有界合同是否可学习且近似最优？
Are Bounded Contracts Learnable and Approximately Optimal?
AI总结：
本文在委托-代理隐藏行动模型中证明，在标准假设下可用多项式查询学习近乎最优的有界合同，并揭示其满足混合近似而非乘法或加法近似最优。
摘要：
本文考虑委托-代理问题中的隐藏行动模型，其中委托人利用合同激励代理人为项目工作。我们研究具有有界支付的合同是否可学习且近似最优。我们的主要结果是两个学习算法，它们能够在文献中的两个标准假设下，使用多项式数量的查询找到近乎最优的有界合同：代理人成本更高的行动会为委托人带来更好的结果分布，以及代理人的成本/努力具有边际收益递减。我们的多项式查询复杂度上界表明，标准假设足以在一般实例的已知下界基础上实现指数级改进。与依赖离散化合同空间的现有算法不同，我们的算法直接学习潜在的结果分布。关于有界合同的近似最优性，我们发现它们在乘法或加法近似意义上可能远非最优，但满足一种混合近似的概念。
英文摘要：
This paper considers the hidden-action model of the principal-agent problem, in which a principal incentivizes an agent to work on a project using a contract. We investigate whether contracts with bounded payments are learnable and approximately optimal. Our main results are two learning algorithms that can find a nearly optimal bounded contract using a polynomial number of queries, under two standard assumptions in the literature: a costlier action for the agent leads to a better outcome distribution for the principal, and the agent's cost/effort has diminishing returns. Our polynomial query complexity upper bound shows that standard assumptions are sufficient for achieving an exponential improvement upon the known lower bound for general instances. Unlike the existing algorithms, which relied on discretizing the contract space, our algorithms directly learn the underlying outcome distributions. As for the approximate optimality of bounded contracts, we find that they could be far from optimal in terms of multiplicative or additive approximation, but satisfy a notion of mixed approximation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
