Optimally Auditing Adversarial Agents
It addresses the problem of fraud in resource allocation domains by offering a principled approach to audit policy design, though the results are theoretical and domain-specific.
This paper introduces a general model for designing optimal audit policies in principal-agent games with multiple agents, providing efficient algorithms for both adaptive and non-adaptive settings, including under limited audit budgets.
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.