Homo Heuristicus: Why Biased Minds Make Better Inferences

Topics in Cognitive Science

Volumn 1, Issue 1, 2009, Pages 107-143

  Gigerenzer, Gerd ^a   Brighton, Henry ^a  

^a MAX PLANCK INSTITUTE FOR HUMAN DEVELOPMENT   (Germany)

Author keywords

Decision making; Heuristics; Induction; Inferences; Rationality; Uncertainity

Indexed keywords

ADAPTIVE BEHAVIOR; COGNITION; HUMAN; PHYSIOLOGY; PSYCHOLOGICAL MODEL; PSYCHOLOGICAL THEORY; THINKING;

ADAPTATION, PSYCHOLOGICAL; COGNITION; HUMANS; MODELS, PSYCHOLOGICAL; PSYCHOLOGICAL THEORY; THINKING;

EID: 70349808366     PISSN: 17568757     EISSN: 17568765     Source Type: Journal    
DOI: 10.1111/j.1756-8765.2008.01006.x     Document Type: Article

References (127)

1. Alpaydin, E. (2004). Introduction to machine learning. Cambridge, MA: MIT Press.
2. Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409-429.
3. Axelrod, R. (1984). The evolution of cooperation. New York: Basic Books.
4. Ayton, P., & Fisher, I. (2004). The hot hand fallacy and the gambler's fallacy: Two faces of subjective randomness? Memory & Cognition, 32, 1369-1378.
5. Barbey, A. K., & Sloman, S. A. (2007). Base-rate respect: From ecological rationality to dual processes. Behavioral and Brain Sciences, 30, 241-254.
6. Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3, 439-449.
7. Beilock, S. L., Bertenthal, B. I., Mc Coy, A. M., & Carr, T. H. (2004). Haste does not always make waste: Expertise, direction of attention, and speed versus accuracy in performing sensorimotor skills. Psychonomic Bulletin and Review, 11, 373-379.
8. Beilock, S. L., Carr, T. H., Mac Mahon, C., & Starkes, J. L. (2002). When paying attention becomes counterproductive: Impact on divided versus skill-focused attention on novice and experienced performance of sensorimotor skills. Journal of Experimental Psychology: Applied, 8, 6-16.
9. Bergert, F. B., & Nosofsky, R. M. (2007). A response-time approach to comparing generalized rational and take-the-best models of decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 107-129.
10. Birmbaum, M. H. (2008). Evaluation of the priority heuristic as a descriptive model of risky decision making: Comment on Brandstätter, Gigerenzer, and Herwig (2006). Psychological Review, 115, 253-262.
11. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, England: Oxford University Press.
12. Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
13. Bookstaber, R., & Langsam, J. (1985). On the optimality of coarse behavior rules. Journal of Theoretical Biology, 116, 161-193.
14. Boyd, R., & Richerson, P. J. (2005). The origin and evolution of cultures. New York: Oxford University Press.
15. Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without trade-offs. Psychological Review, 113, 409-432.
16. Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2008). Risky choice with heuristics: Reply to Birnbaum (2008), Johnson, Schulte-Mercklenbeck, and Willemsen (2008) and Rieger and Wang (2008). Psychological Review, 115, 281-290.
17. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, P. J. (1994). Classification and regression trees. Belmont, CA: Wadsworth International Group.
18. Brighton, H. (2006). Robust inference with simple cognitive models. In C. Lebiere & R. Wray (Eds.), AAAI spring symposium: Cognitive science principles meet AI-hard problems (pp. 17-22). Menlo Park, CA: American Association for Artificial Intelligence.
19. Brighton, H., & Gigerenzer, G. (in press). How heuristics exploit uncertainty. In P. M. Todd, G. Gigerenzer, & the ABC Research Group, Ecological rationality: Intelligence in the world. New York: Oxford University Press.
20. Bröder, A. (2003). Decision making with the "adaptive toolbox": Influence of environmental structure, intelligence, and working memory load. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29, 611-625.
21. Bröder, A. (in press). The quest for take-the-best. In P. M. Todd, G. Gigerenzer, & the ABC Research Group, Ecological rationality: Intelligence in the world. New York: Oxford University Press.
22. Bröder, A., & Gaissmaier, W. (2007). Sequential processing of cues in memory-based multiattribute decisions. Psychonomic Bulletin & Review, 14, 895-900.
23. Bruss, F. T. (2000). Der Ungewissheit ein Schnippchen schlagen. Spektrum der Wissenschaft, 6, 106.
24. Carnap, R. (1947). On the application of inductive logic. Philosophy and Phenonmenlogical Research, 8, 133-148.
25. Chater, N., Oaksford, M., Nakisa, R., & Redington, M. (2003). Fast, frugal and rational: How rational norms explain behavior. Organizational Behavior and Human Decision Processes, 90, 63-86.
26. Clutton-Brock, T. H., & Albon, S. D. (1979). The roaring of red deer and the evolution of honest advertisement. Behaviour, 69, 145-170.
27. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21-27.
28. Czerlinski, J., Gigerenzer, G., & Goldstein, D. G. (1999). How good are simple heuristics? In G. Gigerenzer, P. M. Todd, & the ABC Research Group, Simple heuristics that make us smart (pp. 97-118). New York: Oxford University Press.
29. Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571-582.
30. Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95-106.
31. Dawkins, R. (1989). The selfish gene (2nd ed.). Oxford, England: Oxford University Press.
32. De Miguel, V., Garlappi, L., & Uppal, R. (in press). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, forthcoming.
33. Dieckmann, A., & Rieskamp, J. (2007). The influence of information redundancy on probabilistic inferences. Memory & Cognition, 35, 1801-1813.
34. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
35. Dudey, T., & Todd, P. M. (2002). Making good decisions with minimal information: Simultaneous and sequential choice. Journal of Bioeconomics, 3, 195-215.
36. Einhorn, H. J., & Hogarth, R. M. (1975). Unit weighting schemes for decision making. Organizational Behavior and Human Performance, 13, 171-192.
37. Elman, J. L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48, 71-99.
38. Fehr, E., & Schmidt, K. (1999). A theory of fairness, competition, and cooperation. Quarterly Journal of Economics, 114, 817-868.
39. Fishburn, P. C. (1974). Lexicographic orders, utilities and decision rules: A survey. Management Science, 20, 1442-1471.
40. Ford, J. K., Schmitt, N., Schechtman, S. L., Hults, B. M., & Doherty, M. L. (1989). Process tracing methods: Contributions, problems, and neglected research questions. Organizational Behavior and Human Decision Processes, 43, 75-117.
41. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-58.
42. Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98, 254-267.
43. Gigerenzer, G. (1996). On narrow norms and vague heuristics: A reply to Kahneman and Tversky (1996). Psychological Review, 103, 592-596.
44. Gigerenzer, G. (2000). Adaptive thinking: Rationality in the real world. Oxford, England: Oxford University Press.
45. Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. New York: Viking.
46. Gigerenzer, G. (2008). Rationality for mortals. New York: Oxford University Press.
47. Gigerenzer, G., Dieckmann, A., & Gaissmaier, W. (in press). Heuristic search as a building block of cognition. In P. M. Todd, G. Gigerenzer, & the ABC Research Group, Ecological rationality: Intelligence in the world. New York: Oxford University Press.
48. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650-669.
49. Gigerenzer, G., & Goldstein, D. G. (1999). Betting on one good reason: The take the best heuristic. In G. Gigerenzer, P. M. Todd, & the ABC Research Group, Simple heuristics that make us smart (pp. 75-95). New York: Oxford University Press.
50. Gigerenzer, G., & Regier, T. (1996). How do we tell an association from a rule? Comment on Sloman (1996). Psychological Bulletin, 119, 23-26.
51. Gigerenzer, G., & Selten, R. (Eds.) (2001). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.
52. Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple heuristics that make us smart. New York: Oxford University Press.
53. Gilbert, J. P., & Mosteller, F. (1966). Recognizing the maximum of a sequence. American Statistical Association Journal, 61, 35-73.
54. Gilovich, T., & Griffin, D. W. (2002). Heuristics and biases then and now. In T. Gilovich, D. W. Griffin, & D. Kahneman (Eds.), The psychology of intuitive judgment: Heuristic and biases (pp. 1-18). Cambridge, England: Cambridge University Press.
55. Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17, 295-314.
56. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75-90.
57. Good, I. J. (1967). On the principle of total evidence. The British Journal for the Philosophy of Science, 17, 319-321.
58. Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17 (9), 767-773.
59. Guttman, L. (1944). A basis for scaling qualitative data. American Sociological Review, 9, 139-150.
60. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
61. Hertwig, R., & Todd, P. M. (2003). More is not always better: The benefits of cognitive limits. In D. Hardman & L. Macchi (Eds.), Thinking: Psychological perspectives on reasoning, judgment and decision making (pp. 213-231). Chichester, England: Wiley.
62. Hogarth, R. M. (in press). When simple is hard to accept. In P. M. Todd, G. Gigerenzer, & the ABC Research Group, Ecological rationality: Intelligence in the world. Oxford, England: Oxford University Press.
63. Hogarth, R. M., & Karelaia, N. (2005). Ignoring information in binary choice with continuous variables: When is less "more"? Journal of Mathematical Psychology, 49, 115-124.
64. Hogarth, R. M., & Karelaia, N. (2006). "Take-the-best" and other simple strategies: Why and when the work "well" with binary cues. Theory and Decision, 61, 205-249.
65. Hutchinson, J. M. C., & Gigerenzer, G. (2005). Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behavioural Processes, 69, 97-124.
66. Jacoby, L. L., & Dallas, M. (1981). On the relationship between autobiographical memory and perceptual learning. Journal of Experimental Psychology: General, 110, 306-340.
67. Johnson, E. J., & Goldstein, D. G. (2003). Do defaults save lives? Science, 302, 1338-1339.
68. Johnson, J., & Raab, M. (2003). Take the first: Option generation and resulting choices. Organizational Behavior and Human Decision Processes, 91, 215-229.
69. Jolls, C., Sunstein, C. R., & Thaler, R. (1998). A behavioral approach to law and economics. Stanford Law Review, 50, 1471-1550.
70. Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103, 582-591.
71. Karelaia, N. (2006). Thirst for confirmation in multi-attribute choice: Does search for consistency impair decision performance? Organizational Behavior and Human Decision Processes, 100, 128-143.
72. Katsikopoulos, K. V., & Gigerenzer, G. (2008). One-reason decision-making: Modeling violations of expected utility theory. Journal of Risk and Uncertainty, 37, 35-56.
73. Katsikopoulos, K. V., & Martignon, L. (2006). Naive heuristics for paired comparisons: Some results on their relative accuracy. Journal of Mathematical Psychology, 50, 488-494.
74. Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives. Cambridge, England: Cambridge University Press.
75. Lee, M. D., Loughlin, N., & Lundberg, I. B. (2002). Applying one reason decision-making: The prioritization of literature searches. Australian Journal of Psychology, 54, 137-143.
76. Luria, A. R. (1968). The mind of a mnemonist. New York: Basic Books.
77. Martignon, L., & Hoffrage, U. (1999). Why does one reason decision making work? In G. Gigerenzer, P. M. Todd, & the ABC Research Group, Simple heuristics that make us smart (pp. 119-140). New York: Oxford University Press.
78. Martignon, L., & Hoffrage, U. (2002). Fast, frugal, and fit: Simple heuristics for paired comparisons. Theory and Decision, 52, 29-71.
79. Martignon, L., Katsikopoulos, K. V., & Woike, J. (2008). Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology, 52, 352-361.
80. Mugford, S. T., Mallon, E. B., & Franks, N. R. (2001). The accuracy of Buffon's needle: A rule of thumb used by ants to estimate area. Behavioral Ecology, 12, 655-658.
81. Newell, B. R. (2005). Re-visions of rationality? Trends in Cognitive Sciences, 9, 11-15.
82. Newell, B. R., & Shanks, D. R. (2003). Take the best or look at the rest? Factors influencing "one-reason" decision-making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 53-65.
83. Newell, B. R., Weston, N. J., & Shanks, D. R. (2003). Empirical tests of a fast and frugal heuristic: Not everyone "takes-the-best. Organizational Behavior and Human Decision Processes, 91, 82-96.
84. Newport, E. L. (1990). Maturational constraints on language learning. Cognitive Science, 14, 11-28.
85. Nosofsky, R. M. (1990). Relations between exemplar similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393-418.
86. Nosofsky, R. M., & Bergert, F. B. (2007). Limitations of exemplar models of multi-attribute probabilistic inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 999-1019.
87. Oaksford, M., & Chater, N. (1998). Rational models of cognition. Oxford, England: Oxford University Press.
88. Pachur, T., Bröder, A., & Marewski, J. N. (2008). The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue? Journal of Behavioral Decision Making, 21, 183-210.
89. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New York: Cambridge University Press.
90. Perlich, C., Provost, F., & Simonoff, J. S. (2003). Tree-induction vs. logistic regression: A learning curve analysis. Journal of Machine Learning Research, 4, 211-255.
91. Petrie, M., & Halliday, T. (1994). Experimental and natural changes in the peacock's (Pavo cristatus) train can affect mating success. Behavioral Ecology and Sociobiology, 35, 213-217.
92. Pichert, D., & Katsikopoulos, K. V. (2008). Green defaults: Information presentation and pro-environmental behaviour. Journal of Environmental Psychology, 28, 63-73.
93. Pohl, R. F. (2006). Empirical tests of the recognition heuristic. Journal of Behavioral Decision Making, 19, 251-271.
94. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
95. Richter, T., & Späth, P. (2006). Recognition is used as one cue among others in judgment and decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 150-162.
96. Rieskamp, J., & Hoffrage, U. (2008). Inferences under time pressure: How opportunity costs affect strategy selection. Acta Psychologica, 127, 258-276.
97. Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207-236.
98. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
99. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart, & J. L. Mc Clelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition Vol. 1: Foundations (pp. 318-362). Cambridge, MA: MIT Press.
100. Scheibehenne, B., & Bröder, A. (2007). Predicting Wimbledon 2005 tennis results by mere player name recognition. International Journal of Forecasting, 3, 415-426.
101. Schmidt, F. L. (1971). The relative efficiency of regression and simple unit weighting predictor weights in applied differential psychology. Educational and Psychological Measurement, 31, 699-714.
102. Schmitt, M., & Martignon, L. (2006). On the complexity of learning lexicographic strategies. Journal of Machine Learning Research, 7, 55-83.
103. Schooler, L. J., & Hertwig, R. (2005). How forgetting aids heuristic inference. Psychological Review, 112, 610-628.
104. Sedlmeier, P., Hertwig, R., & Gigerenzer, G. (1998). Are judgments of the positional frequencies of letters systematically biased due to availability? Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 754-770.
105. Selten, R. (2001). What is bounded rationality? In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 13-36). Cambridge, MA: MIT Press.
106. Serwe, S., & Frings, C. (2006). Who will win Wimbledon? The recognition heuristic in predicting sports events. Journal of Behavioral Decision Making, 19, 321-332.
107. Shaffer, D. N., Krauchunas, S. M., Eddy, M., & Mc Beath, M. K. (2004). How dogs navigate to catch Frisbees. Psychological Science, 15, 437-441.
108. Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: An effort-reduction framework. Psychological Bulletin, 137, 207-222.
109. Shepard, R. N. (1974). Representation of structure in similarity data: Problems and prospects. Psychometrika, 39, 373-421.
110. Shepard, R. N. (1987). Towards a universal law of generalization for psychological science. Science, 37, 1317-1323.
111. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99-118.
112. Simon, H. A. (1991). Models of my life. New York: Basic Books.
113. Simon, H. A. (1992). What is an "explanation" of behavior? Psychological Science, 3, 150-161.
114. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3-22.
115. Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69, 213-225.
116. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society B, 36, 111-147.
117. Sunstein, C. R. (Ed.) (2000). Behavioral law and economics. Cambridge, England: Cambridge University Press.
118. Tinbergen, N. (1958). Curious naturalists. London: Country Life.
119. Todd, P. M., & Miller, G. F. (1999). From pride and prejudice to persuasion: Realistic heuristics for mate search. In G. Gigerenzer, P. M. Todd, & the ABC Research Group, Simple heuristics that make us smart (pp. 287-308). New York: Oxford University Press.
120. Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79, 281-299.
121. Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327-352.
122. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 4, 207-232.
123. Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185, 1124-1131.
124. Volz, K. G., Schooler, L. J., Schubotz, R. I., Raab, M., Gigerenzer, G., & Von Cramon, D. Y. (2006). Why you think Milan is larger than Modena: Neural correlates of the recognition heuristic. Journal of Cognitive Neuroscience, 18, 1924-1936.
125. Weisberg, S. (1985). Applied linear regression. New York: Wiley.
126. Wübben, M., & Wangenheim, F. v. (2008). Instant customer base analysis: Managerial heuristics often "get it right. Journal of Marketing, 72, 82-93.
127. Yee, M., Dahan, E., Hauser, J. R., & Orlin, J. B. (2007). Greedoid-based non-compensatory two-stage consideration-then-choice inference. Marketing Science, 26, 532-549.