Algorithmic Contract Theory: A Survey
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Abstract (arXiv)
A contract is an economic tool used by a principal to incentivize one or more agents to exert effort on her behalf, by defining payments based on observable performance measures. A key challenge addressed by contracts -- known in economics as moral hazard -- is that, absent a properly set up contract, agents might engage in actions that are not in the principal's best interest. Another common feature of contracts is limited liability, which means that payments can go only from the principal -- who has the deep pocket -- to the agents. With classic applications of contract theory moving online, growing in scale, and becoming more data-driven, tools from contract theory become increasingly important for incentive-aware algorithm design. At the same time, algorithm design offers a whole new toolbox for reasoning about contracts, ranging from additional tools for studying the tradeoff between simple and optimal contracts, through a language for discussing the computational complexity of contracts in combinatorial settings, to a formalism for analyzing data-driven contracts. This survey aims to provide a computer science-friendly introduction to the basic concepts of contract theory. We give an overview of the emerging field of "algorithmic contract theory" and highlight work that showcases the potential for interaction between the two areas. We also discuss avenues for future research.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Dec 2024.
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Citing papers per year: 2025: 6; 2026: 2.
8 papers.
- 2025Is Your LLM Overcharging You? Tokenization, Transparency, and Incentivescited by 13
- 2025Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providerscited by 5
- 2025The Pseudo-Dimension of Contractscited by 3
- 2025Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyondcited by 3
- 2025Learning to Play Multi-Follower Bayesian Stackelberg Gamescited by 2
- 2025Incentivizing High-Quality Human Annotations with Golden Questionscited by 0
- 2026The Optimal Sample Complexity of Linear Contractscited by 0
- 2026Adaptive Contracts for Cost-Effective AI Delegationcited by 0
- 2026Adaptive Contracts for Cost-Effective AI Delegation17 Mar 2026
- 2026The Optimal Sample Complexity of Linear Contracts4 Jan 2026
- 2025Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers2 Nov 2025
- 2025Learning to Play Multi-Follower Bayesian Stackelberg Games1 Oct 2025
- 2025Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives27 May 2025
- 2025Incentivizing High-Quality Human Annotations with Golden Questions25 May 2025
- 2025Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond3 Mar 2025
- 2025The Pseudo-Dimension of Contracts24 Jan 2025