arXiv:2601.01496v2 Announce Type: replace-cross Abstract: In this paper, we settle the problem of learning optimal linear contracts from data in the offline setting, where agent types are drawn from an unknown distribution and the principal's goal is to design a contract that maximizes her expected utility. Specifically, our analysis shows that the simple Empirical Utility Maximization (EUM) algorithm yields an $\varepsilon$-approximation of the optimal linear contract with probability at least $1-\delta$, using just $O(\ln(1/\delta) / \varepsilon^2)$ samples. This result improves upon previou
This research provides a theoretical advancement in the efficiency of designing optimal linear contracts, particularly relevant as AI systems increasingly manage complex economic interactions.
Improving the sample complexity for learning optimal contracts can lead to more efficient and equitable outcomes in principal-agent problems, impacting economic design and AI agent interactions.
The demonstrated efficiency of the EUM algorithm suggests that 'optimal' contract design can be achieved with significantly less data than previously assumed, potentially lowering barriers to deployment.
- · AI developers
- · Gig economy platforms
- · Automated contract systems
- · Economists
- · Inefficient contract design methods
- · Organizations relying on large, expensive datasets for optimization
More robust and data-efficient automated contract systems can be developed.
This could accelerate the adoption of autonomous AI agents in negotiation and resource allocation, as their incentive structures become easier to optimize.
Widespread use of optimally designed linear contracts might eventually alter labor markets and resource distribution by standardizing and automating incentive alignment.
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Read at arXiv cs.LG