Working Paper: NBER ID: w29897
Authors: Abhijit Banerjee; Olivier Compte
Abstract: We explore a model of non-Bayesian information aggregation in networks. Agents non-cooperatively 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.
Keywords: Information Aggregation; Social Learning; Consensus; Polarization
JEL Codes: D0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Friedkin-Johnsen aggregation rule (C69) | disagreement under noisy conditions (D80) |
| DeGroot aggregation rule (C69) | consensus under noiseless conditions (D70) |
| transmission errors (L96) | weight on initial opinions (G41) |
| weight on initial opinions (G41) | greater divergence in beliefs (Z12) |
| optimal choice of aggregation rules (C43) | amplified disagreement (D74) |
| weights chosen for initial opinions (mi) (Y20) | insufficient aggregation of information (D80) |
| insufficient aggregation of information (D80) | excessive divergence (F12) |