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Regret-Minimizing Contracts: Agency Under Uncertainty (2402.13156v2)

Published 20 Feb 2024 in cs.GT

Abstract: We study the fundamental problem of designing contracts in principal-agent problems under uncertainty. Previous works mostly addressed Bayesian settings in which principal's uncertainty is modeled as a probability distribution over agent's types. In this paper, we study a setting in which the principal has no distributional information about agent's type. In particular, in our setting, the principal only knows some uncertainty set defining possible agent's action costs. Thus, the principal takes a robust (adversarial) approach by trying to design contracts which minimize the (additive) regret: the maximum difference between what the principal could have obtained had them known agent's costs and what they actually get under the selected contract.

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References (33)
  1. Bayesian Analysis of Linear Contracts. In Proceedings of the 24th ACM Conference on Economics and Computation (London, United Kingdom) (EC ’23). Association for Computing Machinery, New York, NY, USA, 66. https://doi.org/10.1145/3580507.3597795
  2. Contracts with Private Cost per Unit-of-Effort. In Proceedings of the 22nd ACM Conference on Economics and Computation. 52–69.
  3. Combinatorial agency. Journal of Economic Theory 147, 3 (2012), 999–1034.
  4. Moshe Babaioff and Eyal Winter. 2014. Contract complexity. EC 14 (2014), 911.
  5. Regret-minimizing Bayesian persuasion. Games and Economic Behavior 136 (2022), 226–248.
  6. Learning Optimal Contracts: How to Exploit Small Action Spaces. arXiv preprint arXiv:2309.09801 (2023).
  7. Analysis of medicare pay-for-performance contracts. Available at SSRN 2839143 (2016).
  8. Gabriel Carroll. 2015. Robustness and linear contracts. American Economic Review 105, 2 (2015), 536–563.
  9. Gabriel Carroll. 2019. Robustness in mechanism design and contracting. Annual Review of Economics 11 (2019), 139–166.
  10. Bayesian agency: Linear versus tractable contracts. Artificial Intelligence 307 (2022).
  11. Designing menus of contracts efficiently: The power of randomization. Artificial Intelligence 318 (2023), 103881.
  12. Multi-Agent Contract Design: How to Commission Multiple Agents with Individual Outcomes (EC ’23). Association for Computing Machinery, New York, NY, USA, 412–448. https://doi.org/10.1145/3580507.3597793
  13. Learning Approximately Optimal Contracts. In Algorithmic Game Theory: 15th International Symposium, SAGT 2022, Colchester, UK, September 12–15, 2022, Proceedings (Colchester, United Kingdom). Springer-Verlag, Berlin, Heidelberg, 331–346.
  14. Lin William Cong and Zhiguo He. 2019. Blockchain disruption and smart contracts. The Review of Financial Studies 32, 5 (2019), 1754–1797.
  15. On Supermodular Contracts and Dense Subgraphs. In Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). SIAM, 109–132.
  16. Combinatorial contracts. In 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 815–826.
  17. Multi-agent contracts. In Proceedings of the 55th Annual ACM Symposium on Theory of Computing. 1311–1324.
  18. Combinatorial Contracts Beyond Gross Substitutes. In Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). SIAM, 92–108.
  19. Simple versus optimal contracts. In Proceedings of the 2019 ACM Conference on Economics and Computation. 369–387.
  20. The complexity of contracts. SIAM J. Comput. 50, 1 (2021), 211–254.
  21. Yuval Emek and Michal Feldman. 2012. Computing optimal contracts in combinatorial agencies. Theoretical Computer Science 452 (2012), 56–74.
  22. Optimal Coordination in Generalized Principal-Agent Problems: A Revisit and Extensions. arXiv preprint arXiv:2209.01146 (2022).
  23. Robust Stackelberg Equilibria. In Proceedings of the 24th ACM Conference on Economics and Computation (London, United Kingdom) (EC ’23). Association for Computing Machinery, New York, NY, USA, 735. https://doi.org/10.1145/3580507.3597680
  24. Teofilo F. Gonzalez. 1985. Clustering to minimize the maximum intercluster distance. Theoretical Computer Science 38 (1985), 293–306. https://doi.org/10.1016/0304-3975(85)90224-5
  25. Sanford J Grossman and Oliver D Hart. 1992. An analysis of the principal-agent problem. In Foundations of Insurance Economics: Readings in Economics and Finance. Springer, 302–340.
  26. The Power of Menus in Contract Design. In Proceedings of the 24th ACM Conference on Economics and Computation (London, United Kingdom) (EC ’23). Association for Computing Machinery, New York, NY, USA, 818–848. https://doi.org/10.1145/3580507.3597735
  27. Contracts under moral hazard and adverse selection. In EC ’21: The 22nd ACM Conference on Economics and Computation. 563–582.
  28. Learning in Online Principle-Agent Interactions: The Power of Menus. arXiv preprint arXiv:2312.09869 (2023).
  29. Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems. In Proceedings of the Fifteenth ACM Conference on Economics and Computation (Palo Alto, California, USA) (EC ’14). Association for Computing Machinery, New York, NY, USA, 359–376.
  30. Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems. Journal of Artificial Intelligence Research 55 (2016), 317–359.
  31. Daniel Walton and Gabriel Carroll. 2022. A General Framework for Robust Contracting Models. Econometrica 90, 5 (2022), 2129–2159.
  32. Yimin Yu and Xiangyin Kong. 2020. Robust contract designs: Linear contracts and moral hazard. Operations Research 68, 5 (2020), 1457–1473.
  33. The Sample Complexity of Online Contract Design (EC ’23). Association for Computing Machinery, New York, NY, USA, 1188.
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Authors (3)
  1. Martino Bernasconi (19 papers)
  2. Matteo Castiglioni (60 papers)
  3. Alberto Marchesi (45 papers)

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