Abstract
Despite many theories and algorithms for decision-making, after estimating the utility function the choice is usually made by maximising its expected value (the max EU principle). This traditional and ‘rational’ conclusion of the decision-making process is compared in this paper with several ‘irrational’ techniques that make choice in Monte-Carlo fashion. The comparison is made by evaluating the performance of simple decision-theoretic agents in stochastic environments. It is shown that not only the random choice strategies can achieve performance comparable to the max EU method, but under certain conditions the Monte-Carlo choice methods perform almost two times better than the max EU. The paper concludes by quoting evidence from recent cognitive modelling works as well as the famous decision-making paradoxes.
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References
Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: Critique des postulats et axiomes de l’École americaine. Econometrica, 21, 503–546.
Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum.
Anscombe, F. J., & Aumann, R. J. (1963). A definition of subjective probability. Annals of Mathematical Statistics, 34, 199–205.
Belavkin, R. V., & Ritter, F. E. (2003. April). The use of entropy for analysis and control of cognitive models. In F. Detje, D. Dörner, & H. Schaub (Eds.), Proceedings of the Fifth International Conference on Cognitive Modelling (pp. 21–26). Bamberg, Germany: Universitäts-Verlag Bamberg.
Belavkin, R. V., & Ritter, F. E. (2004). Optimist: A new conflict resolution algorithm for ACT-R. In Proceedings of the Sixth International Conference on Cognitive Modelling (pp. 40–45). Mahwah, NJ: Lawrence Erlbaum.
Bellman, R. E. (1957). Dynamic programming. Princeton, NJ: Princeton University Press.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, J. M. P. (1983, May). Optimization by simulated annealing. Science, 220(4598), 671–680.
Myers, J. L., Fort, J. G., Katz, L., & Suydam, M. M. (1963). Differential monetary gains and losses and event probability in a two-choice situation. Journal of Experimental Psychology, 77, 453–359.
Neumann, J. von, & Morgenstern, O. (1944). Theory of games and economic behavior (first ed.). Princeton, NJ: Princeton University Press.
Savage, L. (1954). The foundations of statistics. NY: John Wiley & Sons.
Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88(2), 135–170.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453–458.
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© 2006 Springer-Verlag London Limited
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Belavkin, R.V. (2006). Acting Irrationally to Improve Performance in Stochastic Worlds. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXII. SGAI 2005. Springer, London. https://doi.org/10.1007/978-1-84628-226-3_23
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DOI: https://doi.org/10.1007/978-1-84628-226-3_23
Publisher Name: Springer, London
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