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Environments That Make Us Smart Ecological Rationality Peter M CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Environments That Make Us Smart Ecological Rationality Peter M. Todd1,2 and Gerd Gigerenzer1 1Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany, and 2Program in Cognitive Science, Indiana University ABSTRACT—Traditional views of rationality posit general- prevailing explanations of behavior are still expressed most often purpose decision mechanisms based on logic or optimiza- in terms of personality traits, cognitive styles, brain-region acti- tion. The study of ecological rationality focuses on un- vation patterns, preferences and utilities, and other assumed covering the ‘‘adaptive toolbox’’ of domain-specific simple entities ‘‘inside’’ the mind. heuristics that real, computationally bounded minds em- The research program on ecological rationality aims to ex- ploy,and explaining how these heuristics produce accurate plicate the mind–world interactions underlying good decision decisions by exploiting the structures of information in the making. We build on the foundations from Darwin, Brunswik, environments in which they are applied. Knowing when Simon, and others to create a framework for understanding how and how people use particular heuristics can facilitate the environment structure—in the form of useful patterns of avail- shaping of environments to engender better decisions. able information in the world—can be exploited by heuristics in the head to produce adaptive behavior (Gigerenzer, Todd, & KEYWORDS—ecologicalrationality;decision making;adap- the ABC Research Group, 1999; Todd, Gigerenzer, & the ABC tive toolbox; simple heuristics; environment structure; Research Group, in press). Heuristics—simple decision algo- bounded rationality rithms that can work well in appropriate environments—gen- erate both routine behavior and important decisions, for The importance of looking at the world to understand the mind inference, choice, group deliberations, and even moral issues. has long been appreciated by a few prominent thinkers. Charles For instance, consider the puzzling observation that only 28% of Darwin held that environmental forces had shaped human be- Americans become potential organ donors but 99.9% of French havior through natural selection, leading to the modern call by people do. To find out why, one might administer personality evolutionary psychologists to look to our ancestral world for the tests, measure moral attitudes, add a knowledge exam, and then problems our minds are designed to solve. More than 50 years ago, perform multiple regression on the lot to find some significant Egon Brunswik urged psychologists to study the texture of nat- predictors—but not the answer. Rather, most Americans and ural environments and the corresponding structure of cues the French seem to rely on the same simple default heuristic: ‘‘If mind relies on to infer the state of its surroundings. Roger there’s a default choice, stick with it.’’ The difference is in the Shepard spoke of the mind as a mirror, reflecting long-standing external (here institutional) default-setting environment in each physical aspects of the world such as the 24-hour light–dark country: In most U.S. states there is a no-organ-donation default, cycle. Herbert Simon proposed the metaphor of the mind and so one has to actively opt in to become a donor, while in France world fitting together like the blades of a pair of scissors—the one has to opt out to not be a donor (Johnson & Goldstein, 2003). two must be well matched for effective behavior to be produced, It is the interaction between a heuristic and its social, institu- and just looking at the cognitive blade will not explain how the tional, or physical environment that explains behavior. This scissors cut. In each case, the world is a key for understanding adaptive view has policy consequences: It explains why the workings of the mind (Todd & Gigerenzer, 2001). However, pro-donor information campaigns have had only limited success and indicates that changing the legal default should be more effective. Address correspondence to Peter M. Todd, Program in Cognitive Science, Indiana University, 1101 E. 10th Street, Bloomington, IN Or consider the inference task of predicting which of two 47405; e-mail: [email protected]. tennis players will win an upcoming Wimbledon match. This Volume 16—Number 3 Copyright r 2007 Association for Psychological Science 167 Ecological Rationality decision can be made on the basis of pieces of information, or The first component is explanatory, asking, for instance, how cues, that could be looked up about each player, such as whether people make decisions about organ donation or winning sports they are past champions, how many games they have won this competitors. The second is normative, determining what envi- season, what their seeding by the Wimbledon experts is, and so ronment structures will increase organ-donation rates or help the on. More simply, one could ignore all of this information and just recognition heuristic to predict match outcomes accurately. The rely on the recognition heuristic: ‘‘If you recognize one player and study of ecological rationality, our focus here, requires clear not the other, then predict the recognized one will win’’ definitions of both heuristics and environments. (Gigerenzer, 2007). Tennis novices make predictions in line with this heuristic often (90% of the time). More surprisingly, their STUDYING ECOLOGICAL RATIONALITY IN collective recognition can be even more accurate (e.g., correct DECISION MAKING on 72% of men’s 2003 matches) than the Wimbledon experts’ ratings (69%). But the recognition heuristic will only perform so The modern study of decision making began with the normative well in environments that it is suited to—namely, those where ideal that good decisions follow the mathematical prescriptions the ‘‘biggest’’ objects (like the biggest winners in sports) are of Bayes’s rule, or the maximization of expected utility. In these frequently discussed and hence likely to be recognized. views, there is only one mental tool, and the question of this tool’s Our research program has two components that correspond to ecological rationality—its fit to different environments—does the two blades of Simon’s scissors: not arise. But now there is an impressive body of experimental evidence showing that people often make decisions in an en- (a) The study of the ‘‘adaptive toolbox’’ of decision mechanisms tirely different way: Humans rely on multiple simple decision in the mind (see Table 1 for examples). The goal is to uncover heuristics, not one general-purpose calculus of rationality (e.g., and understand heuristics for inference and preference (e.g., Bro¨der & Schiffer, 2003; Gigerenzer, 2007). Individuals can tasks of categorization, estimation, and choice), their certainly be led to use particular heuristics in inappropriate building blocks, the few pieces of information they use, and environments and consequently make errors, as the heuristics- the evolved abilities they exploit. The methods employed are and-biases research tradition emphasized (Kahneman, Slovic, & theoretical and experimental. Tversky, 1982). The study of ecological rationality goes beyond (b) The study of the ecological rationality of decision mecha- this beginning: By (a) designing computational models of heu- nisms. The goal is to determine what environmental struc- ristics, it (b) specifies which environments are appropriate for tures enable a given heuristic to be successful, and where it which heuristics, and vice versa. will fail. The methods also include computer simulation and People often rely on a single reason to make decisions; but can mathematical analysis. this particular heuristic approach—restricting information TABLE 1 Sample Heuristics, Environmental Structures That Make Them Ecologically Rational, and Surprising Predictions Heuristic Definition1 Ecologically rational if Surprising predictions Recognition To decide which of two options is Recognition is a valid cue (i.e., leads Contradicting information about greater on some criterion, if only to correct decisions over half of the recognized object is ignored; one option is recognized, choose time) recognizing fewer options can lead that one. to greater accuracy. Take The Best As above, but if both options are Cue validities vary highly; moderate Can decide more accurately than (see Fig. 1) recognized, to high redundancy between cues multiple regression, neural (1) search through cues in networks, and exemplar models order of validity when generalizing to new data (2) stop search on first discriminating cue (3) choose option favored by this cue Tallying (unit-weight To estimate criterion for some object, Cue validities vary little; low cue Can decide as accurately as multiple linear model) count number of favoring cues. redundancy (Hogarth & Karelaia, regression 2006). Try-a-dozen To select a high-valued option from an Unknown distribution of option Near-optimal performance over a wide (satisficing) unknown sequence, set an values; no returning to previously range of sequence lengths (i.e., aspiration level at highest value seen options number of available options matters seen in first 12 options, then choose little) next option that exceeds aspiration. Note. 1For further details, see Gigerenzer et al. (1999). 168 Volume 16—Number 3 Peter M. Todd and Gerd Gigerenzer use as much as possible—ever be reasonable? To answer, we must mechanisms (standard decision models that add up all cue first define specific
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