HYPOTHESIS AND THEORY ARTICLE published: 12 July 2012 doi: 10.3389/fnins.2012.00105 Cognitive processes in decisions under risk are not the same as in decisions under uncertainty Kirsten G. Volz 1* and Gerd Gigerenzer 2 1 Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany 2 Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany Edited by: Gabriel José Corrêa Mograbi, Federal University of Mato Grosso, Brazil Reviewed by: Paul Cisek, Université de Montréal, Canada Christopher MacDonald, Boston University, USA *Correspondence: Kirsten G. Volz, Werner Reichardt Centre for Integrative Neuroscience, Otfried-Müller-Straße 25, 72076 Tuebingen, Germany. e-mail: kirsten.volz@cin. uni-tuebingen.de We deal with risk versus uncertainty, a distinction that is of fundamental importance for cognitive neuroscience yet largely neglected. In a world of risk (“small world”), all alterna- tives, consequences, and probabilities are known. In uncertain (“large”) worlds, some of this information is unknown or unknowable. Most of cognitive neuroscience studies exclu- sively study the neural correlates for decisions under risk (e.g., lotteries), with the tacit implication that understanding these would lead to an understanding of decision making in general. First, we show that normative strategies for decisions under risk do not generalize to uncertain worlds, where simple heuristics are often the more accurate strategies. Sec- ond, we argue that the cognitive processes for making decisions in a world of risk are not the same as those for dealing with uncertainty. Because situations with known risks are the exception rather than the rule in human evolution, it is unlikely that our brains are adapted to them. We therefore suggest a paradigm shift toward studying decision processes in uncertain worlds and provide ﬁrst examples. Keywords: as-if versus process models, neuroscience of decision making, risk and uncertainty, small world versus large world problems, heuristics RISK 6D UNCERTAINTY In 1999, Elkhonon Goldberg and Kenneth Podell distinguished between adaptive and veridical decision making. Noticing the pre- dominance of the latter in the cognitive neuroscientiﬁc studies at that time, they concluded that new paradigms were desperately needed: In a typical experimental paradigm used in cognitive neu- roscience, one possible response is correct and others are incorrect. The determination of what is correct and what is “incorrect” is inherent in the experimental situation (external milieu) and does not require any knowledge of the organ- ism making the choice (internal milieu). The typical exper- imental paradigms used in cognitive neuropsychology are deterministic and veridical. (p. 365) With some disappointment they concluded: “Paradoxically and almost incomprehensibly, the arsenal of cognitive neuroscience is virtually completely bereft of paradigms capable of examining how adaptive (as opposed to veridical) decisions are made” (p. 366). As a result, they called for innovative experimental procedures to determine the contribution of the prefrontal lobes to adaptive decision making. Goldberg and Podell (1996) hit on a distinction closely related to one made in economics and decision theory: the distinction between risk and uncertainty (Knight, 1921). Risk, according to Knight, refers to situation of perfect knowledge: the decision maker knows the probabilities of all outcomes for all alternatives. This makes it possible to calculate the only correct, or optimal, response. Uncertainty, in contrast, refers to situations where the probabilities cannot be expressed with any mathematical preci- sion, neither in frequencies nor in propensities. That is, in an www.frontiersin.org uncertain world, the probabilities are unknown or unknowable. As an economist, Knight perceived this distinction to be impor- tant, since uncertainty may afford opportunities for proﬁt that do not exist in situations where risks can be calculated (Rakow, 2010). A related distinction was made by Savage (1954), known as the founder of modern Bayesian decision theory. Savage introduced the term “small worlds” for situations of perfect knowledge where all relevant alternatives, their consequences, and their probabili- ties are known for certain. According to him, these are the worlds in which Bayesian theory provides the best answer. Examples are lotteries and roulette. Small worlds need to be distinguished from “large worlds,” where part of the relevant information is unknown or must be estimated from small samples, or the future is uncer- tain (Savage, 1954; Binmore, 2009). Examples are decisions about when to plan a picnic, whom to marry, and how to raise your kids. Decision making under uncertainty is what our brain does most of the time, while situations of known risk are relatively rare and found mostly in gambling. Savage made it very clear that applying Bayesian theory to decisions in large (uncertain) worlds would be “utterly ridiculous” (p. 16) because there is no way to know all alternatives, consequences, and probabilities. As a conse- quence, the brain needs strategies beyond Bayes’ rule to succeed in an uncertain social and physical environment. The distinction between risk and uncertainty has not always been recognized in cognitive neuroscience. In this article, we make a normative and a descriptive argument regarding this distinction: 1. The best solution in a world of risk is generally not the best one in a world of uncertainty. We argue that what the brain should do July 2012 | Volume 6 | Article 105 | 1