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IESE Research Papers,
IESE Business School
No D/1120: A General Model of Boundedly Rational Observational Learning: Theory and Experiment
Manuel Mueller-Frank () and Itai Arieliy ()
Additional contact information
Manuel Mueller-Frank: IESE Business School, Postal: IESE Business School. Research Division, Av Pearson 21, 08034 Barcelona, SPAIN
Itai Arieliy: Israel Institute of Technology, Postal: Haifa, 3200003, Israel
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
39 pages, February 27, 2015
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WP-1120-E.pdf
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