Adversarial Elicitation

TL;DR

This paper investigates the limits of information disclosure in strategic settings with multiple equilibria, proposing a worst-case optimal mechanism that improves upon full commitment by leveraging partial commitment and strategic message design.

Contribution

It introduces a worst-case implementation framework for cheap talk games, characterizes the optimal two-message mechanism, and highlights the strategic benefits of partial commitment in information elicitation.

Findings

Partial commitment improves the principal's worst-case payoff.

Optimal mechanism uses polarizing messages to influence uncommitted agents.

Results reveal a complementarity between automated and human decision-making.

Abstract

When multiple informative equilibria are possible in a general cheap talk game, how much information can a principal guarantee herself? To answer this question, I define the notion of worst-case implementation-implementation via the worst non-trivial equilibrium of a mechanism. Under this objective, standard full-commitment mechanisms fail, yielding the principal no more than her no-communication payoff. Partial commitment, however, can provide a strict improvement. The possibility of facing a strategic, uncommitted principal disciplines the agent's reporting incentives across all equilibria. I characterize the worst-case optimal mechanism and payoff under weak assumptions on the players' preferences. The optimal mechanism has a simple two-message structure. The agent's messages are polarizing, designed to maximize their strategic impact on the uncommitted principal's actions. If full commitment is interpreted as decision automation, these results highlight a fundamental complementarity between automated and human decision-makers: the presence of a human aligns the agent's incentives to reveal information, while the automated system leverages these informative reports to take accurate actions. This strategic interaction is often overlooked by literature that compares the two based on standalone decision accuracy. Applications of the model include bail-setting automation, fintech lending, delegation, lobbying, and audit design.