We study multi-agent contracts, in which a principal delegates a task to multiple agents and incentivizes them to exert effort. Prior research has mostly focused on maximizing the principal's utility, often resulting in highly disparate payments among agents. Such disparities among agents may be undesirable in practice, for example, in standardized public contracting or worker cooperatives where fairness concerns are essential. Motivated by these considerations, our objective is to quantify the tradeoff between maximizing the principal's utility and equalizing payments among agents, which we call the price of non-discrimination. Our first result is an almost tight bound on the price of non-discrimination, which scales logarithmically with the number of agents. This bound can be improved to a constant by allowing some relaxation of the non-discrimination requirement. We then provide a comprehensive characterization of the tradeoff between the level of non-discrimination and the loss in the optimal utility.

翻译：本研究探讨多智能体合约问题，其中委托人将任务委托给多个智能体并通过激励机制促使其付出努力。现有研究主要聚焦于最大化委托人效用，这往往导致智能体间报酬差异悬殊。此类差异在实践中可能引发问题，例如在标准化公共合同或工人合作社等注重公平性的场景中。基于上述考量，本研究旨在量化委托人效用最大化与智能体报酬均等化之间的权衡关系，即非歧视性价格。我们的首要成果是建立了近乎紧致的非歧视性价格边界，该边界随智能体数量呈对数级增长。通过适度放宽非歧视性要求，该边界可提升至常数级别。最后，我们系统刻画了非歧视性水平与最优效用损失之间的权衡关系。