Exploiting Myopic Learning

  • Mohamed Mostagir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6484)

Abstract

We show how a principal can exploit myopic social learning in a population of agents in order to implement social or selfish outcomes that would not be possible under the traditional fully-rational agent model. Learning in our model takes a simple form of imitation, or replicator dynamics; a class of learning dynamics that often leads the population to converge to a Nash equilibrium of the underlying game. We show that, for a large class of games, the principal can always obtain strictly better outcomes than the corresponding Nash solution and explicitly specify how such outcomes can be implemented. The methods applied are general enough to accommodate many scenarios, and powerful enough to generate predictions that allude to some empirically-observed behavior.

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References

  1. 1.
    Acemoglu, D., Bimpikis, K., Ozdaglar, A.: Communication Dynamics in Endogenous Social Networks. Working Paper (2010)Google Scholar
  2. 2.
    Acemoglu, D., Dahleh, M., Lobel, I., Ozdaglar, A.E.: Bayesian learning in social networks. NBER Working Paper (2008)Google Scholar
  3. 3.
    Borgers, T., Sarin, R.: Learning through reinforcement and replicator dynamics. Journal of Economic Theory 77(1), 1–14 (1997)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Crawford, V.P., Kugler, T., Neeman, Z., Pauzner, A.: Behaviorally Optimal Auction Design: Examples and Observations. Journal of the European Economic Association 7(2-3), 377–387 (2009)CrossRefGoogle Scholar
  5. 5.
    Eeckhout, J., Persico, N., Todd, P.: A Theory of Optimal Random Crackdowns. American Economic Review (2010)Google Scholar
  6. 6.
    Fischer, S., Vöcking, B.: On the evolution of selfish routing. In: Albers, S., Radzik, T. (eds.) ESA 2004. LNCS, vol. 3221, pp. 323–334. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Fudenberg, D., Levine, D.K.: The theory of learning in games. The MIT Press, Cambridge (1998)MATHGoogle Scholar
  8. 8.
    Fudenberg, D., Maskin, E.: The folk theorem in repeated games with discounting or with incomplete information. Econometrica: Journal of the Econometric Society 54(3), 533–554 (1986)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Kamenica, E., Gentzkow, M.: Bayesian persuasion. NBER Working Paper (2009)Google Scholar
  10. 10.
    Myerson, R.B.: Optimal auction design. Mathematics of operations research 6(1), 58 (1981)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Nisan, N., Ronen, A.: Algorithmic mechanism design. Games and Economic Behavior 35(1-2), 166–196 (2001)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mohamed Mostagir
    • 1
  1. 1.Social and Information Sciences LaboratoryCalifornia Institute of TechnologyUSA

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