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Adaptive Probability Theory: Human Biases as an Adaptation

Martins, André C. R. (2005) Adaptive Probability Theory: Human Biases as an Adaptation. [Preprint]

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Abstract

Humans make mistakes in our decision-making and probability judgments. While the heuristics used for decision-making have been explained as adaptations that are both efficient and fast, the reasons why people deal with probabilities using the reported biases have not been clear. We will see that some of these biases can be understood as heuristics developed to explain a complex world when little information is available. That is, they approximate Bayesian inferences for situations more complex than the ones in laboratory experiments and in this sense might have appeared as an adaptation to those situations. When ideas as uncertainty and limited sample sizes are included in the problem, the correct probabilities are changed to values close to the observed behavior. These ideas will be used to explain the observed weight functions, the violations of coalescing and stochastic dominance reported in the literature.

Item Type:Preprint
Keywords:Rationality, Heuristics, Evolution, Bounded Rationality, Bayesian Inference, Adaptation, Weight functions, Decision Making
Subjects:Philosophy > Decision Theory
Psychology > Evolutionary Psychology
ID Code:4377
Deposited By: Martins, Prof André C. R.
Deposited On:02 Aug 2005
Last Modified:11 Mar 2011 08:56

References in Article

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Allais, P. M. (1953) The behavior of rational man in risky situations - A critique of the axioms and postulates of the American School. Econometrica, 21, 503-546.

Bernardo, J. M., Smith, A.F.M. (1994), Bayesian Theory. New York, Wiley.

Birnbaum, M. H., (2005) New paradoxes of risky decision making. Working Paper.

Birnbaum, M. H., & Chavez, A. (1997) Tests of theories of decision making: Violations of branch independence and distribution independence. Organizational Behavior and Human Decision Processes, 71 (2), 161-194.

Cohen, J., Chesnick, E.I., & Haran, D., (1979) Evaluation of compound probabilities in sequential choice, Nature, 232, 414-416

Ellsberg, D. (1961) Risk, ambiguity and the Savage axioms. Quart. J. of Economics, 75, 643-669.

Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psych. Rev., 103, 650-669.

Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psych. Rev., 102, 684-704.

Gigerenzer, G., & Selten, R. (2001). Rethinking Rationality in G. Gigerenzer, R. Selten (eds.), Bounded rationality: The adaptive toolbox. Dahlem Workshop Report, 147-171. Cambridge, Mass, MIT Press.

Gilovich, T., & Griffin, D. (2002) Introduction - Heuristics and biases: Then and now in Gilovich, T., Griffin, D., & Kahneman, D. (eds.) Heuristics and biases: The Psychology of intuitive judgment. Cambridge, Cambridge University Press.

Hertwig, R., Hoffrage, U., & Martignon, L. (1999). Quick estimation: Letting the environment do the work in G. Gigerenzer, & P. M. Todd (eds.), Simple Heuristics that make us smart, 209-234. New York, Oxford University Press.

Kahneman, D., & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291

Kahneman, D., & Tversky, A. (1992) Advances in prospect theory: Cumulative representation of uncertainty. J. of Risk and Uncertainty, 5, 297-324.

Kareev, Y., Lieberman, I., & Lev, M. (1997) Through a narrow window: Sample size and the perception of correlation. J. of Exp. Psych.: General, 126, 278-287

Luce, R. D. (2000) Utility of gains and losses: Measurement-theoretical and experimental approaches. Mahwah: Lawrence Erlbaum Associates.

Marley, A. A. J., & Luce, E. D. (2001) Rank-weighted utilities and qualitative convolution. J. of Risk and Uncertainty, 23 (2), 135-163.

Martignon, L. (2001). Comparing fast and frugal heuristics and optimal models in G. Gigerenzer, & R. Selten (eds.), Bounded rationality: The adaptive toolbox. Dahlem Workshop Report, 147-171. Cambridge, Mass, MIT Press.

Phillips, L. D., & Edwards, W. (1966) Conservatism in a simple probability inference task, J. of Exp. Psych., 72, 346-354.

Pinker, S. (1997) How the mind works. New York, Norton.

Prelec, D. (2000) Compound invariant weighting functions in Prospect Theory in Kahneman, D., & Tversky, A. (eds.), Choices, Values and Frames}, 67-92. New York, Russell Sage Foundation, Cambridge University Press.

Savage, L. J. (1954) The Foundations of Statistics. New York, Wiley.

Selten, R. (1998), Aspiration adaptation theory. J. of Math. Psych., 42, 191-214.

Selten, R. (2001). What is bounded rationality? in G. Gigerenzer, R. Selten (eds.), Bounded rationality: The adaptive toolbox. Dahlem Workshop Report, 147-171. Cambridge, Mass, MIT Press.

Simon, H. A. (1956) Rational choice and the structure of environments. Psych. Rev., 63, 129-138

Tversky, A., & Kahneman, D. (1981) The framing of decisions and psychology of choice. Science, 211, 453-458

von Neumann, J., & Morgenstern, O. (1947) Theory of Games and Economic Behavior. Princeton, Princeton University Press.

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