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

S-Tier
Journal: Journal of Political Economy
Year: 2024
Volume: 132
Issue: 8
Pages: 2790 - 2829

Authors (2)

Score contribution per author:

4.022 = (α=2.01 / 2 authors) × 4.0x S-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

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.

Technical Details

RePEc Handle
repec:ucp:jpolec:doi:10.1086/729448
Journal Field
General
Author Count
2
Added to Database
2026-01-24