Learning and Communication Towards Unanimous Consent

TL;DR

This paper analyzes how a principal and agent can achieve unanimous consent through optimal testing and communication, considering limited commitment and preference alignment, to improve decision-making and screening strategies.

Contribution

It characterizes optimal testing strategies under limited commitment, including binary, threshold, interval, and tail tests, and introduces a menu of tests for agent screening.

Findings

Binary tests are optimal under limited commitment.

Optimal tests depend on preference alignment and risk attitudes.

A simple menu of tests can effectively screen the agent's type.

Abstract

A principal and an agent can launch a project under unanimous consent. Their individual payoffs from the project depend on an underlying state, and the agent privately knows his own preference. The principal can conduct a test to learn about the state and then communicate with the agent, but has limited commitment, as she may misreport her findings. We show that limited commitment makes binary tests optimal. Moreover, when players' preferences are positively aligned, the optimal test is a threshold test. When their preferences are negatively aligned, the optimal test is either an interval test or a tail test, depending on the agent's relative risk attitude. Additionally, the principal can benefit from screening the agent through a menu of tests, which admits a simple structure regardless of the complexity of the agent's type space.