Motivated Reasoning and Information Aggregation

TL;DR

This paper examines how motivated reasoning influences information aggregation in society, showing that it can improve or impair collective decision-making depending on the context and degree of motivation.

Contribution

It introduces a formal notion of motivated reasoning applicable to key social learning models and analyzes its impact on information aggregation and welfare.

Findings

Information still aggregates in large electorates despite motivated reasoning.

Motivated reasoning can improve welfare when signals are more informative.

Excessive motivated reasoning can hinder optimal information aggregation.

Abstract

If agents engage in motivated reasoning, how does that affect the aggregation of information in society? We study the effects of motivated reasoning in two canonical settings - the Condorcet jury theorem (CJT), and the sequential social learning model (SLM). We define a notion of motivated reasoning that applies to these and a broader class of other settings, and contrast it to other approaches in the literature. We show for the CJT that information aggregates in the large electorate limit even with motivated reasoning. When signal quality differs across states, increasing motivation improves welfare in the state with the more informative signal and worsens it in the other state. In the SLM, motivated reasoning improves information aggregation up to a point; but if agents place too little weight on truth-seeking, this can lead to worse aggregation relative to the fully Bayesian benchmark.