Artificial currencies have grown in popularity in many real-world resource allocation settings, gaining traction in government benefits programs like food assistance and transit benefits programs. However, such programs are susceptible to misreporting fraud, wherein users can misreport their private attributes to gain access to more artificial currency (credits) than they are entitled to. To address the problem of misreporting fraud in artificial currency based benefits programs, we introduce an audit mechanism that induces a two-stage game between an administrator and users. In our proposed mechanism, the administrator running the benefits program can audit users at some cost and levy fines against them for misreporting their information. For this audit game, we study the natural solution concept of a signaling game equilibrium and investigate conditions on the administrator budget to establish the existence of equilibria. The computation of equilibria can be done via linear programming in our problem setting through an appropriate design of the audit rules. Our analysis also provides upper bounds that hold in any signaling game equilibrium on the expected excess payments made by the administrator and the probability that users misreport their information. We further show that the decrease in misreporting fraud corresponding to our audit mechanism far outweighs the administrator spending to run it by establishing that its total costs are lower than that of the status quo with no audits. Finally, to highlight the practical viability of our audit mechanism in mitigating misreporting fraud, we present a case study based on the Washington D.C. federal transit benefits program. In this case study, the proposed audit mechanism achieves several orders of magnitude improvement in total cost compared to a no-audit strategy for some parameter ranges.

翻译：人工智能货币在现实资源分配场景中的应用日益普及，尤其在食品援助、交通补贴等政府福利项目中获得推广。然而，此类项目易受虚报欺诈威胁——用户可通过虚报个人属性信息获取超出应得额度的人工货币（积分）。针对基于人工智能货币的福利项目中的虚报欺诈问题，我们提出一种审计机制，通过构建管理员与用户间的两阶段博弈进行防控。在该机制中，运行福利项目的管理员可以承担一定成本对用户实施审计，并对虚报信息行为处以罚款。针对这一审计博弈，我们研究信号博弈均衡这一自然解概念，探究管理员预算约束下的均衡存在条件。通过合理设计审计规则，我们问题中的均衡计算可转化为线性规划问题。分析进一步给出了信号博弈均衡中管理员预期超额支付的上界，以及用户虚报信息概率的上界。我们证明，与无审计的现状相比，该审计机制减少虚报欺诈的效益远高于其运行成本。最终，为验证该审计机制在抑制虚报欺诈方面的实际可行性，我们基于华盛顿特区联邦交通福利项目开展案例研究。该案例表明，在特定参数区间内，所提审计机制的总成本较无审计策略可实现数个数量级的优化提升。