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We explore a model of non-Bayesian information aggregation in networks.Agents non-cooperatively choose among Friedkin-Johnsen type aggregation rulesto maximize payoffs. The DeGroot rule is chosen in equilibrium if and only ifthere is noiseless information transmission, leading to consensus. With noisytransmission, while some disagreement is inevitable, the optimal choice of ruleamplifies the disagreement: even with little noise, individuals placesubstantial weight on their own initial opinion in every period, exacerbatingthe disagreement. We use this framework to think about equilibrium versussocially efficient choice of rules and its connection to polarization ofopinions across groups.

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