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Learning in a Black Box

Author

Listed:
  • Heinrich H. Nax

    (PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Maxwell N. Burton-Chellew

    (Department of Zoology [Oxford] - University of Oxford)

  • Stuart A. West

    (Department of Zoology [Oxford] - University of Oxford)

  • H. Peyton Young

    (Department of Economics - University of Oxford)

Abstract
Many interactive environments can be represented as games, but they are so large and complex that individual players are in the dark about what others are doing and how their own payo s are a ected. This paper analyzes learning behavior in such 'black box' environments, where players' only source of information is their own history of actions taken and payoff s received. Speci fically we study repeated public goods games, where players must decide how much to contribute at each stage, but they do not know how much others have contributed or how others' contributions a effect their own payoff s. We identify two key features of the players' learning dynamics. First, if a player's realized payoff increases he is less inclined to change his strategy, whereas if his realized payo ff decreases he is more inclined to change his strategy. Second, if increasing his own contribution results in higher payoff s he will tend to increase his contribution still further, whereas the reverse holds if an increase in contribution leads to lower payo ffs. These two e ffects are clearly present when players have no information about the game; moreover they are still present even when players have full information. Convergence to Nash equilibrium occurs at about the same rate in both situations.

Suggested Citation

  • Heinrich H. Nax & Maxwell N. Burton-Chellew & Stuart A. West & H. Peyton Young, 2013. "Learning in a Black Box," Working Papers hal-00817201, HAL.
    • Handle: RePEc:hal:wpaper:hal-00817201
    Note: View the original document on HAL open archive server: https://pjse.hal.science/hal-00817201v2
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    References listed on IDEAS

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    Cited by:

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    2. Mäs, Michael & Nax, Heinrich H., 2016. "A behavioral study of “noise” in coordination games," LSE Research Online Documents on Economics 65422, London School of Economics and Political Science, LSE Library.
    3. Nax, Heinrich H. & Murphy, Ryan O. & Helbing, Dirk, 2014. "Stability and welfare of 'merit-based' group-matching mechanisms in voluntary contribution game," LSE Research Online Documents on Economics 65444, London School of Economics and Political Science, LSE Library.
    4. Peiran Jiao & Heinrich H. Nax, 2016. "When is Market the Benchmark? Reinforcement Evidence from Repurchase Decisions," Economics Papers 2016-W01, Economics Group, Nuffield College, University of Oxford.
    5. Jean-François Laslier & Bernard Walliser, 2015. "Stubborn learning," Theory and Decision, Springer, vol. 79(1), pages 51-93, July.

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