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Pooya Molavi

Biographic Data

ID4973200
NAMEPooya Molavi
GIVEN NAMESPooya
FAMILY NAMEMolavi
SIGNATUREMOLAVI P
AFFILIATIONSUniversity of Pennsylvania
VERIFIEDNo
TOTAL WORKS4
TOTAL CITATIONS0
WORKS AS AUTHOR4
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2011
LATEST PUBLICATION YEAR2018
H-INDEX0
  • A Theory of Non-Bayesian Social Learning

    Open Access•Pooya Molavi, Alireza Tahbaz-Salehi et al.•ARTICLE•Econometrica•2018

    This paper studies the behavioral foundations of non-Bayesian models of learning over social networks and develops a taxonomy of conditions for information aggregation in a general framework. As our main behavioral assumption, we postulate that agents follow social learning rules that satisfy “imperfect recall,” according to which they treat the current beliefs of their neighbors as sufficient statistics for the entire history of their observatio…

  • Information Heterogeneity and the Speed of Learning in Social Networks

    Open Access•Ali Jadbabaie, Pooya Molavi et al.•ARTICLE•SSRN Electronic Journal•2013

  • Non-Bayesian social learning

    Open Access•Ali Jadbabaie, Pooya Molavi et al.•ARTICLE•Games and Economic Behavior•2012

  • 'Non-Bayesian Social Learning', Third Version

    Open Access•Ali Jadbabaie, Pooya Molavi et al.•PREPRINT•SSRN Electronic Journal•2011

No prominent works on this page.

  • 'Non-Bayesian Social Learning', Third Version

    Open Access•Ali Jadbabaie, Pooya Molavi et al.•PREPRINT•SSRN Electronic Journal•2011

  • Non-Bayesian social learning

    Open Access•Ali Jadbabaie, Pooya Molavi et al.•ARTICLE•Games and Economic Behavior•2012

  • Information Heterogeneity and the Speed of Learning in Social Networks

    Open Access•Ali Jadbabaie, Pooya Molavi et al.•ARTICLE•SSRN Electronic Journal•2013

  • A Theory of Non-Bayesian Social Learning

    Open Access•Pooya Molavi, Alireza Tahbaz-Salehi et al.•ARTICLE•Econometrica•2018

    This paper studies the behavioral foundations of non-Bayesian models of learning over social networks and develops a taxonomy of conditions for information aggregation in a general framework. As our main behavioral assumption, we postulate that agents follow social learning rules that satisfy “imperfect recall,” according to which they treat the current beliefs of their neighbors as sufficient statistics for the entire history of their observatio…

Artificial Intelligence (4 works) · Computer Science (4 works) · Game Theory and Applications (4 works) · Opinion Dynamics and Social Influence (4 works) · Bayesian probability (3 works) · Complex Network Analysis Techniques (3 works) · Economics (3 works) · Knowledge management (3 works) · Machine learning (3 works) · Social learning (3 works)

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