Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning

Journal article: We explore a model of non-Bayesian information aggregation in networks. Agents noncooperatively choose among Friedkin-Johnsen-type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission, leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule amplifies the disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, exacerbating the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups.

Author(s)

Abhijit Banerjee, Olivier Compte

Journal
  • Journal of Political Economy
Date of publication
  • 2024
Pages
  • 2790-2829
Version
  • 1
Volume
  • 132