40one 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 An Effectual Analysis of Markets and States

41since. 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 openended 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, S. D. Sarasvathy

42including 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.