 Choosing a good toolkit, I: Prior-free heuristicsAlejandro Francetich and David Kreps Journal of Economic Dynamics and Control, 2020, vol. 111, issue C Abstract: The dynamic problem of choosing subsets of objects or “toolkits” when their value distribution is unknown is a multi-armed bandit problem with non-independent arms. Accordingly, except for very simple specifications, this problem cannot (practically) be solved, either analytically or numerically. Decision makers facing this problem must resort to decision heuristics, employing past experience and, perhaps, what they know about the problem. This paper focuses on prior-free heuristics, the simpler and more naive end of the spectrum of heuristics where the decision maker is guided entirely by past experience. Prior-free heuristics can take a variety of forms, depending on the decision maker’s unit of analysis: tools or toolkits. We examine and compare different prior-free heuristics using both analytical methods and simulations. In a companion paper, Francetich and Kreps (2019), we examine heuristics in which the decision maker engages in Bayesian updating of her prior beliefs about the environment. Keywords: Heuristics; Multi-armed bandits; Reinforcement learning; Prior free; Behavioral decision making (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:111:y:2020:i:c:s0165188918302690 DOI: 10.1016/j.jedc.2019.103813 Access Statistics for this article Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok More articles in Journal of Economic Dynamics and Control from Elsevier |
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