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A cognitive hierarchy model of learning in networks

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Abstract

This paper proposes a method for estimating a hierarchical model of bounded rationality in games of learning in networks. A cognitive hierarchy comprises a set of cognitive types whose behavior ranges from random to substantively rational. Specifically, each cognitive type in the model corresponds to the number of periods in which economic agents process new information. Using experimental data, we estimate type distributions in a variety of task environments and show how estimated distributions depend on the structural properties of the environments. The estimation results identify significant levels of behavioral heterogeneity in the experimental data and overall confirm comparative static conjectures on type distributions across task environments. Surprisingly, the model replicates the aggregate patterns of the behavior in the data quite well. Finally, we found that the dominant type in the data is closely related to Bayes-rational behavior. Copyright Springer-Verlag 2012

Suggested Citation

  • Syngjoo Choi, 2012. "A cognitive hierarchy model of learning in networks," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 215-250, September.
  • Handle: RePEc:spr:reecde:v:16:y:2012:i:2:p:215-250
    DOI: 10.1007/s10058-012-0126-6
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    References listed on IDEAS

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    8. Agranov, Marina & Potamites, Elizabeth & Schotter, Andrew & Tergiman, Chloe, 2012. "Beliefs and endogenous cognitive levels: An experimental study," Games and Economic Behavior, Elsevier, vol. 75(2), pages 449-463.
    9. Nagel, Rosemarie, 1995. "Unraveling in Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 85(5), pages 1313-1326, December.
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    1. repec:eee:gamebe:v:106:y:2017:i:c:p:16-37 is not listed on IDEAS
    2. Agranov, Marina & Potamites, Elizabeth & Schotter, Andrew & Tergiman, Chloe, 2012. "Beliefs and endogenous cognitive levels: An experimental study," Games and Economic Behavior, Elsevier, vol. 75(2), pages 449-463.
    3. repec:eee:gamebe:v:104:y:2017:i:c:p:131-145 is not listed on IDEAS
    4. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2012. "Social learning in networks: a Quantal Response Equilibrium analysis of experimental data," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 135-157, September.
    5. Maria Cubel & Santiago Sanchez-Pages, 2016. "Gender differences and stereotypes in strategic thinking," UB Economics Working Papers 2016/338, Universitat de Barcelona, Facultat d'Economia i Empresa, UB Economics.
    6. Corazzini, Luca & Pavesi, Filippo & Petrovich, Beatrice & Stanca, Luca, 2012. "Influential listeners: An experiment on persuasion bias in social networks," European Economic Review, Elsevier, vol. 56(6), pages 1276-1288.
    7. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    8. María Cubel & Santiago Sanchez-Pages, 2014. "Gender differences and stereotypes in the beauty contest," Working Papers 2014/13, Institut d'Economia de Barcelona (IEB).

    More about this item

    Keywords

    Cognitive hierarchy; Bounded rationality; Social learning; Social networks; C51; C92; D82; D83;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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