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Minds and Machines

, Volume 26, Issue 1–2, pp 9–30 | Cite as

Building the Theory of Ecological Rationality

  • Peter M. Todd
  • Henry Brighton
Article

Abstract

While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the organism and a set of principles, such as the bias– variance tradeoff, that can explain when and why heuristics work in uncertain environments, and then shows how effective heuristics can be built by biasing and simplifying more complex models. We conclude with challenges these approaches face in developing a psychologically realistic perspective on human rationality.

Keywords

Ecological rationality Bounded rationality Heuristics Toolbox Bias/variance dilemma Statistical learning theory Uncertainty Optimality Hedgefox 

References

  1. Anderson, J. R. (1990). The adaptive character of thought. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  2. Barrett, H. C., & Kurzban, R. (2006). Modularity in cognition: Framing the debate. Psychological Review, 113(3), 628–647.CrossRefGoogle Scholar
  3. Bennis, W. M., Katsikopoulos, K. V., Goldstein, D. G., Dieckmann, A., & Berg, N. (2012). Designed to fit minds: Institutions and ecological rationality. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 409–427). New York: Oxford University Press.Google Scholar
  4. Berlin, I. (1953). The Hedgehog and the Fox: An essay on Tolstoy’s view of history. New York: Simon and Schuster.Google Scholar
  5. Borges, B., Goldstein, D. G., Ortmann, A., & Gigerenzer, G. (1999). Can ignorance beat the stock market? In G. Gigerenzer, P. M. Todd, & The ABC Research Group, Simple heuristics that make us smart (pp. 59–72). New York: Oxford University Press.Google Scholar
  6. Boyd, M. (2001). On ignorance, intuition, and investing: A bear market test of the recognition heuristic. Journal of Psychology and Financial Markets, 2, 150–156.CrossRefGoogle Scholar
  7. Boyd, R., & Richerson, P. J. (1985). Culture and evolutionary processes. Chicago: University of Chicago Press.Google Scholar
  8. Boyd, R., & Richerson, P. J. (2005). The origin and evolution of cultures. New York: Oxford University Press.Google Scholar
  9. Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16, 199–231.MathSciNetCrossRefzbMATHGoogle Scholar
  10. Brighton, H., & Gigerenzer, G. (2012a). How heuristics handle uncertainty. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 33–60). New York: Oxford University Press.Google Scholar
  11. Brighton, H., & Gigerenzer, G. (2012b). Are rational actor models “rational” outside small worlds? In S. Okasha & K. Binmore (Eds.), Evolution and rationality: Decisions, co-operation and strategic behaviour (pp. 84–109). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  12. Brighton, H. J., & Olsson, H. (2009). Identifying the optimal response is not a necessary step toward explaining function. Behavioral and Brain Sciences, 32, 85–86.CrossRefGoogle Scholar
  13. Bröder, A. (2012). The quest for take-the-best: Insights and outlooks from experimental research. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 216–240). New York: Oxford University Press.Google Scholar
  14. Buss, D. M. (2011). Evolutionary psychology: The new science of the mind (4th ed.). Boston: Pearson.Google Scholar
  15. Cosmides, L., & Tooby, J. (1994). Origins of domain specificity: The evolution of functional organization. In L. A. Hirschfeld & S. A. Gelman (Eds.), Mapping the mind: Domain specificity in cognition and culture (pp. 85–116). New York: Cambridge University Press.CrossRefGoogle Scholar
  16. 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.Google Scholar
  17. Davey, G. (1989). Ecological learning theory. Florence, KY: Taylor & Frances/Routledge.Google Scholar
  18. DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22, 1915–1953.CrossRefGoogle Scholar
  19. Dieckmann, A., & Rieskamp, J. (2007). The influence of information redundancy on probabilistic inferences. Memory & Cognition, 35, 1801–1813.CrossRefGoogle Scholar
  20. Fasolo, B., McClelland, G. H., & Todd, P. M. (2007). Escaping the tyranny of choice: When fewer attributes make choice easier. Marketing Theory, 7(1), 13–26.CrossRefGoogle Scholar
  21. Geisser, S. (1993). Predictive inference: An introduction. New York: Chapman and Hall.CrossRefzbMATHGoogle Scholar
  22. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1–58.CrossRefGoogle Scholar
  23. Gigerenzer, G. (2008). Rationality for mortals: How people cope with uncertainty. New York: Oxford University Press.Google Scholar
  24. Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107–143.CrossRefGoogle Scholar
  25. Gigerenzer, G., Dieckmann, A., & Gaissmaier, W. (2012). Efficient cognition through limited search. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 241–274). New York: Oxford University Press.Google Scholar
  26. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.CrossRefGoogle Scholar
  27. 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.Google Scholar
  28. Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.Google Scholar
  29. Gigerenzer, G., & Todd, P. M. (1999). Fast and frugal heuristics: The adaptive toolbox. In G. Gigerenzer, P. M. Todd, & The ABC Research Group, Simple heuristics that make us smart (pp. 3–34). New York: Oxford University Press.Google Scholar
  30. Gigerenzer, G., & Todd, P. M. (2012). Ecological rationality: The normative study of heuristics. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 487–497). New York: Oxford University Press.Google Scholar
  31. Gigerenzer, G., Todd, P. M., & The ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.Google Scholar
  32. Gilboa, I., Postlewaite, A., & Schmeidler, D. (2012). Rationality of belief or: Why Savage's axioms are neither necessary nor sufficient for rationality. Synthese, 187, 11–31.MathSciNetCrossRefzbMATHGoogle Scholar
  33. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75–90.CrossRefGoogle Scholar
  34. Hammerstein, P., & Stevens, J. R. (Eds.). (2012). Evolution and the mechanisms of decision making. Strüngmann Forum Reports. Cambridge, MA: MIT Press.Google Scholar
  35. Hertwig, R., Hoffrage, U., & The ABC Research Group. (2013). Simple heuristics in a social world. New York: Oxford University Press.Google Scholar
  36. Hogarth, R. M. (2012). When simple is hard to accept. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 61–79). New York: Oxford University Press.Google Scholar
  37. 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.CrossRefzbMATHGoogle Scholar
  38. Hogarth, R. M., & Karelaia, N. (2007). Heuristic and linear models of judgment: Matching rules and environments. Psychological Review, 114, 733–758.CrossRefGoogle Scholar
  39. Hutchinson, J., Wilke, A., & Todd, P. M. (2008). Patch leaving in humans: Can a generalist adapt its rules to dispersal of items across patches? Animal Behaviour, 75(4), 1331–1349.CrossRefGoogle Scholar
  40. Hutchinson, J. M. C., Fanselow, C., & Todd, P. M. (2012). Car parking as a game between simple heuristics. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 454–484). New York: Oxford University Press.Google Scholar
  41. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.Google Scholar
  42. Katsikopoulos, K. V. (2011). Psychological heuristics for making decisions: Definition, performance, and the emerging theory and practice. Decision Analysis, 8, 10–29.MathSciNetCrossRefGoogle Scholar
  43. Katsikopoulos, K. V., & Martignon, L. (2006). Naive heuristics for paired comparisons: Some results on their relative accuracy. Journal of Mathematical Psychology, 50, 488–494.MathSciNetCrossRefzbMATHGoogle Scholar
  44. Kenrick, D. T., Griskevicius, V., Sundie, J. M., Li, N. P., Li, Y. J., & Neuberg, S. L. (2009). Deep rationality: The evolutionary economics of decision-making. Social Cognition, 27, 764–785.CrossRefGoogle Scholar
  45. Kurzenhäuser, S., & Hoffrage, U. (2012). Designing risk communication in health. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 428–453). New York: Oxford University Press.Google Scholar
  46. Loewenstein, G., Vohs, K. D., & Baumeister, R. F. (2007). Introduction: The hedgefox. In K. D. Vohs, R. F. Baumeister, & G. Loewenstein (Eds.), Do emotions help or hurt decision making? A Hedgefoxian perspective (pp. 3–9). New York: Russell Sage Foundation.Google Scholar
  47. Marewski, J. N., & Schooler, L. J. (2011). Cognitive Niches: An ecological model of strategy selection. Psychological Review, 118, 393–437.CrossRefGoogle Scholar
  48. 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.Google Scholar
  49. McKenzie, C. R. M., & Chase, V. M. (2012). Why rare things are precious: How rarity benefits inference. In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 309–334). New York: Oxford University Press.Google Scholar
  50. Miller, G. F. (2000). The mating mind: How sexual choice shaped the evolution of human nature. New York: Doubleday.Google Scholar
  51. Pachur, T., Todd, P. M., Gigerenzer, G., Schooler, L. J., & Goldstein, D. G. (2012). When is the recognition heuristic an adaptive tool? In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 113–143). New York: Oxford University Press.Google Scholar
  52. Piattelli-Palmarini, M. (1996). Inevitable illusions: How mistakes of reason rule our minds. New York: Wiley.Google Scholar
  53. Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207–236.CrossRefGoogle Scholar
  54. Rissanen, J. (2007). Information and complexity in statistical modeling. New York: Springer.zbMATHGoogle Scholar
  55. Schooler, L. J., & Hertwig, R. (2005). How forgetting aids heuristic inference. Psychological Review, 112, 610–628.CrossRefGoogle Scholar
  56. Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.MathSciNetCrossRefzbMATHGoogle Scholar
  57. Silver, N. (2012). The signal and the noise: Why so many predictions fail—But some don’t. New York: Penguin Press.Google Scholar
  58. Simon, H. A. (1981). The sciences of the artificial (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
  59. Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41, 1–19.CrossRefGoogle Scholar
  60. Şimşek, Ö. (2013). Linear decision rule as aspiration for simple decision heuristics. Advances in Neural Information Processing Systems, 26, 2904–2912.Google Scholar
  61. Tetlock, P. E. (2006). Expert political judgment: How good is it? How can we know?. Princeton, NJ: Princeton University Press.Google Scholar
  62. Todd, P. M. (2001). Fast and frugal heuristics for environmentally bounded minds. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 51–70). Cambridge, MA: MIT Press.Google Scholar
  63. Todd, P. M., & Gigerenzer, G. (2007). Environments that make us smart: Ecological rationality. Current Directions in Psychological Science, 16(3), 167–171.CrossRefGoogle Scholar
  64. Todd, P. M., & Gigerenzer, G. (2012). What is ecological rationality? In P. M. Todd, G. Gigerenzer, & The ABC Research Group, Ecological rationality: Intelligence in the world (pp. 3–30). New York: Oxford University Press.Google Scholar
  65. Todd, P. M., Gigerenzer, G., & The ABC Research Group. (2012a). Ecological rationality: Intelligence in the world. New York: Oxford University Press.Google Scholar
  66. Todd, P. M., Hills, T. T., & Robbins, T. W. (Eds.) (2012b). Cognitive search: Evolution, algorithms, and the brain. Strüngmann Forum Reports (vol. 9). Cambridge, MA: MIT Press.Google Scholar
  67. Tukey, J. W. (1962). The future of data analysis. The Annals of Mathematical Statistics, 33, 1–67.MathSciNetCrossRefzbMATHGoogle Scholar
  68. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.CrossRefGoogle Scholar
  69. Wilke, A., & Todd, P. M. (2012). Evolutionary foundations of decision making. In M. K. Dhami, A. Schlottmann, & M. Waldmann (Eds.), Origins of judgment and decision making (pp. 3–27). Mahwah, NJ: Lawrence Erlbaum.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Cognitive Science ProgramIndiana University BloomingtonBloomingtonUSA
  2. 2.Center for Adaptive Behavior and CognitionMax Planck Institute for Human DevelopmentBerlinGermany

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