Choosing a good toolkit, II: Bayes-rule based heuristics

Alejandro Francetich and David Kreps

Journal of Economic Dynamics and Control, 2020, vol. 111, issue C

Abstract: We study heuristics for a class of complex multi-armed bandit problems, the period-by-period choice of a set of objects or “toolkit” where the decision maker learns about the value of tools within the chosen toolkit. This paper studies heuristics that involve a decision maker who employs Bayesian inference. Analytical results are combined with simulations to gain insights into the relative performance of these heuristics. We depart from the extensive bandit-learning literature in computer science and operations research by employing the discounted-expected-reward formulation that stresses the importance of the classic exploration–exploitation tradeoff. A companion paper, Francetich and Kreps (2019), studies a variety of prior-free heuristics.

Keywords: Heuristics; Multi-armed bandits; Behavioral decision making (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:111:y:2020:i:c:s0165188918302689

DOI: 10.1016/j.jedc.2019.103814

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