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Choosing a Good Toolkit: An Essay in Behavioral Economics

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  • Alejandro Francetich
  • David M. Kreps

Abstract

The problemof choosing an optimal toolkit day after day,when there is uncertainty concerning the value of different tools that can only be resolved by carrying the tools, is a multi-armed bandit problem with nonindependent arms. Accordingly, except for very simple specifications, this optimization problem cannot (practically) be solved. Decision takers facing this problem presumably resort to decision heuristics, “sensible” rules fordeciding which tools to carry, based on past experience. In this paper, we examine and compare the performance of a variety of heuristics, some very simple and others inspired by the computer-science literature on these problems. Some asymptotic results are obtained, especially concerning the long-run outcomes of using the heuristics, hence these results indicate which heuristics do well when the discount factor is close to one. But our focus is on the relative performance of these heuristics for discount factors bounded away from one, which we study through simulation of the heuristics on a collection of test problems.

Suggested Citation

  • Alejandro Francetich & David M. Kreps, 2014. "Choosing a Good Toolkit: An Essay in Behavioral Economics," Working Papers 524, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:524
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    References listed on IDEAS

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    1. Blume, Lawrence E. & Easley, David, 1982. "Learning to be rational," Journal of Economic Theory, Elsevier, vol. 26(2), pages 340-351, April.
    2. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    3. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
    4. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, March.
    5. Milgrom, Paul & Roberts, John, 1991. "Adaptive and sophisticated learning in normal form games," Games and Economic Behavior, Elsevier, vol. 3(1), pages 82-100, February.
    6. Alejandro Francetich, 2014. "Managing Multiple Research Projects," Working Papers 516, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Simon, Herbert A, 1979. "Rational Decision Making in Business Organizations," American Economic Review, American Economic Association, vol. 69(4), pages 493-513, September.
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    Cited by:

    1. Francetich, Alejandro & Kreps, David, 2014. "Bayesian inference does not lead you astray…on average," Economics Letters, Elsevier, vol. 125(3), pages 444-446.

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