When to Request Evidence?

2026-02-14

AI Summary

This study addresses the challenge of acquiring costly yet verifiable information through a strategically manipulative, preference-misaligned intermediary in a dynamic environment with exogenously determined decision timing. Integrating dynamic game theory and mechanism design, the paper investigates the optimal scheduling of evidence requests to enhance decision quality. The central finding is that the optimal request strategy exhibits state-dependent bias: when an interim report leans toward the intermediary’s preferred outcome, additional evidence should be solicited more frequently. This biased-request mechanism effectively curbs the intermediary’s incentive to manipulate reports and substantially outperforms unconditional evidence requests. Ignoring the intermediary’s strategic incentives renders decisions vulnerable to distortion, whereas incorporating such a bias-aware verification protocol significantly improves the accuracy of final decisions.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Appropriate decisions depend on information gathered beforehand, yet such information is often obtained through intermediaries with biased preferences. Motivated by settings such as testing and recertification in organ transplantation, we study the problem faced by a decision-maker who can only access costly information through an agent with misaligned preferences. In a dynamic framework with exogenous decision timing, we ask how requests for verifiable information (evidence) should be scheduled and their implications for the quality of attained choices. When the agent's incentives are ignored, evidence requests do not condition on previously reported information. However, such policies may be susceptible to strategic manipulation by the agent. We show that, in these cases, optimal requests should be biased: additional evidence is more likely to be sought when previous reports favor the agent's preferred outcome.

Problem

• evidence request
• strategic manipulation
• misaligned preferences
• dynamic decision-making
• information acquisition

Innovation

• evidence request
• misaligned incentives
• strategic manipulation
• dynamic decision-making
• information acquisition