46 Pages Posted: 18 Feb 2015 Last revised: 1 Dec 2015
Date Written: August 18, 2015
This paper introduces a model of boundedly rational observational learning, which is rationally founded and applicable to general environments. Under Quasi-Bayesian updating each action is treated as if it were based only on the private information of its respective observed agent. We analyze the theoretical long run implications of Quasi-Bayesian updating in a model of repeated interaction in social networks with binary actions. We characterize the environments in which consensus and information aggregation is achieved and establish that for any environment information aggregation fails in large networks. Evidence from a laboratory experiment supports Quasi-Bayesian updating and our theoretical predictions.
Keywords: social networks, naive learning, bounded rationality, experiments, consensus, information aggregation
JEL Classification: C91, C92, D83, D85
Suggested Citation: Suggested Citation
Mueller-Frank, Manuel and Neri, Claudia, A General Model of Boundedly Rational Observational Learning: Theory and Evidence (August 18, 2015). IESE Business School Working Paper No. WP-1120-E. Available at SSRN: https://ssrn.com/abstract=2566210 or http://dx.doi.org/10.2139/ssrn.2566210