Coordination is a desirable feature in multi-agent systems, allowing the execution of tasks that would be impossible by individual agents. We study coordination by a team of strategic agents choosing to undertake one of the multiple tasks. We adopt a stochastic framework where the agents decide between two distinct tasks whose difficulty is randomly distributed and partially observed. We show that a Nash equilibrium with a simple and intuitive linear structure exists for diffuse prior distributions on the task difficulties. Additionally, we show that the best response of any agent to an affine strategy profile can be nonlinear when the prior distribution is not diffuse. Finally, we state an algorithm that allows us to efficiently compute a data-driven Nash equilibrium within the class of affine policies.

翻译：协调是多试剂系统中一个可取的特征,可以使个别代理人无法执行的任务。我们研究由选择执行其中一项任务的一组战略代理人进行协调。我们采用一个随机框架,由这些代理人在两种不同的任务之间作出决定,其困难是随机分布的,并部分观察到。我们显示,存在一种简单和直观的线性结构,可以分散先前的任务困难分配。此外,我们表明,任何代理人对一个亲子战略简介的最佳反应,在先前的分布不分散时,可以是非线性反应。最后,我们指出一种算法,使我们能够有效地计算出在亲子政策类别中以数据驱动的纳什平衡。