Tell Me Why: Incentivizing Explanations

TL;DR

This paper introduces a novel mechanism that incentivizes agents to truthfully explain their beliefs, enhancing information aggregation by revealing shared and private information.

Contribution

It proposes a new deliberation mechanism that incentivizes truthful explanations, addressing the lack of mechanisms for eliciting explanations in Bayesian models.

Findings

The mechanism achieves perfect Bayesian equilibrium with truthful reporting.

Explanations improve the efficiency of information aggregation.

Agents can identify shared versus private information through explanations.

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.