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. © 2006 Springer-Verlag London.
CITATION STYLE
Belavkin, R. V. (2006). Acting irrationally to improve performance in stochastic worlds. In Research and Development in Intelligent Systems XXII - Proceedings of AI 2005, the 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 305–316). Springer London. https://doi.org/10.1007/978-1-84628-226-3_23
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