Our project connects the distinct research areas of contracts and learning theory. For example: In “Deep Contract Design via Discontinuous Networks” we initiate the study of deep learning for the automated design of optimal contracts. We provide empirical results that demonstrate success in approximating the principal's utility function with a small number of training samples and scaling to find approximately optimal contracts on problems with a large number of actions and outcomes. In our work titled “Delegated Classification”, the starting point is that when machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance. In this work, we propose a contract-based, theoretical framework for incentive-aware delegation of machine learning tasks, and validate it empirically.
We also combine contracts with the study of information to achieve more realistic models: In “Algorithmic Cheap Talk”, we initiate the algorithmic study of natural communication among rational players, known as cheap talk. The next step is to apply this to communication among the contract designer and the agent(s). In “Information Design in the Principal-Agent Problem”, we allow the task delegator to get information about the agent's effort according to an “information structure” – a monitoring scheme designed to increase social welfare and lower losses from incomplete information.
Two works with high impact potential study (1) contracts for learning agents and (2) contracts for pricing AI services:
(1) As autonomous AI agents are increasingly used in various markets and computational systems, the classic analyses of contract theory may no longer be sufficient. The work “Contracting with a Learning Agent” initiates the study of repeated contracts with learning agents. We also provide the first analysis of optimization against learning agents with uncertainty about the time horizon. The paper has since sparked a series of follow-up works, suggesting that the model provides a useful foundation for a range of new questions at the interface of contract theory and learning.
(2) Our paper “Incentivizing Quality Text Generation via Statistical Contracts” challenges current pay-per-token pricing schemes of large language models (LLMs). From a practical perspective, the paper highlights and addresses moral hazard challenges that emerge in the deployment of large language models (LLMs), and applies contract design theory to design optimal pricing schemes which align incentives. From a theoretical perspective, the paper makes a foundational contribution by establishing a direct connection between the theory of optimal contract design and the theory of optimal hypothesis testing in statistics. “Loose” connections between moral hazard and hypothesis testing have been known for some time, but formal evidence has been limited. This work provides a rigorous direct correspondence between these two fields, which extends beyond the case of LLM pricing, and paves the way towards new results which combine ideas from statistics to solve economic problems, and vice versa.
