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Non-Probabilistic Decision Making with Memory Constraints

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  • Vostroknutov, Alexander

Abstract

In the model of choice, studied in this paper, the decision maker chooses the actions non-probabilistically in each period (Sarin and Vahid, 1999; Sarin, 2000). The action is chosen if it yields the biggest payoff according to the decision maker’s subjective assessment. Decision maker knows nothing about the process that generates the payoffs. If the decision maker remembers only recent payoffs, she converges to the maximin action. If she remembers all past payoffs, the maximal expected payoff action is chosen. These results hold for any possible dynamics of weights and are robust against the mistakes. The estimates of the rate of convergence reveal that in some important cases the convergence to the asymptotic behavior can take extremely long time. The model suggests simple experimental test of the way people memorize past experiences: if any weighted procedure is actually involved, it can possibly generate only two distinct modes of behavior.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2653.

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Date of creation: Mar 2005
Date of revision: Jul 2007
Handle: RePEc:pra:mprapa:2653

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Related research

Keywords: Adaptive learning; constrained memory; bandit problem; non-probabilistic choice;

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  1. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, Elsevier, vol. 28(2), pages 294-309, August.
  2. Huck Steffen & Sarin Rajiv, 2004. "Players With Limited Memory," The B.E. Journal of Theoretical Economics, De Gruyter, De Gruyter, vol. 4(1), pages 1-27, September.
  3. Sarin, Rajiv, 2000. "Decision Rules with Bounded Memory," Journal of Economic Theory, Elsevier, Elsevier, vol. 90(1), pages 151-160, January.
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