Heuristics: The Foundations of Adaptive Behavior
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How do people make decisions when time is limited, information unreliable, and the future uncertain? Based on the work of Nobel laureate Herbert Simon and with the help of colleagues around the world, the Adaptive Behavior and Cognition (ABC) Group at the Max Planck Institute for Human Development in Berlin has developed a research program on simple heuristics, also known as fast and frugal heuristics. In the social sciences, heuristics have been believed to be generally inferior to complex methods for inference, or even irrational. Although this may be true in "small worlds" where everything is known for certain, we show that in the actual world in which we live, full of uncertainties and surprises, heuristics are indispensable and often more accurate than complex methods. Contrary to a deeply entrenched belief, complex problems do not necessitate complex computations. Less can be more. Simple heuristics exploit the information structure of the environment, and thus embody ecological rather than logical rationality. Simon (1999) applauded this new program as a "revolution in cognitive science, striking a great blow for sanity in the approach to human rationality."
By providing a fresh look at how the mind works as well as the nature of rationality, the simple heuristics program has stimulated a large body of research, led to fascinating applications in diverse fields from law to medicine to business to sports, and instigated controversial debates in psychology, philosophy, and economics. In a single volume, the present reader compiles key articles that have been published in journals across many disciplines. These articles present theory, real-world applications, and a sample of the large number of existing experimental studies that provide evidence for people's adaptive use of heuristics.
inequality, the market, and individual behavior. Linear regression estimates the optimal beta weights for the predictors. In the 1970s, researchers discovered that equal (or random) weights can predict almost as accurately as, and sometimes better than, multiple linear regression (Dawes, 1979; Dawes & Corrigan, 1974; Einhorn & Hogarth, 1975; Schmidt, 1971). Weighting equally is also termed tallying, reminiscent of the tally sticks for counting, which can be traced back some 30,000 years in human
“true” underlying temperature pattern, which is a degree-3 polynomial, h(x). A sample of 30 noisy observations of h(x) is also shown. This new setting allows us to illustrate why bias is only one source of error impacting on the accuracy of model predictions. (p. 12 ) The second source of error is variance, which occurs when making inferences from finite samples of noisy data. As well as plotting prediction error as a function of the degree of the polynomial model, Figure 1-4B decomposes this
Ralph Hertwig 8. One-Reason Decision-Making: Modeling Violations of Expected Utility Theory 185 Konstantinos V. Katsikopoulos and Gerd Gigerenzer 9. Moral Satis icing: Rethinking Moral Behavior as Bounded Rationality 201 Gerd Gigerenzer 10. Hindsight Bias: By-Product of Knowledge Updating? 222 Ulrich Hoffrage, Ralph Hertwig, and Gerd Gigerenzer How are heuristics selected? 11. SSL: Theory of How People Learn to Select Strategies 243 Jörg Rieskamp and Philipp E. Otto Part II: Tests When do
the ith cue is where t(a) and t(b) are the values of objects a and b on the target variable t and p is a probability measured as a relative frequency in R. The ecological validity of the nine cues ranged over the whole spectrum: from .51 (only slightly better than chance) to 1.0 (certainty), as shown in Table 2-1. A cue with a high ecological validity, however, is not often useful if its discrimination rate is small. Table 2-1 shows also the discrimination rates for each cue. The discrimination
knowledge—the Americans had about their own cities, and despite their limited knowledge about Germany, they could not make more accurate inferences about American cities than about German ones. Faced with German cities, the participants could apply the recognition heuristic. Faced with American cities, they had to rely on knowledge beyond recognition. The fast-and-frugal recognition heuristic exploited the information inherent in a lack of knowledge to make inferences that were slightly more