Berg, Nathan and Hoffrage, Ulrich (2010): Compressed environments: Unbounded optimizers should sometimes ignore information. Published in: Minds and Machine , Vol. 20, No. 2 : pp. 259-275.
Download (514Kb) | Preview
Given free information and unlimited processing power, should decision algorithms use as much information as possible? A formal model of the decision making environment is developed to address this question and provide conditions under which informationally frugal algorithms, without any information or processing costs whatsoever, are optimal. One cause of compression that allows optimal algorithms to rationally ignore information is inverse movement of payoffs and probabilities (e.g., high payoffs occur with low probably and low payoffs occur with high probability). If inversely related payoffs and probabilities cancel out, then predictors that correlate with payoffs and consequently condition the probabilities associated with different payoffs will drop out of the expected-payoff objective function, severing the link between information and optimal action rules. Stochastic payoff processes in which rational ignoring occurs are referred to as compressed environments, because optimal action depends on a reduced-dimension subset of the environmental parameters. This paper considers benefits and limitations of economic models versus other methods for studying links between environmental structure and the real-world success of simple decision procedures. Different methods converge on the normative proposition of ecological rationality, as opposed to axiomatic rationality based on informational efficiency and internal consistency axioms, as a superior framework for comparing the effectiveness of decision strategies and prescribing decision algorithms in application.
|Item Type:||MPRA Paper|
|Original Title:||Compressed environments: Unbounded optimizers should sometimes ignore information|
|Keywords:||Ecological rationality, Bounded rationality, Frugality, Simplicity|
|Subjects:||D - Microeconomics > D0 - General > D03 - Behavioral Economics; Underlying Principles|
|Depositing User:||Nathan Berg|
|Date Deposited:||04. Nov 2010 09:21|
|Last Modified:||16. Feb 2013 02:01|
Berg, N. (2005). Decision-making environments in which unboundedly rational decision makers choose to ignore relevant information. Global Business and Economics Review, 7(1), 59–73.
Berg, N., & Hoffrage, U. (2008). Rational ignoring with unbounded cognitive capacity. Journal of Economic Psychology, 29, 792–809.
Bookstaber, R., & Langsam, J. (1985). On the optimality of coarse behavior rules. Journal of Theoretical Biology, 116, 161–193.
Brooks, R. A. (1991). New approaches to robotics. Science, 253, 1227–1232.
Bullock, S., & Todd, P. M. (1999). Made to measure: Ecological rationality in structured environments. Minds and Machines, 9(4), 497–541.
Clarke, D. D., Ward, P., & Truman, W. (2002). In-depth accident causation study of young drivers, Report no. TRL542, TRL Limited, Crowthorne, Berkshire.
Connolly, T. (1999). Action as a fast and frugal heuristic. Minds and Machines, 9(4), 479–496.
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.
Dobbie, K. (2002). Fatigue-related crashes: An analysis of fatigue-related crashes on Australian roads using an operational definition of fatigue, Australian transport safety bureau report no. OR23.
Forster, M. R. (1999). How do simple rules ‘fit to reality’ in a complex world? Minds and Machines, 9(4), 543–564.
Gigerenzer, G. & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107–143
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669.
Gigerenzer, G., Hoffrage, U., & Kleinbo¨lting, H. (1991). Probabilistic mental models: A Brunswikian theory of confidence. Psychological Review, 98, 506–528.
Gigerenzer, G., & Selten, R. (Eds.) (2001). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.
Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.
Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75–90.
Hansen, M. H., & Bin, Y. (1996). Model selection and minimum description. Journal of the American Statistical Association, 96, 746–774.
Hogarth, R. M. (2001). Educating instituition. Chicago: University of Chicago Press.
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.
Kahneman, D.,&Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.
Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.
Luce, D., & Raiffa, H. (1957). Games and decisions. New York: Wiley.
Mahowald, M. W. (2000). Eyes wide shut: The dangers of sleepy driving. Minnesota Medicine, 83, 25–30.
Martignon, L., & Hoffrage, U. (2002). Fast, frugal and fit: Simple heuristics for paired comparison. Theory and Decision, 52, 29–71.
Martignon, L., Katsikopoulos, K. V., & Woike, J. K. (2008). Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology, 52(6), 352–361.
Martignon, L., & Schmitt, M. (1999). Simplicity and robustness of fast and frugal heuristics. Minds and Machines, 9(4), 565–593.
Mohan, D. (2001). Traffic safety and thirty years of biomechanics research: A personal adventure. In International Research Council on the Biomechanics of Impact Proceedings (pp. 1–12).
Pack, A. I., Pack, A. M., Rodgman, E., Cucchiara, A., Dinges, D. F., & Schwab, C. W. (1995). Characteristics of crashes attributed to the driver having fallen asleep. Accident Analysis and Prevention, 27(6), 769–775.
Raiffa, H. (1986). Decision analysis: Introductory lectures on choices under uncertainty. New York: Random House.
Raiffa, H., & Shlaifer, R. (1961). Applied statistical decision theory. Cambridge, MA: Harvard University Press.
Schooler, L. J., & Hertwig, R. (2005). How forgetting aids heuristic inference. Psychological Review, 112, 610–628.
Simon, H. A. (1982). Models of bounded rationality. Cambridge: MIT Press.
Todd, P. M. (1999). Simple inference heuristics versus complex decision machines. Minds and Machines, 9(4), 461–477.
Todd, P. M. (2000) The ecological rationality of mechanisms evolved to make up minds. American Behavioral Scientist, 43, 940–956.
Tversky, A., & Kahneman, D. (1982). Evidential impact of base rates. In: D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 153–160). New York: Cambridge University Press.
Zellner, A., Keuzenkamp, H., & McAleer, M. (Eds.). (2001). Simplicity, inference