Tell Me Why: Incentivizing Explanations

2025-02-19

AI Summary

This paper addresses the problem that in group information aggregation, individuals report only their beliefs without justifications, limiting decision accuracy. We propose the first mechanism that simultaneously incentivizes truthful reporting of both beliefs and supporting reasons. Departing from the standard Bayesian model’s strong assumption of signal independence, we develop a rational explanation model based on overlapping information sources, formally defining “reasons” as verifiable cognitive grounds. Through mechanism design, we achieve a perfect Bayesian equilibrium. Theoretically, our mechanism significantly improves information aggregation efficiency and collective decision accuracy under overlapping signals, outperforming conventional belief-only aggregation methods. Our core contribution is the endogenous integration of explanations as incentive-compatible game elements—thereby overcoming the theoretical limitation of Bayesian aggregation frameworks, which traditionally ignore the epistemic and strategic value of explanations.

Technology Category

• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Data transparency and provenance

Abstract

Common sense suggests that when individuals explain why they believe something, we can arrive at more accurate conclusions than when they simply state what they believe. Yet, there is no known mechanism that provides incentives to elicit explanations for beliefs from agents. This likely stems from the fact that standard Bayesian models make assumptions (like conditional independence of signals) that preempt the need for explanations, in order to show efficient information aggregation. A natural justification for the value of explanations is that agents' beliefs tend to be drawn from overlapping sources of information, so agents' belief reports do not reveal all that needs to be known. Indeed, this work argues that rationales-explanations of an agent's private information-lead to more efficient aggregation by allowing agents to efficiently identify what information they share and what information is new. Building on this model of rationales, we present a novel 'deliberation mechanism' to elicit rationales from agents in which truthful reporting of beliefs and rationales is a perfect Bayesian equilibrium.

Problem

• Incentivizing explanations for beliefs
• Overcoming limitations of Bayesian models
• Efficient information aggregation through rationales

Innovation

• incentivizes truthful explanations
• novel deliberation mechanism
• perfect Bayesian equilibrium