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

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

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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Publisher 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
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:jae:japmet:v:17:y:2002:i:3:p:225-247

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References listed on IDEAS
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John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA. [Downloadable!]
Other versions:
- Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier. [Downloadable!] (restricted)

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