Non-Probabilistic Decision Making with Memory Constraints

Alexander Vostroknutov ()

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Adaptive learning; constrained memory; bandit problem; non-probabilistic choice (search for similar items in EconPapers)
JEL-codes: C02 D81 D83 (search for similar items in EconPapers)
Date: 2005-03, Revised 2007-07
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