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This paper formalizes the problem of designing optimal audit policies in resource allocation domains as a principal-agent game with multiple strategic agents. They develop efficient algorithms to compute optimal audit policies for both adaptive and non-adaptive settings, where adaptivity refers to the principal's ability to respond to the distribution of agent reports. The algorithms are further extended to handle scenarios with limited audit budgets, providing a practical approach to fraud mitigation.
Strategic auditing can be efficiently optimized, even when agents adapt their behavior to minimize the principal's utility.
Fraud can pose a challenge in many resource allocation domains, including social service delivery and credit provision. For example, agents may misreport private information in order to gain benefits or access to credit. To mitigate this, a principal can design strategic audits to verify claims and penalize misreporting. In this paper, we introduce a general model of audit policy design as a principal-agent game with multiple agents, where the principal commits to an audit policy, and agents collectively choose an equilibrium that minimizes the principal’s utility. We examine both adaptive and non-adaptive settings, depending on whether the principal's policy can be responsive to the distribution of agent reports. Our work provides efficient algorithms for computing optimal audit policies in both settings and extends these results to a setting with limited audit budgets.