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

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  • Francetich, Alejandro
  • Kreps, David

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.

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  • Francetich, Alejandro & Kreps, David, 2020. "Choosing a good toolkit, II: Bayes-rule based heuristics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:dyncon:v:111:y:2020:i:c:s0165188918302689
    DOI: 10.1016/j.jedc.2019.103814
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    References listed on IDEAS

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    1. Gigerenzer, Gerd & Todd, Peter M. & ABC Research Group,, 2000. "Simple Heuristics That Make Us Smart," OUP Catalogue, Oxford University Press, number 9780195143812.
    2. Francetich, Alejandro & Kreps, David, 2014. "Bayesian inference does not lead you astray…on average," Economics Letters, Elsevier, vol. 125(3), pages 444-446.
    3. Denis Sauré & Assaf Zeevi, 2013. "Optimal Dynamic Assortment Planning with Demand Learning," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 387-404, July.
    4. Alejandro Francetich & David Kreps, 2014. "Bayesian Inference Does Not Lead You Astray... On Average," Working Papers 514, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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    Cited by:

    1. Fudenberg, Drew & He, Kevin, 2021. "Player-compatible learning and player-compatible equilibrium," Journal of Economic Theory, Elsevier, vol. 194(C).

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