A general model of boundedly rational observational learning: theory and evidence
Series
IESE Business School Working Paper
Type
working paper
Date Issued
2015-11-06
Author(s)
Abstract
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.
Language
English
Keywords
social networks
naive learning
bounded rationality
experiments
consensus
information aggregation
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
No
Number
WP-1120-E
Pages
46
Official URL
Subject(s)
Eprints ID
245262