Robust Trust

2026-02-10

AI Summary

This study addresses how a decision-maker (agent) should optimally combine private information with advice from a potentially misaligned advisor when the advisor’s recommendations are not fully credible. Focusing on a binary state–action environment, the paper introduces a “trust region” representation in belief space and integrates Bayesian inference, worst-case analysis, and convex optimization to fully characterize the structure of the optimal decision rule. A key contribution is the derivation of a critical threshold for the probability that the advisor’s interests align with those of the agent. The analysis demonstrates that whenever this alignment probability exceeds the threshold, the presence of the advisor strictly improves the agent’s worst-case expected payoff.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Adversarial Search
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

An agent chooses an action using her private information combined with recommendations from an informed but potentially misaligned adviser. With a known alignment probability, the adviser reports his signal truthfully; with remaining probability, the adviser can send an arbitrary message. We characterize the decision rule that maximizes the agent's worst-case expected payoff. Every optimal rule admits a trust region representation in belief space: advice is taken at face value when it induces a posterior within the trust region; otherwise, the agent acts as if the posterior were on the trust region's boundary. We derive thresholds on the alignment probability above which the adviser's presence strictly benefits the agent and fully characterize the solution in binary-state as well as binary-action environments.

Problem

• robust decision-making
• adviser misalignment
• trust region
• worst-case payoff
• strategic communication

Innovation

• trust region
• worst-case optimization
• adviser alignment
• robust decision-making
• Bayesian persuasion