Robust Trust

TL;DR

This paper develops a robust decision-making framework where an agent uses private information and potentially unreliable advice, employing trust-region policies to optimize worst-case outcomes in binary settings.

Contribution

It introduces a novel trust-region approach for robust decision rules that handle adviser misreporting and characterizes optimal policies in binary environments.

Findings

Optimal decision rules are trust-region policies in belief space.

Thresholds determine when advice is valuable.

Complete characterization in binary-state and binary-action cases.

Abstract

An agent chooses an action based on her private information and a recommendation from an informed but potentially misaligned adviser. With a known probability, the adviser truthfully reports his signal; with the remaining probability, he can send any message. We characterize optimal robust decision rules that maximize the agent's worst-case expected payoff. Every optimal rule is equivalent to a trust-region policy in belief space: the adviser's reported beliefs are taken at face value if they fall within the trust region but are otherwise clipped to the trust region's boundary. We derive alignment thresholds above which advice is strictly valuable and fully characterize the solution in both binary-state and binary-action environments.