设计最优审计策略，防止多主体谎报信息骗取资源
Optimally Auditing Adversarial Agents
- 将审计建模为多方代理博弈，主体制定策略应对集体谎报
- 提出高效算法求解适应与非适应场景下的最优审计方案
- 适用于信用审核、社会服务等需防欺诈的资源分配场景
欺诈在资源分配领域（如社会服务发放与信贷提供）构成挑战。例如，参与者可能隐瞒私人信息以获取利益或信贷资格。为缓解此问题，委托方可设计战略性审计来验证申报并惩罚虚假陈述。本文将审计策略设计建模为具有多个参与者的主代理博弈：委托方预先确定审计策略，而各参与者共同选择使委托方效用最小化的均衡策略。研究涵盖自适应与非自适应两种情形，取决于委托方策略是否能响应报告分布。本文提供了两类情形下计算最优审计策略的高效算法，并将结果扩展至审计预算受限的情形。
原文摘要 · Abstract (English)
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.
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