Allocating Resources under Strategic Misrepresentation

2026-03-04

AI Summary

This study addresses the efficient allocation of scarce resources when agents can strategically—and costly—inflate their qualifications. Integrating mechanism design theory, game theory, and large-market asymptotic analysis, the paper formulates and solves a resource allocation model featuring strategic signaling. It characterizes the incentive compatibility constraints and demonstrates that, in large markets, random allocation retains persistent value for agents of intermediate types. Moreover, when the number of participants is sufficiently large, a randomized mechanism based on costly signals strictly outperforms any deterministic mechanism, particularly under high scarcity, yielding significantly higher allocative efficiency. The optimal mechanism further converges toward a “winner-takes-all” contest structure as resource scarcity intensifies.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

We study how to allocate resources to participants who can strategically misrepresent their deservingness at a cost. A principal assigns item(s) (or money) among multiple agents on the basis of their costly signals. Each agent's signal reflects their private type in the absence of misrepresentation but can be inflated above their true type at a cost. The principal is a social planner who aims to maximize the weighted average of matching efficiency and a utilitarian objective. Strategic misrepresentation introduces novel incentive-compatibility constraints, under which we characterize the optimal mechanism. We apply our characterization to two kinds of markets, distinguished by resource scarcity, and show that the principal strictly benefits from randomizing the allocations based on costly signals when the population of participants is large enough. Interestingly, in large markets with scarce resources, the format of the optimal mechanism converges to a winner-takes-all contest; however, there is a non-diminishing value in randomizing allocations to middle types as the population of participants grows.

Problem

• resource allocation
• strategic misrepresentation
• incentive compatibility
• costly signaling
• mechanism design

Innovation

• strategic misrepresentation
• costly signaling
• optimal mechanism design
• randomized allocation
• large market asymptotics