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Consensus and Disagreement: Information Aggregation under (not so) Naive Learning

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  • 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.

Suggested Citation

  • Abhijit Banerjee & Olivier Compte, 2022. "Consensus and Disagreement: Information Aggregation under (not so) Naive Learning," NBER Working Papers 29897, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29897
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    Cited by:

    1. is not listed on IDEAS
    2. Florian Mudekereza, 2025. "Collective Intelligence in Dynamic Networks," Papers 2502.12660, arXiv.org, revised Jun 2025.

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    JEL classification:

    • D0 - Microeconomics - - General

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