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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.25085 |
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| _version_ | 1866914513029169152 |
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| author | Das, Sanmay Yu, Fang-Yi Zhang, Yuang |
| author_facet | Das, Sanmay Yu, Fang-Yi Zhang, Yuang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25085 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimally Auditing Adversarial Agents Das, Sanmay Yu, Fang-Yi Zhang, Yuang Computer Science and Game Theory Artificial Intelligence Computers and Society 68T05, 91A10 I.2.11; J.4 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. |
| title | Optimally Auditing Adversarial Agents |
| topic | Computer Science and Game Theory Artificial Intelligence Computers and Society 68T05, 91A10 I.2.11; J.4 |
| url | https://arxiv.org/abs/2604.25085 |