Timo Ehrig

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Decisions in Complex Environments and Imagination

Neoclassical economics and its intellectual offspring are arguably lacking an understanding of the impact of human creativity on business strategies. Theories in this tradition -among them industrial organization and strategic factor market theory- do not include a notion of 'superior cognition', as Bayesian rationality is set as a seemingly stable benchmark of how firms should make decisions. In my view, however, these theories are not taking a close enough look at the decision problems that strategists face.
In the organization and strategy literature, the problem of the complexity of strategic decision-making has often been addressed. For instance, scholars have demonstrated that the complexity of business strategies can prevent entrants from successfully imitating incumbents, as the complexities of learning are too high. However, I see a tendency that in some strategy and organization papers causal explanations (like: particular decision rules in complex environments are superior to others, as they exploit the specific statistical properties of complex environments) are confused with quantitative analogies (like: strategic decision-making is like finding peaks on NK landscapes).
In my own work, I try to understand complexity in decision making, and possibilities to make superior decisions ('superior' being defined in a business context, meaning 'decisions that lead to competitive advantages').
In one project ('The Difference Between Learning and Imagiation') I try to point out that there is an urgent need for a theory of strategic decision making, as the existing decision theory paradigms ignore a key property of strategic decision making. Most theories of decision making or belief update focus on situations where humans form their expectations, or update their beliefs, on the basis of facts. But opportunities like Google in perspective of 2002 or Amazon in perspective of 1998 are characterized by the condition that one cannot learn from facts. The properties of the business of Amazon or Google became known much later, when the opportunity was no longer existing, as it had already been acted upon. And even if facts become known over time, the prior beliefs of a decision-maker will almost completely determine how they are evaluted (here making the assumption that the decision maker is Bayesian rational). In other words, the assumption of Bayesian rationality alone does not tell us why some decision-makers are better than others. What is missing is a theory of prior belief generation. A decision maker who is superior in the generation of a good hypothesis how a new business enviornment may work is likely to be superior to a decision-maker who is lacking this ability (given the assumption that both decision-makers are fully Bayesian rational).
But then, how are superior prior beliefs generated? One corresponding question is why some decision makers are superiorly able to imagine the future. In another project ('The Formation of Expectations for Novel Opportunities', with L. Kauffman, under review) I am exploring how humans can form forward-looking beliefs superiorly. If one forms beliefs about a new business environment, one can easily run into contradictions. For instance, an investor may have believed (in perspective of 1998) that Amazon will be the company that dominates physical booksellers in 2010, because Amazon is very good in creating trust for its web portal. But the same investor may also have believed that competitors have a lower cost of goods sold (which was true in 1998), and therefore competitors will dominate Amazon. This creates a contradiction in the belief of the investor. The paradigm of Bayesian rationality does not instruct us what to do with such contradictions. I am exploring what may lead to be superior in such tasks.
To put this research into perspective, many decision-makers are certainly using simple heuristics when they act upon a new opportunity. In the simplest case, they may just use a social heuristic and do what their peers are doing. Herding is a well-known phenomenon. But I argue that we need to go beyond studying the social dynamics of belief formation for novel opportunities. Some decision-makers may deliberately ignore the group consensus around them. They may benefit from thinking alone, if they have abilities to form forward-looking beliefs better than their peers. I argue that there are systematic principles for such deliberative cognition, which can explain why some decision makers are better than others in their forward-looking belief formation. To work out these systematic principles is the goal of my work.