A General Model of Boundedly Rational Observational Learning: Theory and Experiment

Manuel Mueller-Frank and Itai Arieliy ()
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Itai Arieliy: Israel Institute of Technology, Postal: Haifa, 3200003, Israel

No D/1120, IESE Research Papers from IESE Business School

Abstract: This paper introduces a new general model of boundedly rational observational learning: Quasi-Bayesian updating. The approach is applicable to any environment of observational learning and is rationally founded. We conduct a laboratory experiment and find strong supportive evidence for Quasi-Bayesian updating. We analyze the theoretical long run implications of Quasi-Bayesian updating in a model of repeated interaction in social networks with binary actions. We provide a characterization of the environment in which consensus and information aggregation is achieved. The experimental evidence is in line with our theoretical predictions. Finally, we establish that for any environment information aggregation fails in large networks.

Keywords: social networks; naive learning; bounded rationality; experiments; consensus; information aggregation (search for similar items in EconPapers)
JEL-codes: C91 C92 D83 D85 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2015-02-27
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ebg:iesewp:d-1120

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