Optimally Auditing Adversarial Agents

TL;DR

This paper models audit policy design in principal-agent games with multiple agents, providing algorithms for optimal audits under various settings including budget constraints.

Contribution

It introduces a general framework for strategic audit policy design and offers efficient algorithms for computing optimal policies in adaptive and non-adaptive scenarios.

Findings

Algorithms for optimal audit policies in adaptive settings

Algorithms for optimal audit policies in non-adaptive settings

Extension of methods to budget-limited audit scenarios

Abstract

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.