Algorithmic Audit Design Against Fraudulent Agents
A recent study by computer science experts presents a comprehensive framework for creating effective audit policies aimed at reducing fraud in areas like social services and credit allocation. This framework conceptualizes audit policy formulation as a principal-agent scenario involving several agents, where the principal establishes an audit policy, and agents work together to select an equilibrium that reduces the principal's utility. The research explores both adaptive and non-adaptive environments, depending on whether the principal can adjust their policy based on the agents' report distribution. The authors offer efficient algorithms for determining optimal audit strategies in both contexts and also adapt their findings to situations with restricted audit budgets. This research falls under the domains of Computer Science and Game Theory.
Key facts
- Fraud poses challenges in social service delivery and credit provision.
- Agents may misreport private information to gain benefits.
- A principal can design strategic audits to verify claims and penalize misreporting.
- The model is a principal-agent game with multiple agents.
- The principal commits to an audit policy; agents choose an equilibrium minimizing principal's utility.
- Both adaptive and non-adaptive settings are examined.
- Efficient algorithms compute optimal audit policies in both settings.
- Results extend to settings with limited audit budgets.
Entities
Institutions
- arXiv