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Clever agents in adaptive learning

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  • Matros, Alexander

    (Dept. of Economics, Stockholm School of Economics)

Abstract

Saez-Marti and Weibull [4] investigate the consequences of letting some agents play a myopic best reply to the myopic best reply in Young's [8] bargaining model. This is how they introduce ''cleverness'' of players. We analyze such clever agents in general finite two-player games. We show that Young's [9] prediction is robust: adaptive learning with clever agents does select the same minimal curb set as in the absence of clever agents, if their population share is less than one. However, the long-run strategies distribution in such a curb set may vary with the share of clever agents.

Suggested Citation

  • Matros, Alexander, 2000. "Clever agents in adaptive learning," SSE/EFI Working Paper Series in Economics and Finance 403, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0403
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    References listed on IDEAS

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    1. Young H. P., 1993. "An Evolutionary Model of Bargaining," Journal of Economic Theory, Elsevier, vol. 59(1), pages 145-168, February.
    2. Basu, Kaushik & Weibull, Jorgen W., 1991. "Strategy subsets closed under rational behavior," Economics Letters, Elsevier, vol. 36(2), pages 141-146, June.
    3. Saez-Marti, Maria & Weibull, Jorgen W., 1999. "Clever Agents in Young's Evolutionary Bargaining Model," Journal of Economic Theory, Elsevier, vol. 86(2), pages 268-279, June.
    4. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    5. P. Young, 1999. "The Evolution of Conventions," Levine's Working Paper Archive 485, David K. Levine.
    6. Hurkens Sjaak, 1995. "Learning by Forgetful Players," Games and Economic Behavior, Elsevier, vol. 11(2), pages 304-329, November.
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    Cited by:

    1. Nax, Heinrich Harald & Newton, Jonathan, 2022. "Deep and shallow thinking in the long run," Theoretical Economics, Econometric Society, vol. 17(4), November.
    2. Andriy Zapechelnyuk, 2009. "Limit Behavior of No-regret Dynamics," Discussion Papers 21, Kyiv School of Economics.
    3. Abhimanyu Khan & Ronald Peeters, 2014. "Cognitive hierarchies in adaptive play," International Journal of Game Theory, Springer;Game Theory Society, vol. 43(4), pages 903-924, November.
    4. Josephson, Jens, 2009. "Stochastic adaptation in finite games played by heterogeneous populations," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1543-1554, August.
    5. Matros, Alexander, 2012. "Altruistic versus egoistic behavior in a Public Good game," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 642-656.
    6. Khan, Abhimanyu, 2021. "Evolutionary stability of behavioural rules in bargaining," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 399-414.
    7. Takako Fujiwara-Greve & Carsten Krabbe Nielsen, 2021. "Algorithms may not learn to play a unique Nash equilibrium," Journal of Computational Social Science, Springer, vol. 4(2), pages 839-850, November.
    8. Abhimanyu Khan, 2021. "Evolution of conventions in games between behavioural rules," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 9(2), pages 209-224, October.
    9. Alexander Matros, 2006. "Altruistic Versus Rational Behavior in a Public Good Game," Working Paper 309, Department of Economics, University of Pittsburgh, revised Sep 2008.

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    More about this item

    Keywords

    Evolution; game theory; bounded rationality; Markov chain; stochastic stability;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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