. Those individuals are more likely to survive to produce offspring bearing that allele. Further, biological diversity is essential in the self-organizing ability of complex adaptive systems(Levin, 1999) both in terms of absorbing disturbance and in regenerating and re-organizing the system following disturbance(Folke,2006). In social systems, the success of the human species also has a lot to do with diversity. Many animals exhibit social learning and behavioral traditions, but, human culture appears unique in that it is cumulative, i.e. human cultural traits increase in diversity and complexity over time (Legare, 2017).

The importance of diversity was also demonstrated in various domains and scientific studies. Some examples are; Micro diversity (Allen, 1976; 1994; Allen and Jacqueline, 1987) ), Functional redundancy (Naeem 1998, Petchey et al. 2007), Response diversity (Elmqvist et al., 2003), Diversity in Social-ecological systems (Ellis 2000; Low et al. 2003), Portfolio theory in economics and biodiversity management (Schindler et al, 2010), Problem-solving in decision making (Hong and Page, 2004, 2001), Business Performance (Kochan et al, 2003), Cultural diversity (Ely and Thomas, 2001), Experiential diversity (Weigelt and Sarkar, 2009; Spanjer and Witteloostuijn, 2017), Ensemble models (Tebaldi and Knutti, 2007; Sagi and Rokach, 2018; Anderson,1996), Cognitive diversity (Mello and Rentsch, 2015; Olson et al, 2007), etc.


Cognitive diversity(CD) that includes following cognitive repertoires(Scott Page);

Information Diversity(data, facts), Knowledge Diversity(expertise and knowledge in a domain), Heuristics Diversity(simple rules of them, techniques), Representations Diversity(perspectives on the situation), Mental model Diversity(simplified) simplify complex ideas.

Diversity in general(DG) means;

Skill Diversity, Functional Diversity, People Diversity(not just cognitive, but functional, or based on potential), Network Diversity, Information source Diversity, Relational-expertise Diversity, Institutional Diversity, Cultural Diversity, Location Diversity, Specialization Diversity, Artifact Diversity, Tool Diversity, etc.

Dynamics and Evolution

The diversity or elements of variation in an entrepreneurial ecosystem is constantly in interaction, dynamics, and evolution. Every part is connected to every other part and they exchange energy and information. This makes it important to be in a continuous exploration mode. Thus, diversity must be approached with the view that the real world has complexity, it is dynamic, it is always evolving, it is connected, in it, elements interact with each other. Practically this means that ideas will evolve, tools will become useless, people will die or move on, things and forces will mutate together to form unknowns and incomprehensible things.

This must also accommodate exploration of more types of diversity which is missing from the above list. There can also be opposing and contradictory diversity too. 

The objective of the disposition

Here I will introduce the concept of Affordance. I will discuss why affordance as a concept can be used to check the ecological validity of a model, or education program, etc. This will help us to promote ecological validity of ideas, models, etc., and to prevent ecologically invalid ideas from taking over too much time, space and resources.


Explanation

Affordances refer to the offerings or action possibilities present in the environment, relative to the action capabilities of an actor. It represents a direct relationship between the actor and the specific action possibilities available in their environment. The dispositional propensity of affordances can serve as a means to assess the ecological validity of prescriptive models, solutions, education programs, and ideas utilized in the field of Entrepreneurship, among others. This approach aids in promoting the ecological validity of ideas and models, while also preventing ecologically invalid ideas from consuming excessive time, space, and resources.

Affordances and Ecological validity

The concept of ecological validity was originally proposed by Egon Brunswik(1956). At a later point, Ulric Neisser(1967) used the concept of ecological validity to refer to the external validity of research design. Since then researchers have generally used the label to refer to external validity. For the ESOLoop framework, ecological validity is adopted as a simple rule or heuristics to think about the validity of ideas used. This is to prioritize the context-dependent realities of the entrepreneur (Trueblood et al, 2013; Hutchins and Klausen, 1996). In other words, we must prioritize to view the world with an ecological perspective where the agent is entangled in a deep relationship with the environment.

The framework adopts the concept of affordances as a useful tool to enact ecological validity. The concept of affordance was developed by J.J Gibson(1977) to describe the interactions between a goal-oriented actor and an object in the environment in terms of what it “affords” the actor, or in other words in terms of action possibilities for meeting the actor’s goal.

According to Gibson(1979/2014), environmental properties provide affordances for each individual. They are not intrinsic properties of the object. Rather, affordances emerge from the relationship between the object and the actor with which it is interacting. Further, the perception of affordances depends on one’s needs in the specific situation at hand as well as on the ultimate aim of the action.

With learning, experience, and knowledge, entrepreneurs can become attuned to the perceptual variables of various affordances available in a taskscape. At a given point, entrepreneurs are provided with a manifold of action possibilities, which are uniquely relative to the entrepreneur. Due to these differences in the affordances available for different entrepreneurs, the opportunities for action in an environment differ between people, problems, and contexts. Because affordance is relative to each entrepreneur, each requires their own unique path of exploration. Entrepreneurs need to learn and explore the environment in a way to get more and more attuned to available desired affordances that are functional for entrepreneurial action at the opportune time.

Ultimately the effect of an intervention, mentorship, education, policy, etc on entrepreneurial activity or learning can only be understood by what affordances it has provided the entrepreneur.

The objective of the disposition

This part stress the adaptiveness of the individual to the dynamic environment. The primary objective is to enhance adaptiveness for entrepreneurs, the framework, and other stakeholders, aligning with the emerging realities they encounter. The focus is not on prescribing specific methods to improve adaptiveness, but rather on empowering entrepreneurs to explore and understand what adaptiveness means in their unique contexts and address the challenges.


Explanation

Terms like expertise, skill acquisition, competence, etc, often comes with an individualistic bend. This is clearly visible in disciplines like entrepreneurship, psychology, philosophy, education, movement science and performance development, etc. In cognitive psychology it goes further extremes to the establishment of an internal state or representation of an act which is believed to be acquired as a result of learning and task experience. Adopting the suggestion by Araújo and Davids (2011) from the domain of sports to entrepreneurship, I propose that an ecological perspective to entrepreneurship should stress more on adaptive functional relationship between an organism and its environment, thus avoid the inherent organismic asymmetry that comes with possessive individualistic framing like skill acquisition. Here I will discuss what it means to be adaptive and what are dimensions of adaptation that are relevant.

A complex system is adaptive when heterogeneous components react differently to outside influences, thus continually modifying the system and allowing it to adapt to altered conditions (Messier et al, 2016). In biology, adaptation primarily involves the dynamic evolutionary process that fits organisms to their environment, enhancing their evolutionary fitness. Adaptation is important because it makes a system better and able to sustain itself. This is because nature and evolution do not favor stability and equilibrium: instead, natural processes select for resilience and adaptability (Juarrero, 2015).

In order to adapt to a dynamic environment, a complex system needs a sufficiently large variety(diversity) of possible states to cope with likely perturbations. Further given this variety(diversity), the most adequate configurations are selected according to their fitness, either by the environment or by subsystems that have already adapted to the environment at an earlier stage (Heylighen 2001). The same opinion on the relationship between diversity(variation) and adaptation is shared by the Constraints-led approach (Renshaw et al, 2019), according to which, "Skill adaptation implies that performance goals can still be achieved, as athletes learn to vary their actions according to the information that emerges in unpredictable performance contexts. Adaptation provides a functional relationship between stability (i.e. persistent behaviors) and flexibility (i.e. variable behaviors) during performance. Highly skilled performance is characterized by stable and reproducible movement patterns, which are consistent over time, resistant to perturbations."

The central questions

The central questions of adaptive dimension are, how to be adaptive? how to be co-adaptive? and 'how to adapt the knowledge, skill, resources, etc, to best suit the ever-changing, ever dynamic entrepreneurial contexts? What is preventing the functional adaptation from taking place.

I am calling this context free constraint as the adaptive constraint because everything we learned and understood previously needs to be rapidly adapted to the dynamic entrepreneurial context, in pursuit of entrepreneurial self-organization. 

The objective of the disposition

Since I have discussed diversity and adaptiveness, two qualities necessary for the self-organizing complex adaptive system, this part is about the overarching concept of system evolution and how we can enable evolvability. This context-free constraint aims to enable evolvability as a dispositional propensity within the framework.

Explanation

Evolution is a fundamental feature of nature, biology, and human social life. In comparison to biological evolution, the human social system is capable of adapting to change at a faster rate. It is particularly significant in the case of entrepreneurship in that entrepreneurs often act as catalysts of social evolution and also are affected by the smallest of changes in the environment. One significant weakness found in existing entrepreneurship models, such as Lean Startup and Business Plan, is the lack of evolvability. Once proposed and written down the model never changes. Even with this vulnerability, most ideas and models in the entrepreneurship domain are self-aggrandizing and self-perpetuating. This often works against the agent's evolutionary potential because it gives a false sense of comfort and certainty, blinding the agent from ever realizing the true emergent nature of entrepreneurial complexity. This framework accepts its own possible weaknesses and contingencies and has evolvability as default constraint. Evolvability in this framework means you can learn, unlearn, add, subtract, design, create, combine, remix, recombine and customize solutions to suit the ever-changing dynamics of idiosyncratic and contextual needs. In other words,"evolving to greater evolvability" (Juarrero, 2015) is the goal here.


Following are some of the features that can enhance evolvability:

1. Diversity(or variation): As discussed in disposition 2 on diversity, cultivating diverse elements within the system promotes evolvability.

2. Adaptation and co-adaptation: As discussed in disposition 3, the ability to adapt and co-adapt to changing circumstances is crucial for evolvability.

3. Resilience design(Ruhl, 2011; Curtin, 2014): Resilient design considers potential future crises and contingencies, allowing the system to continue evolving even in challenging situations.

4. Degeneracy cultivation: Encouraging degeneracy, or the ability of different elements to perform similar functions, enhances evolvability by providing redundancy and flexibility.

5. Destabilizing: Introducing controlled perturbations or disruptions can create opportunities for new adaptations and evolutionary pathways.

6. Avoid rigid constraints and prescriptions: Imposing rigid constraints and prescriptions hinders evolvability and lacks ecological grounding. This can be observed in the limitations of existing prescriptive models in entrepreneurship.

7. Framework as a scaffold: Scaffolding is a temporary structure used to support the development of a structure. It is not meant to be permanent. Existing prescriptive models and frameworks propose permanent structures that end up being inflexible. This framework is not designed as a permanent structure but as an impermanent scaffold that enables emergence (Pendleton and Brown, 2018; Cynefin, 2022a).

8. Learning/Learnability: Emphasizing continuous learning and the ability to acquire new knowledge and skills enhances evolvability by enabling the system to adapt and respond to novel challenges.

These features serve as foundational principles for cultivating evolvability within the entrepreneurial context.

The objective of the disposition

The sixth and final default context-free constraint is the permanent work in progress. This disposition acknowledges ESO-Loop Framework itself as a permanent work in progress. It provides the capability to make changes to the framework itself. Two concepts are discussed; they are Provisional Imperative and Permanent construction.

Explanation

This disposition acknowledges the dynamic nature of the world and the need for continuous adaptation. It recognizes the framework itself as an ongoing development, allowing for changes and improvements over time. It emphasizes the importance of providing opportunities for adaptation within the framework. Users are encouraged to actively contribute their ideas, designs, and modifications to align the framework with their specific contextual demands and dispositions. This approach can be likened to the concept of open-source software development, which fosters open contributions from a diverse community. This disposition extends the notion of evolvability and emphasizes the framework's openness to evolution and co-creation.

Provisional Imperative

The central insight from complexity is that we cannot form a complete picture of the current situation and its meaning, nor can we formulate an exact goal for which we should aim. Our decisions thus will always have to be based on an incomplete understanding and thus will have to be provisional. According to Cillers(2002), In complexity, interpretations are contingent and provisional, pertaining to a certain context and a certain time frame(p. 121-122). This leads to the idea of Provisional imperative (Preiser and Cilliers, 2010; Woermann and Cilliers, 2012).

According to Preiser and Cilliers(2010), the central characteristics of complex systems we have to consider are the following:
1. our knowledge of complex things is radically contingent in both time and space,
2. any decision we make concerning something complex has to be irreducibly provisional, yet
3. we have to act in a way which distinguishes the action from its alternatives otherwise we are not acting at all,
4. meaning emerges through the mutual interaction (both constraining and enabling) amongst components in the system, not through some pre-defined essence. Thus, as subjects we are constituted through interactions with others (both human and non-human) around us. My state depends on the state of others.

Preiser and Cilliers(2010) further points out that these characteristics can be used to formulate an imperative of provisionality while taking action, which include following:
1. justify your actions only in ways which do not preclude the possibility of revising that justification,
2. make only those choices which keep the possibility of choice open,
3. your actions should show a fundamental respect for difference, even as those actions reduce it,
4. act only in ways which will allow the constraining and enabling interactions between the components in the system to flourish.

Woermann and Cilliers(2012) summarizes this way of acting to “When acting, always remain cognisant of other ways of acting”.

Embracing this idea as a cornerstone, the disposition of "Work in progress" enables the framework itself to change according to contextual and evolutionary demands. Thus It is important that the framework itself is under perpetual construction(Prigogine, 1996). 

I introduced six of the default context-free constraints. It is important to remember that entrepreneurs always have their own context-free constraints. If these constraints involve rigid models like a business plan or Lean Startup, it might be counterproductive. However, if the default constraints of this framework take primacy, this is not a problem. It is crucial to understand that if actors use this framework, the primacy of these default constraints becomes a dispositional reality rather than an option. It is like using the Iron-Man suit, which has specific dispositional propensities and capabilities. Although the suit is adaptable and changeable to a certain degree, these default constraints or capabilities take primacy for the system to work properly. This can be compared to Android or iPhone system apps, where changes to the app can impact the function of the entire system, potentially leading to its collapse. However, if changes and variations are introduced at a lower level, the functioning of the systems will not be affected.

Now that I have completed introducing the six core default context-free constraints, it is important to understand that users of this framework can add, design, or use their own context-free constraints that align with the ideas already discussed. For example, in the Praxis session, I introduced two more context-free constraints: enskillment and sense-making. These ideas will be discussed in another session. This means that users of the framework can incorporate their own choices that are compatible with the default constraints.

To recap, the default context-free constraints represent the default dispositional propensities of the framework and its users. This suggests that if you are using this framework, these default context-free constraints become your dispositional propensities as a user or entrepreneur. This means you cannot ignore agency, the need to cultivate and regulate diversity, you cannot ignore affordances and ecological validity of ideas and educational programs, you cannot ignore adaptiveness, you cannot ignore evolvability, and finally you cannot ignore the importance of having the dispositional propensity of having a permanent work in progress. All of these are primary constraints, reflecting the key idea of the contact-sensitive constraints.

In the next session, I will discuss the second key type of constraints, which are context-sensitive constraints.

Context-sensitive constraints play the important role of synchronization of previously independent parts into a systemic whole. A context-sensitive constraint in entrepreneurship is something that is conditional on a state in the context. Every entrepreneurial context is different and inside every contexts there are multiplicity of nested and emergent contexts. It can be related to entrepreneurial actions, team members, partners, mentors, end-user preference, technology penetrations, government policy, infrastructure, etc.

These elements converge in a self-organizing process, integrating both context-sensitive and context-free constraints into a unified system. The context-sensitive constraints act as the glue that connects and correlates the previously independent parts, enabling them to function coherently. As Alicia suggests, these context-sensitive constraints synchronize and correlate various components, allowing them to work harmoniously as a systemic entity. By adapting to the context-dependent constraints imposed on them, the parts become interdependent and interrelated, contributing to the overall success and effectiveness of the entrepreneurial self-organization. The interplay between context-free and context-sensitive constraints is crucial for the dynamic and evolving nature of entrepreneurship, facilitating the integration and alignment of diverse elements within the entrepreneurial ecosystem.

It is important to note here that my use of Alicia Juarrero's idea of context-sensitive constraints is not intended to represent her articulation accurately, but to make use of her idea to design a solution for entrepreneurs. The key point is that I cannot, and nobody can, represent the real context and contextual dynamics a priori (beforehand), so I cannot claim to represent Alicia Juarrero's framework accurately when designing a solution or design. However, despite this, the ultimate intention here is to bring the entrepreneur closer to the original meaning intended by Alicia. Thus, the idea of context-sensitive constraints used here is not an accurate representation of Alicia Juarrero's understanding, but rather an enabling constraint that scaffolds entrepreneurial exploration towards certain key attractor dimensions.

With that acceptance, the operation of context-sensitive constraints can be viewed at micro and macro levels. As a framework, the general structure of ESO-Loop cannot be designed to foresee all kinds of constraints applicable to all kinds of users and their contexts, whether it is micro or macro. So the agenda here is not to build a one-size-fits-all rigid model of constraints or to micro-manage for all entrepreneurs. But to build an evolvable, changeable, flexible map of macro-contextual constraints that are globally applicable to all or most of them. Think of it metaphorically as demarcating a football ground (or a map). The ground is an enabling constraint inside which multiplicity of contextual-constraints can co-exist and co-emerge. This could include number of players, skill level, age, rules, etc, all of which can interact, emerge and co-evolve. Thus this macro level constraints are enablers for exploration, which is intended for the entrepreneurs and other ecological actors.

Design global contextual-constraints based on current disposition of global entrepreneurial state space and attractors.

Self-organizing systems typically evolve towards a state of equilibrium, or an attractor state. Once the system reaches there, the further dynamics and evolution of the system will likely be constrained to remain near such attractor or attractors. Other words, In a complex emergent system, an attractor is a set of states towards which a system will naturally gravitate and remain cycling through. They are islands of stability in a sea of chaos. Thus, even though complex systems are inherently in constant dynamics, they tend to usually settle down into one of a number of possible steady attractor states. These states are called "attractor basins". Some examples of attractor states are; Traffic patterns in a city in working days, Distribution of ethnic groups across many multicultural cities, Distribution of political opinions across the different regions of some nation, etc. For any system we can visualize what is called a state space that represents all the possible states that that system might take which is very much related to current distribution of attractor states and basins.

One of the most important challenges of this framework is to identify and design constraints that enable entrepreneurial self-organization towards a global optima, instead of being stuck in local optima. Such constraints should also make it easier for entrepreneurial agents to transparently see and navigate the big picture(The Forest), even if s/he had to attend to the contextual demands(The Tree). This is where the idea of designing constraints based on major attractors in global state space seems like the optimum thing we can do. Unlike sports or established organizations, entrepreneurship has fewer pre-given constraints like environment(football ground), objective goal(score a goal), rules(rules of engagement), players( limited numbers), etc. This means both uncertainty and potentiality are high. But even in this uncertainty, we can identify some stable elements which form part of entrepreneurship state space.

This idea about attractor states was very well articulated(probably inadvertently) by Jeff Bezos in a 2012 interview in which he stressed the need to think in terms of such stable elements in the entrepreneurship ecology. More specifically to him, decisions should be based on what is not going to change in 10 years, than, what is going to change in 10 years. His comments(Bezos, 2012):

“I very frequently get the question: 'What's going to change in the next 10 years?' And that is a very interesting question; it's a very common one. I almost never get the question: 'What's not going to change in the next 10 years?' And I submit to you that that second question is actually the more important of the two-- because you can build a business strategy around the things that are stable in time....In our retail business, we know that customers want low prices, and I know that's going to be true 10 years from now. They want fast delivery; they want vast selection. It's impossible to imagine a future 10 years from now where a customer comes up and says, 'Jeff I love Amazon; I just wish the prices were a little higher,' [or] 'I love Amazon; I just wish you'd deliver a little more slowly.' Impossible. And so the effort we put into those things, spinning those things up, we know the energy we put into it today will still be paying off dividends for our customers 10 years from now. When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it.”


8 stable exploratory dimensions(Context sensitive exploratory dimensions)

As a design choice, I have identified 8 stable dimensions(that can be viewed as attractor states) in entrepreneurial state space as primary enabling constraints. This list of eight is not comprehensive, but just the result of my satisficing effort to design a scaffold (Pendleton and Brown, 2018; Cynefin, 2022a; Wood, Bruner, and Ross, 1976) that enables the entrepreneur to capture the nature of global state space. It is encouraged that entrepreneurs continuously explore the environment for insights and affordances using the scaffold of these 8 constrain dimensions . Further, entrepreneurial agents are encouraged to add, remove, or design, their own constraints that seem necessary at the particular temporal and contextual point. It provides the entrepreneurs, mentors, and other ecosystem actors with metaphorically-- a playground to initiate their own unique constraint-based designs to enable entrepreneurial self-organization.

Agent/ The end user/Decision Maker: The human being or the kind. This is the end-user to whom entrepreneurs cater to.

Socio-Material Dimension Of The End User/Decision Maker: The society and its dynamics including technology and material aspects in which the agent is embedded.

Temporal and Contextual dimension of End User/ Decision Maker: Every moment of an agent's life represents a different context, which include dimensions like time, situation, role, relationships, etc. In here the context that is specific to entrepreneurship is important. This is primarily influenced by Alicia Juarrero's advocacy for combining time and context together in her work Dynamics In Action(1999).

Needs/Wants/Choices: Human needs(or end user needs/decision maker needs) are commonly used to refer to the drivers of people's actions, the motives behind human behavior. Such needs arise out of specific contexts.

Organization to deliver for Needs ( or Startup/ Venture/ Product/ Idea): The fundamental function of an entrepreneurial organization is to cater to human needs. The organization here may be the entrepreneur, a product, or a startup.

Networks and Ecosystem disposition: This represents the direct connections and embeddedness of the organization(entrepreneur, a product, or a startup) in an ecosystem or bigger environment. More specifically we can think of it as the disposition of the organization in the ecosystem. E.g. dimensions include Network effect, Platform model, Network of Partners, Network of Customers, etc.

Atmosphere: This includes the general features of the entrepreneurship atmosphere or environment. For e.g. those represented by models like 5 forces, PESTLE, etc.

Unconventional Forces: This includes things and forces that are unconventional, unknown or unseen, etc. Corona Virus, New exponential Technologies, Its combinations, etc.

It must also be noted that boundary conditions in complex systems function not as walls, but as “active sites,” like eardrums or membranes(Cilliers, 2002; Juarrero,2013). Thus these 8 dimensions are part of the same state space, hence, encouraged not to see it as separate from the whole. They are interconnected and one in their dynamics. By accepting the inseparable-inter-connectedness it is easy for us to maintain the complexity perspective in mind.

The next key point to convey is that the entrepreneurial actor is expected to explore these eight default exploratory dimensions, with a specific focus on understanding the dynamics of both cognitive and embodied real-world features and affordances associated with each dimension. By distinguishing between cognitive and real-world enactive affordances, we emphasize that cognitive imagination alone is not equivalent to real-world action possibilities. This distinction is not intended to exclude the cognitive dimension; rather, it is introduced to underscore the importance of considering real-world affordances and connecting cognitive ideas to actionable possibilities. The key idea is to redirect the system's overall propensity toward providing action possibilities for the entrepreneur. It is important to note that this exploration should not be limited to immediate action possibilities alone, but should also aim to cultivate future action potential. For instance, attending a networking event or assisting someone today may not immediately provide affordances, but can contribute to future opportunities. Now, let us examine each of these exploratory dimensions in detail.

Agent/ The end user/Decision Maker: The human being or the kind. This is the end-user to whom entrepreneurs cater to.

Let us begin with the Agent dimension, which is the first key exploratory dimension. This dimension focuses on understanding and developing actionability in relation to the agent, who is the end user that the entrepreneur caters to. The agent can encompass a range of entities, such as a dog, a baby, an elderly person, or the actual individual who may make the purchasing decision. It is important to note that there are no clear boundaries within this dimension. The entrepreneur must continuously explore this dimension of the state space in order to cultivate diverse cognitive understandings and develop action possibilities based on this understanding.

Examples; 
Explore the intricate world of neurological processes behind decision-making and understand how it influences consumer choices.

Explore the role of emotions in shaping purchasing behavior and brand preferences, uncovering new insights into consumer psychology.

Explore the impact of cognitive biases on consumer perceptions and decision-making, revealing the hidden factors that shape consumer behavior.

Explore the influence of personality traits on consumer buying patterns and brand loyalty, unraveling the complexities of consumer psychology.

Explore the effect of cultural differences on consumer preferences and perceptions of products, gaining a deeper understanding of diverse consumer markets.

Explore the role of social influence and peer recommendations on consumer decision-making, uncovering the power of social networks in shaping consumer behavior.

Explore the physiological responses and brain activity associated with positive and negative consumer experiences, revealing the science behind consumer emotions.

Explore the impact of sensory marketing on consumer perceptions and product evaluations, discovering the potential of multisensory stimuli in creating compelling brand experiences.

Explore the role of trust and credibility in consumer decision-making and brand reputation, understanding the importance of building trust in the digital age.

Explore the influence of consumer expectations and prior experiences on product satisfaction and repeat purchases, gaining insights into customer-centric strategies for business success.

Socio-Material Dimension Of The End User/Decision Maker: The society and its dynamics including technology and material aspects in which the agent is embedded.

The second key exploratory dimension is the social materiality of the agent or the end user. This dimension focuses on understanding the social and material aspects of the end user or decision maker's environment. It involves learning about and developing affordances that are relevant to the social system in which the agent operates, as well as the technological and material dynamics that shape their experiences. By exploring this dimension, the entrepreneur gains insights into the societal norms, cultural influences, technological advancements, and material resources that impact the end user's behaviors, preferences, and decision-making processes. This understanding allows the entrepreneur to design products, services, or solutions that align with the social and material context of the agent, enhancing their value proposition and increasing the likelihood of adoption and satisfaction.

Examples;

Explore the influence of institutional structures and regulations on entrepreneurial behavior and market dynamics, uncovering the factors that shape the entrepreneurial landscape.

Explore the role of technological advancements and digital platforms in shaping consumer behavior and market interactions, revealing the transformative power of technology in the business world.

Explore the impact of cultural artifacts and symbols on branding strategies and consumer perceptions, understanding how culture shapes brand identity and consumer preferences.

Explore the role of tools and technologies in enhancing productivity and efficiency in entrepreneurial ventures, unlocking innovative solutions for business growth.

Explore the dynamics of market competition and the effects of market forces on entrepreneurial success, gaining insights into strategies for thriving in competitive markets.

Explore the role of intellectual property rights and patents in promoting innovation and entrepreneurship, uncovering the value of protecting intellectual assets.

Explore the influence of social networks and connections on resource access and entrepreneurial opportunities, uncovering the power of networks in entrepreneurial success.

Explore the role of institutions and policies in fostering entrepreneurship and supporting startup ecosystems, understanding the ecosystem dynamics that nurture entrepreneurial ventures.

Explore the impact of cumulative cultural knowledge and historical legacies on entrepreneurial practices and business models, uncovering the influence of culture on entrepreneurial success.

Explore the role of market segmentation and targeting strategies in adapting products and services to specific consumer needs and preferences, unlocking strategies for effectively reaching target markets.

Temporal and Contextual dimension of End User/ Decision Maker: Every moment of an agent's life represents a different context, which include dimensions like time, situation, role, relationships, etc. In here the context that is specific to entrepreneurship is important. This is primarily influenced by Alicia Juarrero's advocacy for combining time and context together in her work Dynamics In Action(1999).  

The third key exploratory dimension is the temporal and contextual dimension of the end user or decision maker. It recognizes that every moment in an agent's life represents a unique context shaped by various factors such as time, situation, role, and relationships. In the context of entrepreneurship, it is crucial to understand the specific context in which the end user operates, as it is from this context that needs and opportunities emerge. By exploring this dimension, the entrepreneur gains a deeper understanding of the temporal and contextual factors that influence the end user's behaviors, preferences, and decision-making processes. This knowledge allows the entrepreneur to identify market opportunities that align with the dynamic nature of the end user's context, enabling the development of innovative solutions that address their evolving needs effectively.

Examples; 

Explore the multiplicity of contexts in which end users operate, considering their social, cultural, and environmental backgrounds that shape their needs and preferences, unlocking a deeper understanding of their diverse contexts.

Explore the emergent nature of contexts for end users, as their circumstances and requirements evolve over time, necessitating adaptive and responsive entrepreneurial solutions, driving innovation to meet their evolving needs.

Explore the contextual dispositional propensity of end users, including their cognitive biases, decision-making processes, and behavioral patterns that influence their interaction with products and services, revealing insights for tailored entrepreneurial strategies.

Explore the interplay between kairos time (opportune moments) and chronos time (sequential time) for end users, understanding how entrepreneurs can effectively address their time-sensitive needs, seizing timely opportunities.

Explore the influence of institutional context on end users' behavior and preferences, such as legal regulations, cultural norms, and societal expectations that shape their choices, guiding entrepreneurial approaches in alignment with the context.

Explore how the artifactual context, including the availability of tools, technologies, and interfaces, impacts end users' experiences and the opportunities entrepreneurs can offer, fostering innovation in user-centric design.

Explore the evolution of contexts for end users, taking into account changing market trends, technological advancements, and social dynamics that require entrepreneurs to adapt their offerings, enabling them to stay relevant in dynamic environments.

Explore the temporal dynamics in end users' decision-making processes, such as their short-term vs long-term orientations and the role of time horizons in shaping their preferences, informing strategic timing in entrepreneurial endeavors.

Explore the contextual factors that influence end users' adoption and usage behaviors, including their social networks, economic conditions, and access to resources, understanding the factors that drive user engagement.

Explore the contextual factors that drive end users' expectations and satisfaction, including their previous experiences, cultural influences, and personal goals, which entrepreneurs must consider when designing products or services, delivering tailored experiences.


Real Needs/Wants/Choices: Human needs(or end user needs/decision maker needs) are commonly used to refer to the drivers of people's actions, the motives behind human behavior. Such needs arise out of specific contexts.  

The fourth default exploratory dimension focuses on real needs, wants, and choices of the end user. Human needs serve as the drivers behind people's actions and the motives that guide their behavior. These needs are influenced by specific contexts, and it is essential for entrepreneurs to understand and grasp the nature of these needs, wants, and choices. The exploration in this dimension involves both cognitive and embodied perspectives, where at the cognitive level the entrepreneur could delve into understanding of models like the the hierarchy of needs, employing frameworks like the candy, vitamin, and painkiller analogy, or exploring heuristics such as Jobs to be Done, etc. Additionally, the exploration extends to the realm of real-world action possibilities, allowing the entrepreneur to cultivate tangible solutions that address the end user's needs in a practical and effective manner.


Examples; 

Explore user needs in context by understanding the diverse factors that shape their preferences and requirements. Analyze the impact of social, cultural, economic, and technological backgrounds on user needs.

Exploring models like Maslow's Hierarchy of Needs(which suggests that individuals have a hierarchical set of needs ranging from physiological needs  to self-actualization needs  AND also models like Tony Robbins' model of human needs(which includes Certainty/Comfort, Uncertainty/Variety, Significance, Love & Connection, Growth, and Contribution) to cultivate diverse ways to understand the underlying drivers behind human behavior and decision-making.

Explore the intricacies of user needs in context by examining the diverse array of factors that shape their preferences and requirements.

Explore the impact of social, cultural, economic, and technological backgrounds on user needs, gaining a deeper understanding of their dynamics and variations.

Explore models like Maslow's Hierarchy of Needs and Tony Robbins' model of human needs to uncover valuable insights into the underlying drivers behind human behavior and decision-making. By delving into these models, cultivate diverse ways to comprehend the complex interplay between different levels of needs and their influence on consumer choices.

Explore the emergence of needs based on societal, technological, and cultural changes, which create new opportunities for entrepreneurs to meet evolving needs and desires.

Explore how needs manifest as wants and choices within real-world contexts, allowing individuals to express their preferences and make decisions based on perceived needs and available options.

Explore the influence of context on needs, wants, and choices, as different contexts (e.g., cultural, social, personal) shape individuals' interpretations of their needs and the choices they make to satisfy them.

Explore scientific theories on needs, wants, choices, and consumer behavior, such as the Expectancy-Value Theory, Self-Determination Theory, or Theory of Planned Behavior, providing frameworks to understand and predict consumer decision-making.

Explore the role of cognitive biases and heuristics in shaping individuals' needs, wants, and choices, such as the anchoring effect, availability heuristic, or framing effect, which influence decision-making processes.

Explore the impact of advertising and marketing strategies on consumer needs, wants, and choices, as companies strategically influence consumer perceptions and desires through branding, messaging, and persuasive techniques.

Explore the influence of social and cultural norms on needs, wants, and choices, as individuals' desires and preferences are shaped by societal expectations, group dynamics, and cultural values.

Explore the relationship between needs, wants, choices, and consumer well-being, exploring theories like the Satisfaction-Needs Fulfillment Model or the Consumer Well-Being Index to understand how meeting needs and making satisfying choices contribute to overall happiness and life satisfaction.


Organization to deliver for Needs ( or Startup/ Venture/ Product/ Idea): The fundamental function of an entrepreneurial organization is to cater to human needs. The organization here may be the entrepreneur, a product, or a startup.


The fifth key exploratory dimension pertains to the startup effort or the organization itself, which is designed to fulfill user needs. One of the primary functions of an entrepreneurial organization is to address the needs of individuals. The organization dimension encompasses various elements, including the entrepreneur, a product, or a startup. It is crucial to recognize that many aspects of entrepreneurship discourse, as well as entrepreneurial models and methods, contribute to the process of building the organization or startup and meeting the needs and requirements of the end user. Tools like the Business Model Canvas or the Hook Behavioral Approach can be viewed as components within these two exploratory dimensions.


Examples.

Explore dimensions discussed by the Business Model Canvas like customer segments, value proposition, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, cost structure, and key metrics.


Explore your entrepreneurial venture using various models like Business Planning, Contingency planning, Discovery-driven planning, Probe-and-Learn approach, Lean startup approach, Theory Based View, Disciplined Entrepreneurship, Design thinking, Design Cognition, Effectual entrepreneurship, Entrepreneurial bricolage, User Entrepreneurship, Copy Cat Model, and Government or Authority Sanctioned, Structure driven.

Explore the role of the entrepreneur itself as a key driving force behind the organization, responsible for identifying opportunities, making strategic decisions, and leading the venture towards meeting user needs.

Exploring and Studying the application of models like Hook Behavioral Approach, which focuses on understanding user habits and designing products that create repeated engagement and satisfy user needs.

Explore the organizational culture and values that foster a customer-centric approach, aligning the entire team towards meeting user needs and delivering exceptional user experiences.

Explore for the influence of external factors, such as market conditions, competitive landscape, and regulatory environment, on the organization's ability to adapt, innovate, and effectively meet user needs in a changing business landscape.

Networks and Ecosystem disposition: This represents the direct connections and embeddedness of the organization(entrepreneur, a product, or a startup) in an ecosystem or bigger environment. More specifically we can think of it as the disposition of the organization in the ecosystem. E.g. dimensions include Network effect, Platform model, Network of Partners, Network of Customers, etc.

The sixth key entrepreneurial exploratory dimension focuses on network and ecosystem disposition. This dimension refers to the direct connections or integration of the organization, be it an entrepreneur, product, or startup, within an ecosystem or larger environment. Specifically, it represents the position and relationship of the organization within the ecosystem. Examples of concepts that are worth exploring cognitively in this dimension include network effects, platform thinking, platform models, networks of partners and customers, and more. It involves examining the organization's direct connections and integration within the ecosystem. This exploration may also involve studying supply-side and demand-side path dependencies and related models.


Examples

Explore the potential network effects that can be harnessed to amplify the impact and value of the organization's offerings.

Explore different platform models and assess their suitability for the organization's ecosystem disposition.

Explore strategies for building and nurturing a robust network of strategic partners to enhance the organization's reach and capabilities.

Explore the dynamics of the network of customers and identify opportunities to deepen engagement and foster customer loyalty.

Explore the role of network intermediaries and assess their potential to facilitate valuable connections and collaborations within the ecosystem.

Explore the regulatory and legal frameworks within the ecosystem to ensure compliance and identify potential opportunities or challenges.

Explore emerging technologies and their potential impact on the organization's network and ecosystem disposition.

Explore the competitive landscape within the ecosystem and identify strategies to differentiate and position the organization effectively.

Explore innovative approaches to community building and user engagement to foster network effects and enhance the organization's ecosystem disposition.

Explore monetization models that leverage network effects, such as revenue sharing or platform fees, to drive sustainable growth and profitability.




Atmosphere: This includes the general features of the entrepreneurship atmosphere or environment. For e.g. those represented by models like 5 forces, PESTLE, etc.

The seventh exploratory dimension focuses on atmosphere as a whole. This dimension encompasses the general features of the entrepreneurship atmosphere or environment, which can include various aspects represented by models like PESTLE and others, but not limited to them. It thus includes exploratory dimensions related to political, economic, socio-cultural, technological, legal, and environmental systems and affordances.

It is worth noting that at the cognitive level, the overlap between the sixth and seventh dimensions may involve models like Porter's Five Forces or BCG metrics. Thus, these dimensions can be explored in the overlapping areas between the sixth and seventh dimensions.

Examples

Explore the political landscape and assess its impact on entrepreneurial activities and opportunities.

Explore the economic factors that influence the entrepreneurship atmosphere, such as market conditions, fiscal policies, and economic trends.

Explore the socio-cultural dimensions of the entrepreneurship atmosphere and how cultural norms, values, and attitudes shape entrepreneurial behavior.

Explore the technological advancements and disruptions that shape the entrepreneurial landscape and create new opportunities or challenges.

Explore the legal frameworks and regulations that entrepreneurs must navigate and comply with in their entrepreneurial endeavors.

Explore the environmental factors and sustainability considerations that impact the entrepreneurship atmosphere, such as climate change, resource availability, and environmental regulations.

Explore the affordances provided by the entrepreneurship atmosphere, such as access to funding, mentorship, networking opportunities, and supportive ecosystems.

Explore the interplay between different dimensions of the entrepreneurship atmosphere and identify potential synergies or tensions.

Explore the impact of global trends and events on the entrepreneurship atmosphere, such as globalization, geopolitical shifts, or major industry disruptions.

Explore the role of innovation ecosystems, incubators, and accelerators in shaping the entrepreneurship atmosphere and supporting entrepreneurial ventures.


Unconventional Forces: This includes things and forces that are unconventional, unknown or unseen, etc. Corona Virus, New exponential Technologies, Its combinations, etc. 


The eighth and final default exploratory dimension is unconventional forces. This dimension encompasses things and forces that are unconventional, unknown, and unseen. It includes possibilities and threats that may arise in the future but are not yet in place. Examples of such forces include events like a Corona-type virus outbreak, emerging exponential technologies, the implications of artificial intelligence, and the direct and indirect impacts of innovative applications like ChatGPT. Additionally, it encompasses the potential effects of various combinatorial innovations.

The key difference between the seventh and eighth exploratory dimensions lies in the nature of the factors they encompass. The seventh dimension, i.e. atmosphere, focuses on the general features and characteristics of the entrepreneurial atmosphere. It includes elements such as political, economic, socio-cultural, technological, legal, and environmental systems and their corresponding affordances. This dimension explores the broader contextual factors that influence entrepreneurship, providing insights into the external environment in which entrepreneurs operate. It involves understanding industry dynamics, market conditions, regulatory frameworks, and socio-cultural trends, among other factors.

On the other hand, the eighth dimension, unconventional forces, pertains to factors that are unconventional, unknown, and often unforeseen. It involves exploring possibilities and threats that may emerge from unexpected events, disruptive technologies, or combinatorial innovations. This dimension acknowledges that entrepreneurship is not solely shaped by predictable patterns or established norms. It highlights the need for entrepreneurs to remain adaptable and responsive to emerging trends and disruptive forces that can have a significant impact on their ventures. Examples of unconventional forces include unforeseen market disruptions, the emergence of new technologies, and the convergence of various innovations.

Examples;

Explore the potential impact of language AI technologies like ChatGPT on transforming customer service and communication in entrepreneurial ventures.

Explore the possible disruptive effects of generative AI technologies on creative industries and content creation in the entrepreneurship atmosphere.

Explore the potential unforeseeable impacts of advanced technologies like AI on job markets and the workforce dynamics, shaping the future of entrepreneurship.

Explore the potential implications of cybersecurity breaches and data privacy concerns on the entrepreneurship atmosphere.

Explore the potential disruptive effects of blockchain technology and cryptocurrencies on various industries and markets.

Explore the potential impact of radical shifts in consumer behavior and preferences on entrepreneurial ventures.

Explore the possible consequences of unexpected changes in government policies and regulations for entrepreneurial activities.

Explore the potential opportunities and challenges presented by the emergence of new business models, such as the sharing economy.

Explore the possible implications of climate change on industries, markets, and entrepreneurial opportunities.

Explore the potential transformative power of breakthroughs in renewable energy technologies on the entrepreneurship atmosphere.

Explore the possible influence of socio-political movements on consumer sentiment and entrepreneurial decision-making.

Explore the potential repercussions of global economic recessions and financial crises on entrepreneurial ventures.

Explore the possible implications of advances in quantum computing for different sectors and entrepreneurial innovation.

Explore the possible transformative effects of breakthroughs in medical science and healthcare innovations on entrepreneurship.

Explore the potential impact of escalating geopolitical conflicts and trade wars on the entrepreneurship atmosphere.

Explore the possible disruptive effects of automation and robotics on workforce dynamics and entrepreneurial strategies.

Explore the potential economic consequences of natural disasters and their aftermath on local and global economies.

Explore the possible opportunities and challenges presented by rapid advancements in virtual and augmented reality technologies for entrepreneurial ventures.

Explore the potential unexpected shifts in cultural norms and values and their implications for entrepreneurship.

Explore the possible implications of the rise of decentralized finance (DeFi) for traditional banking and financial entrepreneurship.

Explore the potential opportunities and challenges presented by the emergence of alternative forms of transportation, such as autonomous vehicles and drones.

Explore the possible implications of unexpected societal shifts, such as changing demographics or social movements, for entrepreneurial endeavors.

Explore the potential unforeseen consequences of global pandemics beyond the health-related impacts on entrepreneurship.

That's the end of the introduction to the context-sensitive constraints. We've learned how context-sensitive exploratory dimensions enables entrepreneurial exploration, provides affordances, and enabling actionability. It is crucial to keep in mind that the ultimate goal of exploration is entrepreneurial success, achieved through the provision of affordances that enable individuals to effectively engage with the world.

Further, a distinction between cognitive exploration alone, and enactive exploration for affordances was made here to clarify the fact that focusing solely on internal cognitive processes or learning is not the ultimate objective. Rather, the aim is to enable and provide affordances for entrepreneurs to create successful ventures.

Furthermore, it is essential to acknowledge the adaptability and malleability of these constraints. Entrepreneurs, along with other ecosystem actors like mentors, have the flexibility to incorporate or modify these constraints according to specific requirements. Additionally, it is important to recognize that each entrepreneur possesses a unique disposition, which may result in distinct exploratory processes, actions, and available affordances. For instance, a tech entrepreneur and a shipping industry entrepreneur would have different dimensions of affordances and opportunities for exploration. This distinction should be clearly understood and kept in mind. Similarly, if individuals such as Jeff Bezos and Elon Musk were to utilize this framework, as different entrepreneurs, they would have their own unique sets of affordances and exploratory dimensions.

 Conclusion  

That concludes the constraints part of ESO-Loop framework. It consists of eight default context-sensitive constraints and six default context-free constraints. However, in the Praxis part, I added two more context-free constraints: enskillment and enactive sense-making. This brings the total to eight context-sensitive exploratory dimensions and eight context-free constraints, balancing both sides.

With the end of the introduction to constraints, it marks the end of the introduction to the three core parts of the ESO-Loop framework. Firstly, I introduced the worldview part, which entails a complex ecological worldview. Secondly, the primary keystone constraint of effectual self-organization was introduced. It serves primarily as a reminder that you are already a self-organizing system and prompts you to act accordingly, employing four dynamical heuristics: understanding your current dispositional propensity, acting in the adjacent possible, make use of the co-evolutionary potential of the present(other systems, people and things.) and embracing emergent and evolving reality. Lastly, I introduced the third key component, which consists of the contact-sensitive and context-free constraints. These constraints are meant to be enablers for entrepreneurs. The context-sensitive exploratory dimensions provides ecologically grounded scaffolds that enable exploration for affordances, while the context-free constraints establish certain default functional enabling constraints that are generally applicable to complex systems. Together, these constraints create a frame or scaffold that supports entrepreneurial self-organization.


 Acronym "ASTRONAUT"(optional)


Here I am introducing the acronym "ASTRONAUT" to represent the previously developed entrepreneurial constraints dimensions.

The acronym "ASTRONAUT" represents; 

A. Agent/ The end user/Decision Maker 
S. Socio-Material Dimension
T. Temporal and Contextual dimension
R. Real Needs/Wants/Choices
O. Organization( or Startup/ Venture/ Product)
N. Networks and Ecosystem disposition
A. Atmosphere
U. Unconventional Forces
T. Tattva (Context-Free Constraints)
Tattva represents the Context-free constraints of the framework. Tattva is a Sanskrit word meaning 'thatness', 'principle', 'reality' or 'truth'.  
          Disposition 1: Agency
          Disposition 2: Diversity
          Disposition 3: Affordances
          Disposition 4: Adaptiveness
          Disposition 5: Evolvability
          Disposition 6: A permanent work in progress.




ASTRONAUT as a term representing the metaphor of Overview Effect experienced by astronauts

The acronym ASTRONAUT here was inspired by the “Overview Effect", a concept developed by Frank White(1998), indicating the ability to see the big picture like an Astronaut. “Overview Effect" is a shift in awareness that happens to astronauts while they are in space, often while viewing the Earth. This include perceiving earth as whole, interconnected, organic, and with no borders or boundaries. It is also the aim of the framework to create such an awareness shift, to make it easier for users to transparently see and navigate the big picture(The Forest), even if they has to attend to the contextual demands(The Tree). 

I would like to mention that you can use the acronym "Astronaut" if you like, as it was part of the framework's earlier design. However, it is not necessary to use it. I am introducing this because I have an emotional attachment to it. This is because I dedicated significant time to designing it in the first version of this framework(call it endowment effect), which was developed two to three years ago.

Using the acronym can help you remember the nine dimensions of entrepreneurial constraints, including both Context-Sensitive and Context-Free aspects. It might serve as a visual aid to understand how constraints impact entrepreneurial exploration.

Just remember that the acronym is part of the scaffold and not a permanent structure in the real world of entrepreneurship.

With that, I conclude. 

Part 3


Praxis

In part 2, I have introduced three core components of the Eso-Loop Framework. In this part I propose a praxis model which includes 4 instruments employed simultaneously. They are; 

1. Effectual self-organization.
2. Context-Sensitive Constraints and Context-Free Constraints
3. Enskilment
4. Meta-Enactive Sensemaking 


1. Effectual self-organization: This suggests that you are already a self-organizing systems so deliberately act like a self-organizing system using primary constraints(Check Image 5.1)

2. Context-Sensitive Constraints and Context-Free Constraints: While being a self-organizing system, simultaneously the entrepreneur must explore the context sensitive exploratory dimensions for various affordances. While being a self-organizing system, who explores the state space using the scaffold of context sensitive exploratory dimensions, entrepreneur is constrained simultaneously by context free enabling constraints like  Agency, Diversity,  Affordances, Adaptiveness, Evolvability, A permanent work in progress. 

3. Enskilment(optional): As we already talked about exploration, Enskillment is an ecological-anthropological idea used or preferred in this framework to enable ecologically grounded exploration; to explore, learn and navigate the entrepreneurial environment. The implementation of the enskillment approach involves four major themes: taskscape, wayfinding, guided attention, and storytelling. This is both an exploratory learning approach and also a pedagogical approach with real implication for entrepreneurship education. 

* This is a preferred instrument that can be used to enable exploration that we discussed in Part 2(lll)
*This connects the entrepreneurial learning and exploration to Gibsonian Ecological anthropology applied in naturalistic learning communities like Tribes, Fishermen, Traditional professions, Sports, etc,(Ingold, 2000; Pálsson, 1994; Prins and Wattchow, 2020; Woods et al, 2020; Woods et al, 2021). 


4. Meta-Enactive Sensemaking(optional): This is influenced by the enactive approach, combines sense-making and actionability in the entrepreneurial context. The enactive view proposes that living beings are inherently sense-making beings, actively constructing meaning and significance through their engagements with the environment. Sensemaking refers to the behavior and conduct of the organism in response to the environmental significance and valence. It is an expression of the organism's autonomy, transforming the world into a meaningful environment through interactive and relational processes. Enactive sense-making in entrepreneurship suggests that entrepreneurs understand and interpret their surroundings in order to take action within the entrepreneurial environment or taskscape. Meta-Enactive Sense-making emphasizes a continues effort to sense make at a meta level so that other factors like entrepreneurs own life, existence, family, purpose, etc, can also be taken into account.

* This can be seen as a Meta level manifestation of understanding or sense-making about the current dispositional propensities which is part of the key dynamics of of effectual self-organization that we discussed in Part 2(ll).
* Through Meta-Enactive Sense-making, I hope to connect entrepreneurial dynamics to the growing field of Radically embodied Non-representational cognitive science(Varela, 1984; Varela et al. 1991; Varela et al., 1992; Thompson, 2011; Thompson and Stapleton, De Jaegher and Di Paolo, 2007; De Jaegher). Check The link(Article by Shaun Gallagher for clarity: Interpretations of embodied cognition

Principle: You are already a self-organizing system.

Meta Heuristics or Prime Primary constraint: So Act like self-organizing systems

Primary constraints

1. Understand your current dispositional propensity(Disposition 1)
2. Act in the adjacent possible
3. Make use of the co-evolutionary potential of the present
4. Accept and leverage the emergent and evolving dispositional propensity (Disposition 2).
5. The propensity of emergent and evolving dispositional reality will become current dispositional propensity i.e. Disposition 1. 

Repeat.
Understand your current disposition
Act in the adjacent possible.
Make use of the co-evolutionary potential of the present.
Leverage and make use of emergent/evolving reality.


Note; The current disposition is the only reality that you have access to act on. All of the four above-discussed dynamical aspects are happening simultaneously. None of them are separate from the other. They are part of an indivisible process. 

Context-Sensitive and Context-Free Constraints 
 
Self-organization alone may not result in great outcomes. Like the parable of the "sower  of seed", only the seeds that fell on the good soil will self-organize to produce great corn. I have talked about using two kinds of constraints to positively bias the self-organization towards a global-optima or a better position. These are Context-sensitive constraints and Context-free constraints. Context-free constraints take a system's components far from equiprobability, and context-sensitive exploratory constraints enable entrepreneurial exploration.

I have listed 8 context-sensitive exploratory dimensions and 6 context-free constraints that are globally applicable to the framework and the users alike. They are;

A. Agent/ The end user/Decision Maker
S. Socio-Material Dimension Of The End User/Decision Maker
T. Temporal and Contextual dimension
R. Real Needs/Wants/Choices 
O. Organization
N. Networks and Ecosystem disposition
A. Atmosphere
U. Unconventional Forces
T. Tattva(Meta-Disposition or Context-Free Constraints)
                1: Agency.
                2: Diversity: Enabling cultivation of diversity(variation).
                3: Affordances, Ecological Validity and 4E.
                4: Adaptiveness.
                5: Evolvability.
                6: A permanent work in progress.





Key points

1.  While being a self-organizing system, the entrepreneur must simultaneously explore the context sensitive exploratory dimensions for various affordances, and while exploring the state space using these scaffolds of context sensitive exploratory constraints, entrepreneur is constrained simultaneously by context free enabling constraints.

2. These constraints frame further enables the entrepreneur to view dysfunctional context free constraints that can negatively impact functional side of context sensitive constraints. Example; the dysfunctional and blind following of lean startup can be a context free constraint that can decouple the entrepreneur from environmental dynamics. Limiting beliefs can be another one.

3. Further, these 8 context-sensitive constraint dimensions and 6 context-free constraints should be viewed as a scaffold which can be changed, updated and even eliminated after relational coherence is achieved. In scaffolding literature the gradual taking away or reducing of scaffolding is called fading. 


The key agenda for the entrepreneur here is continues exploration and learning for affordaces. Using the scaffold of default context-sensitive exploratory constraints and context free constraints entrepreneur will explore the environment to learn the various features of the landscape, including various affordances available for the entrepreneur to act.  Enskillment is an ecological-anthropological idea preferred in this framework to enable ecologically grounded exploration; to explore, learn and navigate the entrepreneurial environment. Since it is an ecological-anthropological way of learning, this approach by default has a propensity to promote ecological validity and development of affordances.

In an enskilment perspective learning is inseparable from doing and place. It acknowledges the complex ecological view of entangled and relational view of real world learning contexts. According to Ingold (2000), enskilment is "understanding in practice”. . . in which learning is inseparable from doing, and in which both are embedded in the context of a practical engagement in the world’ (Ingold, 2000, p. 416). To be enskiled thus is to be skillful and knowledgeable at a particular thing in a particular setting. It refers to the deep practical know-how that skilled people seem to possess in relation to their occupation and location. In enskilment, the unit of analysis is not the autonomous individual who is separated from the social world by the surface of the body or a natural being who passively internalizes the mental scripts of the cultural environment, but rather the whole person in action, acting within the contexts of that activity" (Pálsson, 1994). Thus, becoming enskiled cannot occur separately, in isolation from context or experience, as it grows in the messiness of the noisy ‘real-world’. This contextualized view of enskilment fits perfectly with the growing call for contextualization in the domain of entrepreneurship education.


Following are they key pointers to implement the enskilemnt approach. 

1. Enskilment includes four major ‘threads’; They are are Taskscape, Wayfinding, Guided attention, Storytelling (Ingold, 2000; Prins and Wattchow, 2020; Woods et al, 2020; Woods et al, 2021). Each component is entangled to support enskilment—the taskscape is viewed as the performance environment of the entrepreneur; The wayfinding is viewed as an actively self-organizing and self-regulating entrepreneur who exploring the environment ; The guided attention refers to the role of the more knowledgeable others like educators, mentors, specialists, customers or any other agents who are more knowledgeable at any aspects of the environment; The storytelling enriches the attunement of the entrepreneur to various features and affordances of the environment.

2.In this conception the entrepreneur is viewed as the wayfinder who explores the taskscape(environment) to learn about various features of the landscape. They actively seek guided attention and place based stories from the more knowledgeable others. The more-knowledgeable others in the entrepreneurship context can be people, organizations or artifacts like devises, internet, books, etc. 

3. In the case of entrepreneurship, taskscape is the unique performance environment in which a particular entrepreneur is embedded. According to Ingold, people become enskilled by dwelling within various places—meaning that learning about a place is inseparable from being in a place. It is by dwelling that people learn to become familiar with a place’s features, which includes the tasks of other inhabitants that “make up the pattern of activity of a community” (Ingold, 2000; p. 325). From this dwelling perspective, a task can be understood as a practical, goal-directed activity undertaken by an individual interacting with an environment. So, then, the taskscape is the “entire ensemble of tasks in their mutual interlocking” (Ingold, 2000; p. 195). The idea of mutual interlocking emphasizes that tasks are never completed in isolation, but only exist in an enmeshment of people, organizations, artifacts, events, etc interacting with each other and a performance environment.
 
3. Wayfinding (Ingold, 2000) is best described as an experiential and narrative way of moving through a landscape as one continues to learn it. It is the process of embarking upon a purposeful, intentional, and self-regulated journey that takes an individual from an intended region in one landscape to another. The dynamics of wayfinding would require the entrepreneur to develop an intimate knowledge of a taskscape’s informational constraints such as socio-material features, climate, socio-cultural norms, rules, and local history. It also requires an understanding of how such things enmesh to shape the entrepreneur's perceptions and actions through an evolving attunement and attentiveness to various opportunities for environmental interaction.  

4. Guided attention positions a more knowledgeable other or others as agents who guide the attention of the wayfinding learner. This is the role played by coaches, educators, mentors, organization, artifacts, etc in the entrepreneurship domain. This approach to education is ‘hands-on, not in the hard sense of instructing, but in the soft sense of guiding—guiding one’s attention toward the direct perception of things that may otherwise be cloaked to them by watching, listening, or feeling (Woods et al, 2021). Guiding an entrepreneur’s attention to informational properties that support performance in the taskscape can take shape in a variety of ways. According to Prins and Wattchow(2020), guiding a performer's attention "relies upon anticipating and knowing what to do and when to do it in any given context and with any learner. This relates not only to the task but also the complex array of what the learner needs at that particular moment in the taskscape. If the pedagogic moment is fulfilled, the learner’s attention is drawn to the right thing—at the right time—in the right place—in the right way"

5. Place-based stories and storytelling (Prins and Wattchow, 2020) in the enskilemnt perspective are not just a representation or an account of what has happened but rather, a continual communication that is also a way humans make sense of their world. With this account Ingold (2000) states: "I don’t believe, however, that their purpose is a representational one. Telling a story is not like weaving a tapestry to cover up the world or, as in an over worn anthropological metaphor, to ‘clothe it with meaning’. Far from dressing up a plain reality with layers of metaphor, or representing it, map like, in the imagination, songs, stories and designs serve to conduct the attention of performers into the world, deeper and deeper, as one proceeds from outward appearances to an ever more poetic involvement"(p. 56). When learning how to wayfind, a person must pay careful attention to the many stories told in situ as well as direct experience that place many times for themselves (Ingold, 2000; Tyrrell, 2006). Stories thus help guide attentiveness, and attentiveness leads to becoming more deeply enskiled in the taskscape.

6. When it comes to entrepreneurship we need to stress the need for a distributed form of enskilemnt. This means in entrepreneurship not a single individual is the wayfinder. There could be multiple agents parallelly exploring the environment. For e.g. the technology co-founder maybe exploring the technology environment more than a sales specialist. Similarly the source of guided attention or educating the attention of the entrepreneur will also be distributed across the taskscape. Thus a person who is familiar with law of the land, a lawyer, will provide guided attention about the legal side of the startup; A person who specializes in design will provide guided attention towards the different potential solutions; A person who knows deeply about the platform business model can provide guided attention to information regarding platformization, etc. Wayfinding entrepreneurs can seek information from multiple agents operating in the same, different, or parallel parts of taskscape. E.g. Multiple customers about the same problem, etc. Further, a single story may be biased or may stress only a few aspects of the features of the taskscape. Thus entrepreneurs need to seek different types of stories from people from diverse taskscapes in order to capture the richness of the land we are exploring (adapted from Prins and Wattchow, 2020). 

7. Constrain to afford; The enskilment approach can be viewed as a wayfinding entrepreneur exploring the world for various affordances, navigating various environmental constraints. The guided attention and storytelling are viewed as informational constraints that can channel the entrepreneur's attention to various affordances available to him/her. The entrepreneur will become progressively attuned and calibrated to various features of the environment, so that even slight changes in the environment can be sensed.

I have discussed the primary constrain of effectual self-organization, context-sensitive and context free constraints, exploration and enskilment. Now I propose a constraint that enables us to make sense of the current emergent disposition at a meta level so that other factors like entrepreneurs own life, existence, family, purpose, etc, can also be taken into account. This can be seen as a Meta level manifestation of understanding or sense-making about the current dispositional propensities which is part of the key dynamics of effectual self-organization that we discussed in Part 2(ll).

Meta-Enactive Sensemaking is influenced by the enactive approach, combines sense-making and actionability in the entrepreneurial context at a meta level. The enactive view proposes that living beings are inherently sense-making beings, actively constructing meaning and significance through their engagements with the environment. Sensemaking refers to the behavior and conduct of the organism in response to the environmental significance and valence. It is an expression of the organism's autonomy, transforming the world into a meaningful environment through interactive and relational processes. 

The enactive view proposes that living beings are sense-making beings; they enact or bring forth significance in their intimate engagements with their environments (Thompson, 2011). According to Varela (1984.), “Order is order, relative to somebody or some being who takes such a stance towards it. In the world of the living, order is indeed inseparable from the ways in which living beings make sense, so that they can be said to have a world”. Sensemaking is thus the behaviour or conduct in relation to environmental significance and valence, which the organism itself  enacts or brings forth on the basis of its autonomy (Thompson and Stapleton, 2009). It can be called the exercise of the interactional and relational side of autonomy of the organism that transforms the world into an environment which signifies an inseparable relation.

The enactive approach to sense-making is not internalist because it allows that the operationally closed networks that realize an autonomous sense-making system can extend beyond the brain, skull, or skin (Thompson and Stapleton, 2009). Thus the enactive sense-making is fundamentally different from the traditional cognitive science perspective that is based on representation. According to the enactive approach, cognition is grounded on the sense-making activity of autonomous agents—beings that actively generate and sustain themselves, and thereby enact or bring forth their own domains of meaning and value (Thompson 2007; Varela et al. 1991). 

In the social realm like entrepreneurship, the interactive coordination of embodied sense-making activities with others will lead us to participate in sense-making of others. This is called participatory sense-making in an enactive perspective (De Jaegher and Di Paolo, 2007). According to De Jaegher (2013), the consequence is that, "when we engage in interaction, not only the participants, but also the interaction process as such modulates the sense-making that takes place. This means that intentions can be truly understood as generated and transformed interactionally". To me this fits perfectly with the complex ecological world view that this framework beholds, and also the enskilment view of taskscape, which includes multiple mutually dependent and interacting tasks. 
 

Following are key pointers;

1. This involves continuously attempting to sense-make about the state-space and the self-organizing system as a whole in its dynamics and taking appropriate action(Meta-Action) or design(Meta-design) to learn, explore, enable possibilities, solve problems or limitations, etc. It asserts the importance of entrepreneurs having a holistic perspective and not just assume that the act of entrepreneurship is separate from other things in life like deep values, health, society, family, relationship, happiness, meaning, etc. This is about the awareness that things in life cannot be understood in isolation. In other-words this is a conscious effort not to see the world as a collection of isolated objects, but as a network of phenomena that are fundamentally interconnected and interdependent.

2. This also involves the ability of entrepreneurs to identify bounded rationality, dysfunctional biases, fixation errors, framing effects, etc. It must enable entrepreneurial exploration towards interdisciplinary intelligence, particularly those which is outside the primary design of this framework. It also must aid in deciding when to use focused convergent thinking(exploitative), diffused divergent thinking(Exploratory), Disruptive re-framing, etc.

3. While sense-making, keep a relational sense of various forces(complexity, evolution, etc.) and elements of diversities like tools, ideas, resources, and all others. Keep the relational sense in close proximity so as to make it accessible, usable and to allow cross breeding of the various units of diversities. This is a kind of relational agency (Edwards, 2005) that sees the world as a network of interacting agencies rather than a collection of independent objects subjected to external forces (Heylighen, 2022). The relational agency of this kind is not a one time thing and require continues ecologically grounded attunement which is cultivated progressively by the entrepreneurial exploration and enskilment. This sense-making agency is also located in the social relationship entrepreneur have with the material world(e.g. places) and material objects(Sillar, 2009).

4. Sensemaking of various instruments(tools, ideas, artifacts, etc) are also important in meta-enactive sensemaking. Note that, elements of diversity will be treated equally as like in a tool box or app platform(except for modularity) as mentioned earlier as Deweyan Instrumentalism. For example tools, methods, rules, heuristics, all get a place. The use and value of ideas depends on the dynamics of emergent use value that evolves and changes all the time. This is against one size fits all approach that forces the agent to use a singe model, tool, or method. This means the entrepreneur never have to settle with one or two preinstalled one-size-fits-all models like Lean-startup or Business plan, but can use all of it depending on the contextual demands. This proposes a shift  from each idea(tool, method, etc.) according to its popularity, cult followers and marketing budget, to each according to its ecological use value" (rhyming a Marxian slogan). The entrepreneur must progressively cultivate relational sense(sense-making) of these artifacts and their uses that emerge in various contexts. 

5. The importance of action is asserted in-order to stress the inseparability of cognition from context and the need or ability to act or design. This is based on insights from ecological (Gibson, 1979); embodied, embedded, enactive (Varela et al., 1992), and extended (Clark and Chalmers, 1998) perspectives of human behavior and decision making. 

6. As reflected elsewhere in this paper, here also the agenda is not to hook the entrepreneur to my version or an idealized version of sense-making, but to explore for best fit for emergent contexts. It is just a soft-guidance, not a hard know it all top-down prescription. 

Part 4


Conclusion

Entrepreneurship is a complex decision domain. It is essential that solutions designed for complex domains like entrepreneurship must consider factors like non-linearity, inter-relatedness, emergent property, etc. In contrast to most of the existing models like lean-startup, business plan, etc., this framework is a contextually adaptive entrepreneurial framework for a complex and emergent world. It balances self-organization, a kind of exploit in the local adjacent possible, and exploration towards the global optima. It then uses the meta-dispositions as context-free constraints that drive the proclivities of the entrepreneur and the framework itself.

In contrast to the existing entrepreneurial thinking space, the following are some of the key advantages of the framework.

Contrary to other models and methods, ESO-Loop is built on insights from complexity science. Everybody may agree that entrepreneurship is a complex domain. But actions don't follow through with this agreement. If one agrees or asserts that a domain is complex, he or she must use complexity as the fundamental frame to view, analyze and understand such a domain. No amount of linear approaches, no amount of lean, or planning will compensate for ignoring the underlying nonlinear dynamics of a complex system like entrepreneurship. ESO-Loop is a complexity-science-informed design solution to aid entrepreneurial actions. This is based on the scientific understanding that all open complex adaptive systems have a tendency to self-organize under various constraints. Entrepreneurship is also a complex adaptive system that self-organize under constraints like informational, environmental, cognitive, resources, etc. None of the existing models directly use complexity as a frame.

Contrary to other models and methods, ESO-Loop stresses the need to harmonize the growing collective human intelligence. This also includes both practitioner and academia developed ideas. ESO-Loop attempts to bring it all accessible and affordable for the entrepreneurial actor at the time of his/her decision making and action. This framework promotes other people's models, ideas, and insights. To use an analogy, most of the existing entrepreneurship advice, and models(prescriptive and descriptive) on entrepreneurship are like apps. This framework is like an app platform like Android or IOS(without the modularity), which enables and even forces constant updation. This means you never have to settle with one or two preinstalled apps(E.g. Lean-startup or Business plan). The framework enables adding of more ideas, heuristics, models like---bricolage, design thinking, user innovation, etc, or build your own models. Once a model or idea is brought inside the framework, its propensities like binarization, exclusionism, polarization, parasitism, etc will no longer work. It will be one among many types of variation the agent can use.

Contrary to other models and methods, ESO-Loop promotes interdisciplinary exploration. Even though real-world problems and their solutions are interdisciplinary in nature, most existing entrepreneurial prescriptive models are the result of disciplinary reductionism. It often comes as rigid processes that lack flexibility. Using these models without understanding laws of entrepreneurial complexity could seriously impair the ability to integrate the wholeness of the human individual, social, material realities. This framework has enabling constraints for exploration without boundaries. It is also inclusive of contradictions and diversities.

Contrary to other models and methods, ESO-Loop makes it easier for you to transparently see and navigate the big picture(The Forest), even if you had to attend to the contextual demands(The Tree). Because the framework is built over insights from complexity science, it stresses the need to start the thinking from a holistic perspective. The world view it beholds is ecological complexity, which is an ecological perspective that does not separate humans--or anything else-from the natural environment. It sees the world not as a collection of isolated objects, but as a network of phenomena that are fundamentally interconnected and interdependent. In real-world stressful decision contexts, we may not be able to view the world from a holistic perspective. Every context is like a black hole, and It demands our cognitive and physical resources. This framework act as an enabler for you to see the big picture simultaneously. It provides the transparency to see hidden forces in action.


Contrary to existing prescriptive models, ESO-Loop is mutable, adaptable, evolvable. Most ideas and models in the entrepreneurship domain are self-aggrandizing and self-perpetuating. This often works against the agent's evolutionary potential because it gives a false sense of comfort and certainty, blinding the agent from ever realizing the true emergent nature of entrepreneurial complexity. This framework embodies a Work In Progress attitude with a total acceptance of its own possible weakness. It can be changed and designed to suit the idiosyncrasy of the user and the complexity of the context. Secondly, the structure of constraints in part 2 (lll) could be(not necessary) seen as a scaffold. A scaffold is a temporary structure used to support the development of a structure. It is not meant to be permanent. Existing prescriptive models and frameworks propose permanent structures that end up being inflexible. These constraints are not designed as a permanent structure but as an impermanent scaffold that enables initial emergence. Further, this framework is an adaptive system. An adaptive system involves designing the elements of a system to find by themselves the solution of the problem. Like this, when the problem changes, the elements are able to dynamically find a new solution(Gershenson, 2007). Existing prescriptive models are ignorant of the possibility of their own mutability.

Contrary to existing prescriptive models, ESO-Loop promotes effective learning and transfer that reflects in the structure of the framework itself. It encapsulates various complexity-friendly functional dispositions into a single framework so that an ecology of ideas in its dynamics will transfer to the users, not just one or two ideas or a process. Further, learning something at one point in time and using it adaptively at a different temporal decision context is extremely challenging. This is because isolated ideas don't sustain without connecting with a meaningful preexisting knowledge or contextual application. Without the existence of meaningful coherence, it is very difficult to transfer our learning to adaptive, unknown, and emergent contexts. ESO-Loop does three things: 1) It gives a permanent position, a meaningful coherent structure in which you can build on, place, and connect newly learned ideas and models 2) It forces constant updation. It enables and encourages you to learn, unlearn, add, remove, design, create, combine, remix, recombine and customize solutions to suit the ever-changing dynamics 3) It will enable and encourage you to design your own emergent solutions for learning and transfer problem rather than attempting to prescribe any one-size-fits-all advice.


Contrary to existing prescriptive models, ESO-Loop has inbuilt elements of Immunity against Cognitive Hijacking, Disciplinary Darkness & Certainty Merchants. Human rationality is bounded. In a complex and uncertain world, we make decisions under the constraints of limited knowledge, resources, and time. This results in the tendency of the human brain to solve problems in the most simple and straightforward way rather than utilizing a more sophisticated and effort-intensive way. This human weakness is often exploited by certainty merchants (Espinosa, 2015) who come up with confident advice and one-size-fits-all models. Further, people with disciplinary/domain commitments become more committed to disciplinary/domain boundaries and methods aligning perfectly with legacy incentive structures over time. I would like to label this as a kind of cognitive hijacking where the agent's evolutionary, learning, the adaptive potential is being compromised because of strong preexisting one-size-fits-all models and other commitments. This framework is designed to create default immunity against Cognitive Hijacking. The meta-dispositions are intended to scan the phase space for potential elements of bounded rationality and fixation errors that can prevent exploration and effective self-organization. 

For the development of the design, I have adopted insights from various existing entrepreneurship research, complexity science, decision science, ecological approaches, learning sciences, etc. I have specifically used insights from successful complexity-based models like the Constraints-led approach in sports, Guided Self-organization, Enactive approach, Cynefin, etc. They provided fundamental templates over which I was able to build and integrate all of these insights into a practical framework.

This design provides several potentially fruitful implications. It sets complexity science itself as a constraint for actors and artifacts in the entrepreneurship ecology(entrepreneurs, stakeholders, mentors, entrepreneurship educators, methods, etc). This constraint suggests that if you say entrepreneurship is a complex domain and if you agree that it needs complexity-friendly approaches, do you think your linear approach(say lean-startup) is going to work? Is it going to make the entrepreneur effectively self-organize in their local adjacent possible? If not what can you do now? If it works, what is the extent to which it works? In this way, the framework will make complexity the default setting. It forces the user to consider factors like non-linearity, inter-relatedness, emergent property, etc.

Even though it provides many functional enabling constraints, It is highly adaptive to the agent and the context. The praxis of the framework starts from the embedded situated context of the entrepreneur. By using the effectual self-organization logic, it grounds the entrepreneur in his or her context. It balances self-organization, a kind of exploit in the local adjacent possible, and exploration towards the global optima, providing opportunities for design. It uses the meta-dispositions as a context-free constraint that sets the dispositional proclivities of the entrepreneur and the framework itself.

Ecological actors can use the framework without losing the uniqueness of what they are doing. This framework is not against any other methods. All other methods have a space in the framework. But, it is against propensities like binarization, exclusionism, polarization, parasitism, etc. Such proclivities will be defused. It will no longer work inside the frame of ESO-Loop. All are treated as units of diversity or instruments. It thus views diversities and contradictions as a way towards building more evolvability.

Last but not the least, as the Disposition 6 suggested, this is a permanent work in progress. It is in perpetual construction (Prigogine, 1996).  

Thanks you.

Part 5

Understand the ubiquitous nature of Self-Organization

Self-organization is a ubiquitous feature in open complex adaptive systems.  This means you are already a self-organizing system. Self-organization involves un-prestatable, adaptive and emergent behaviors that cannot be replicated by top down order or one size fits all prescriptive solutions like lean-startup.

1. Act Like Self-Organizing Systems: Effectual Self-Organization

Follow effectual self-organization, which is the primary constraint of the framework. It starts with a reminder that you are already a self-organizing system, so act like a self-organizing systems, instead of being hijacked by external agents, institutions, and artifacts. 

2. Context sensitive and context free enabling constrains.

Self-Organizing entrepreneur simultaneously must explore the context sensitive exploratory dimensions, while being constrained by default context free enabling constrains.

3. Enskilment(optional)

Enskillment is an ecological-anthropological idea preferred in this framework to enable ecologically grounded exploration; to explore, learn and navigate the entrepreneurial environment.

4.Enactive Sense-Making(optional)

Meta-Enactive Sense-making emphasizes a continues effort to sense make the dispositional propensities at a meta level. Not just limited to immediate things to do.

Part 6


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