As I explicate each of the three dimensions below, it will be useful to keep in mind that these can interact and combine. Even though they can occur independently of each other, they are not always mutually exclusive.
2.1 Problem Dimension One: Knightian Uncertainty
Effectuation has been studied relatively well in the context of Knightian uncertainty, a term originating from Frank Knight’s taxonomy of uncertainty in his 1921 thesis, Risk, Uncertainty, and Profit. In lay terms, Knightian uncertainty refers to situations in which the future is not only unknown but also fundamentally unknowable. An iconic example from decision theory can help clarify Knight’s taxonomy. Imagine you are playing a game in which you draw balls from an urn containing 50 green balls and 50 red balls. You will win if you draw a green ball. Although you do not know which ball you will draw, you can still calculate the odds as 50–50 since you know the distribution of balls in the urn. This captures the idea of “risk”—namely, a known set of possibilities but an unknown draw.
Another concept of interest is the notion of “uncertainty” in which you know neither the distribution nor the draw. This would be like an urn containing many different colored balls, but you do not know how many of each color or even the total. The game, however, is the same: You win if you draw a green ball. It is easy to see that this game is much more difficult to play than the game of risk. Many organizational, economic, and socio-political problems are conceptualized as problems of uncertainty that can only be tackled through sophisticated techniques for prediction ranging from systematic hypothesis-testing to scenario analysis and other approaches based on simulation and big data.
In both the above thought experiments, we knew something about the urn’s contents. In situations in which Knightian uncertainty is involved, even this information is unavailable. The urn may contain things that defy classification or even recognition, making it impossible to classify them into a distribution on which predictive techniques can work. It is as though the urn could contain umbrellas, snakes, bars of gold, disease, anything and everything that can and may exist. You get something different every time you draw—not just balls. In other words, Knightian uncertainty refers to the impossibility of imagining, let alone specifying a distribution, on the basis of which you can make predictions. In dealing with Knightian uncertainty, you need to come up with techniques that either minimize or completely avoid prediction altogether. The lessons that expert entrepreneurs learn consist in nonpredictive techniques that we call effectuation or effectual logic, contrasted with predictive or causal logic.
Effectuators develop an awareness of and even a preference for Knightian uncertainty. Hence, in addition to cocreating futures with self-selected stakeholders, effectual approaches emphasize possible errors as decision criteria rather than predicted upsides (e.g., the affordable loss principle). This is a powerful tool to help bring downsides within one’s control, without constraining upsides. Therefore, one starting point for an effectual analysis of markets and states is to ask: In any given governance choice, what are we willing to live with if we get it wrong?
2.2 Problem Dimension Two: Goal Ambiguity
The literature on effectuation also highlights problems of goal ambiguity and isotropy, both of which are also relevant to an analysis of markets and states, especially in terms of their roles in innovation. At the level of analysis of individuals, goal ambiguity refers either to not knowing what one’s preferences are or not knowing how to translate high-level goals into actionable subgoals. The latter applies at the levels of organizations and institutions as well. Especially when faced with complex problems such as climate change, goal-setting is fraught with ambiguities. For example, it is not clear if certain species are more crucial for conservation, bees for example, and therefore need to be protected more than others, say mosquitoes. What about frogs? Or crickets? The foundation species literature argues that there are species that are foundational, but there is little agreement on how to decide which ones at any given point in time. Also consider the famous Julian Simon wager against Paul Ehrlich on peak oil and futures in commodity prices (Simon, 1982). In 1980, Ehrlich chose five metals he predicted would increase in scarcity within 10 years and hence in price, but Simon won the bet in the other direction. Prices of most commodities, including oil, have not hit peak 30 years since. Even with increasing consensus on the reality of climate change, goal ambiguities continue to plague this problem. Effectual action is surely called for here.
Organizations as Fabricators of Artificial Predictability and Goal Clarity. Interestingly, organizations (including states) are a way for us to reduce Knightian uncertainty and goal ambiguity. Hence their ubiquity in human affairs, as argued by Joseph Schumpeter, Herbert Simon, and others. Unlike markets that enable open-ended interactions, organizations are for the most part hierarchical in structure (Williamson, 1973). Note that in the ensuing discussion, I will use the word organization to include a variety of hierarchical structures ranging from familiar for-profit firms to normative institutions such as regulations and customs. At the extreme end of this spectrum are states, which are organizations endowed with the right to use coercive force.
By constraining what members can and cannot do through contractual obligations, organizations create artificial predictability amidst pervasive uncertainty. Traffic lights offer a simple example. By simply agreeing to stop when traffic lights turn red, we create predictability and hence safety for both pedestrians and drivers. However, simple agreement is not sufficient. Some amount of effective enforcement against transgressors is also necessary. Particular combinations of voluntary compliance and enforcement differ across different socio-political contexts (just compare busy streets in Mumbai with those in Frankfurt). In the case of designing traffic systems, contextual elements involve different types and speeds of vehicles, numbers of pedestrians, widths and types of streets, as well as historical and cultural antecedents to behavior. When designed well, organization can provide reasonable predictability in a wide variety of contexts.
On the face of it, it seems easier to see how market interactions (such as interpersonal negotiations) can be more efficacious in the case of organizations such as small businesses than in the case of larger societal institutions such as traffic lights. It seems absurd to think about negotiating with traffic lights. Yet there is more of a role for market interactions in the case of traffic lights, just as, on the flip side, there can be enforcement within organizations, even completely voluntary organizations. For example, communities do negotiate and vote on a variety of institutions around traffic lights, including speed limits on roads, placement of lights, and widths and numbers of lanes. It is unfamiliar, however, to consider any of these as market activities. In such cases, the missing link is provided by institutional entrepreneurs, people acting effectually to build these institutions. As we develop the ensuing analysis of markets and states from an effectual perspective, we will use a more general view of entrepreneurship than a narrow focus on the building of for-profit firms. This generalization is common to the works of noted economists such as Williamson, Ostrom, and North, as well as most entrepreneurship scholars today.
Once formed and functioning well, organizations can also resolve goal ambiguity at the individual level by creating and enforcing norms around particular missions, often defined in behavioral, technological, and strategic terms. Jim March’s “garbage can” model shows how organizations do these through simple mechanisms such as deadlines (Cohen, March, and Olsen, 1972). In market-based societies, individuals can select in and out of particular organizations for a variety of reasons, including alignment with the stated and actual missions embodied in norms practiced within organizations. Whereas individuals with high levels of goal ambiguity might still vacillate in their choices, most will strive to align themselves with the goals of organizations they sign on to.
Similarly, organizations strive to both select in individuals with some degree of mission coherence and then invest in processes and incentives that seek to realign individual and organizational goals as needed and feasible over time. To the extent that they succeed at this function, organizations also create oases of predictability and goal clarity, both for individuals and communities, at least for reasonable periods of time, so that reasonably positive outcomes for both can be fabricated.
This method of reducing uncertainty already involves a move from goal ambiguity to goal alignment. Returning to the example of traffic lights, trade-offs between speed and safety can be efficiently managed by solving the problem of behavioral (human beings), contextual (types of streets), and technological unpredictability (types of vehicles), through a combination of voluntary commitment and enforcement of compliance with that commitment. Voluntary commitments, for example, a community’s determination of an acceptable speed limit, resolve goal ambiguity. Once the limit is determined, anyone ambiguous about it still has to comply with the limit. Or exit. Move to Montana or Manila.
In other words, one way to remove goal ambiguity is through organizations’ efforts to align the goals of its members, through voluntary commitments during formation, and thereafter through incentives and enforcement. Furthermore, multiple goals embodying differing tastes, preferences, and values can be leveraged and achieved through organizations aligned with these. For unaligned individuals, the choice then becomes unwilling compliance or exit. This works in the case of organizations and markets. But it can be problematic or even impossible in the case of states.
2.3 Problem Dimension Three: Isotropy
The third dimension of the effectual problem space, isotropy, differs from Knightian uncertainty and goal ambiguity. Isotropy refers to the problem of relevant vs. irrelevant information. In contexts of reasonable predictability, it is relatively easy to evaluate the relevance of any given piece of information. But contexts of innovation are contexts of unpredictability. And in these, even when goals are clear, the isotropy problem is rampant. In fact, the more innovation called for, the more this problem might become salient to all kinds of endeavors, including the enterprise of policymaking. Decisions and actions for the fabrication of organizations involve isotropy. Even more so the making of markets and the shaping of states. And most importantly, isotropy pervades choices between markets and hierarchies. In order to clarify the concept of isotropy a bit more extensively, let us consider a standard problem that budding entrepreneurs face.
Suppose you have come up with the idea for a green widget. Most standard textbooks and courses in entrepreneurship would suggest you go talk to potential customers and ask for their input in making marketing and production decisions. This advice is based on conventional wisdom that makes a series of assumptions, each of which is usually not only unjustified, but has the potential to misguide entrepreneurial action:
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There exists a market for the product.
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You know who your potential customers are likely to be.
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Your potential customers know what they want.
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They will actually do what they say—buy what they say they will buy, not buy things they say they will not buy, etc. Note that these two are not the same, nor are they symmetrical.
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You have the time and resources to talk to enough potential customers to figure out what they want and do not want.
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Your potential customers will not want completely contradictory features.
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There are no customers you do not know about.
You can combine the above into the most important and fatal assumption of all: Markets are out there, in an objective sense, and they can give you reliable, actionable answers. This implies that markets are not themselves artifacts of what you and others do. In other words, markets are mostly exogenous to human action, not endogenously created through it.
Not only entrepreneurs, but large established companies who can afford the best market research techniques and talents available, routinely make two bad bets based on these assumptions:
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They make decisions assuming markets are more predictable than they are.
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They miss out on making markets that could be made without resorting to prediction.
Effectual entrepreneurs choose to make the opposite set of bets, choosing to make the opposite error on predictability. They treat markets as artifacts and approach them as less predictable than they might be. Let us now consider how that enables them to overcome the isotropy problem.
How the Crazy Quilt Principle Helps Overcome Isotropy. If you approach markets as exogenous, but predictable, and you ask for information, advice, and feedback from potential customers, one of the interesting problems that arises is not that you do not get enough information, but that you get too much information. Too much in the sense that the information confuses, rather than clarifies, your understanding of the situation. If you now take seriously the idea that there may be other customer segments out there that you may not have predicted and widen the circle for your research, the isotropy problem of too much and too varied information without clear criteria to distinguish relevance only increases in quantity and intensity. No brainer as it may be, seeking more information does not usually reduce isotropy.
The only way to overcome isotropy is to ask for actual commitments, not merely information, advice, or feedback. In other words, market mechanisms such as deal terms, real investments of financial and nonfinancial resources, preselling, etc., are examples of ways to overcome isotropy. When someone says they will or will not buy something at a price, that is predictive information of little or no value to effectual entrepreneurs. But if someone underwrites the next step in the venture, by actually producing a prototype for you, or by introducing you to someone who can do a trial run without charging you up front, or signs a preorder that allows you to set up favorable terms with vendors, etc., then the next step is not a speculative bet. Instead it is an actionable task you can accomplish for affordable loss.
By stitching together a series of such actual commitments (See Fig. 1 for a graphic illustration of this process), effectuators end up cocreating a market that neither entrepreneurs nor anyone else might have predicted. Hence markets themselves become an artifact of the effectual process. In this sense, as Schumpeter argued, entrepreneurship is more about cocreating new markets than innovative products and ventures within extant markets.
Relevant information in the effectual process therefore gets its relevance from individuals and/or organizational actors who, for idiosyncratic reasons of their own, enable you to accomplish key venture-building actions for affordable loss. These individuals or organizational actors self-select into the process—an act characteristic of markets, not states. Yet the deal terms of each effectual commitment entail elements of governance, constraints on future actions, and future interactions with future self-selected stakeholders that become the building blocks of the hierarchy that comes to be as well. In other words, the effectual process offers the quintessential microprocess of mixing and matching market-like and state-like elements that add up to actual new markets and new organizations that come to populate economies and societies. Ergo, it is worthwhile to take a bottom-up view of markets and states through an effectual lens.