Optimally Auditing Adversarial Agents
Game theory meets fraud detection in a model that maximizes audit impact.
Researchers Sanmay Das, Fang-Yi Yu, and Yuang Zhang have published a paper titled 'Optimally Auditing Adversarial Agents' in the Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2026. The work addresses fraud in resource allocation domains like social services and credit provision, where agents may misreport private information for personal gain. The authors model the problem 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.
The paper provides efficient algorithms for computing optimal audit policies in both adaptive (responsive to agent report distributions) and non-adaptive settings, and extends results to scenarios with limited audit budgets. This research offers a formal framework for designing strategic audits that verify claims and penalize misreporting, potentially improving fraud detection in high-stakes allocation systems.
- Model uses principal-agent game theory with multiple adversarial agents
- Efficient algorithms for both adaptive and non-adaptive audit policies
- Extends to limited audit budgets, published at AAAI 2026
Why It Matters
Optimized auditing could reduce fraud in social services and credit systems, saving billions annually.