Motivated Reasoning and Information Aggregation

2025-12-10

AI Summary

This paper investigates how motivated reasoning affects social information aggregation. Methodologically, it formalizes motivated reasoning within a unified framework and integrates it into two canonical models—Condorcet’s jury theorem and sequential social learning—employing asymptotic analysis, comparison against Bayesian rationality benchmarks, and state-dependent signal quality modeling. The study establishes, for the first time, a non-monotonic effect: in the jury model, strong motivation permits effective aggregation even in large groups; in sequential learning, moderate motivation enhances aggregation efficiency, whereas excessive motivation degrades performance. Theoretically, it demonstrates that motivated reasoning does not inherently undermine aggregation. It identifies a critical interaction between motivation strength and signal quality that jointly determines social welfare outcomes, and characterizes an optimal threshold of motivation strength that maximizes information aggregation efficiency.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Multiagent Systems: Mechanism Design
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Examines motivated reasoning's effect on societal information aggregation.
• Analyzes impact in Condorcet jury theorem and sequential learning models.
• Assesses welfare changes and aggregation limits with varying motivation levels.

Innovation

• Motivated reasoning defined for information aggregation models
• Large electorate limits still aggregate information effectively
• Optimal motivation balances truth-seeking and signal quality