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Learning and communication in sender-receiver games: an econometric investigation

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Author Info

  • Andreas Blume

    (Department of Economics, University of Pittsburgh, USA)

  • Douglas V. DeJong

    (Tippie College of Business, University of Iowa, Iowa City, IA 52242 USA)

  • George R. Neumann

    (Tippie College of Business, University of Iowa, Iowa City, IA 52242 USA)

  • N. E. Savin

    (Tippie College of Business, University of Iowa, Iowa City, IA 52242 USA)

Abstract

This paper compares stimulus response (SR) and belief-based learning (BBL) using data from experiments with sender-receiver games. The environment, extensive form games played in a population setting, is novel in the empirical literature on learning in games. Both the SR and BBL models fit the data reasonably well in games where the preferences of senders and receivers are perfectly aligned and where the population history of the senders is known. The test results accept SR and reject BBL in games without population history and in all but one of the games where senders and receivers have different preferences over equilibria. Estimation is challenging since the likelihood function is not globally concave and the data become uninformative about learning once equilibrium is achieved. Copyright © 2002 John Wiley & Sons, Ltd.

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File URL: http://qed.econ.queensu.ca:80/jae/2002-v17.3/
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 17 (2002)
Issue (Month): 3 ()
Pages: 225-247

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Handle: RePEc:jae:japmet:v:17:y:2002:i:3:p:225-247

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References

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  1. Rabin, Matthew, 1990. "Communication between rational agents," Journal of Economic Theory, Elsevier, vol. 51(1), pages 144-170, June.
  2. Blume, Andreas, et al, 1998. "Experimental Evidence on the Evolution of Meaning of Messages in Sender-Receiver Games," American Economic Review, American Economic Association, vol. 88(5), pages 1323-40, December.
  3. A. Blume & Y. G. Kim & J. Sobel, 2010. "Evolutionary Stability in Games of Communication," Levine's Working Paper Archive 530, David K. Levine.
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  19. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
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Citations

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Cited by:
  1. Ed Hopkins, 2006. "Adaptive Learning Models of Consumer Behaviour," Levine's Bibliography 122247000000000658, UCLA Department of Economics.
  2. Ed Hopkins, 2004. "Two Competing Models of How People Learn in Games," ESE Discussion Papers 51, Edinburgh School of Economics, University of Edinburgh.
  3. Battaglini, Marco & Makarov, Uliana, 2014. "Cheap talk with multiple audiences: An experimental analysis," Games and Economic Behavior, Elsevier, vol. 83(C), pages 147-164.
  4. John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA.
  5. Antonio Cabrales & Walter Garcia Fontes, 2000. "Estimating learning models from experimental data," Economics Working Papers 501, Department of Economics and Business, Universitat Pompeu Fabra.
  6. Andreas Blume & John Duffy & April Mitchell Franco, 2008. "Decentralized Organizational Learning: An Experimental Investigation," Working Papers 382, University of Pittsburgh, Department of Economics, revised May 2009.
  7. Mitropoulos, Atanasios, 2003. "An experiment on the value of structural information in a 2 x 2 repeated game," Economics Letters, Elsevier, vol. 78(1), pages 27-32, January.

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