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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

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  • 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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    19. Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 861-898.
    20. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2005. "Learning in Networks: An Experimental Study," Levine's Bibliography 122247000000000044, UCLA Department of Economics.
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    Citations

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    Cited by:

    1. Wright, James R. & Leyton-Brown, Kevin, 2017. "Predicting human behavior in unrepeated, simultaneous-move games," Games and Economic Behavior, Elsevier, vol. 106(C), pages 16-37.
    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. Martin, Daniel, 2017. "Strategic pricing with rational inattention to quality," Games and Economic Behavior, Elsevier, vol. 104(C), pages 131-145.
    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. Avoyan, Ala & Schotter, Andrew, 2020. "Attention in games: An experimental study," European Economic Review, Elsevier, vol. 124(C).
    6. Maria Cubel & Santiago Sanchez-Pages, 2016. "Gender differences and stereotypes in strategic thinking," UB School of Economics Working Papers 2016/338, University of Barcelona School of Economics.
    7. María Cubel & Santiago Sanchez-Pages, 2014. "Gender differences and stereotypes in the beauty contest," Working Papers 2014/13, Institut d'Economia de Barcelona (IEB).
    8. 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.
    9. Tony Haitao Cui & Yinghao Zhang, 2018. "Cognitive Hierarchy in Capacity Allocation Games," Management Science, INFORMS, vol. 64(3), pages 1250-1270, March.
    10. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    11. Larbi Alaoui & Antonio Penta, 2016. "Endogenous Depth of Reasoning," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1297-1333.
    12. María Cubel & Santiago Sanchez-Pages, 2014. "Gender differences and stereotypes in the beauty contest," Working Papers 2014/13, Institut d'Economia de Barcelona (IEB).

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    More about this item

    Keywords

    Cognitive hierarchy; Bounded rationality; Social learning; Social networks; C51; C92; D82; D83;
    All these keywords.

    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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