A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives. We specialize the model to explore several applied questions, including whether compensation should reward individual ability or collaborativeness and how the strength of complementarities shapes pay dispersion.

翻译：委托人利用基于团队项目随机结果的支付来激励团队成员付出成本高昂的努力。我们借助网络博弈方法，发展了经典一阶契约优化方法的多智能体推广。主要结果刻画了激励薪酬在智能体与结果间的最优分配机制。激励最优性要求实现以下三项乘积在智能体间的均衡：(i)个体生产力 (ii)组织中心度 (iii)对货币激励的响应度。我们将模型具体化以探讨若干应用问题，包括薪酬设计应奖励个人能力还是协作能力，以及互补性强弱如何影响薪酬离散度。