Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by a high Price of Stability (PoS) in recent literature [Blum et al., 2021]. Implicit in the traditional PoS analysis is the assumption that agents have full knowledge of how their tasks relate to one another. We offer a new perspective on bringing about efficient collaboration among strategic agents using information design. Inspired by the growing importance of collaboration in machine learning (such as platforms for collaborative federated learning and data cooperatives), we propose a framework where the platform has more information about how the agents' tasks relate to each other than the agents themselves. We characterize how and to what degree such platforms can leverage their information advantage to steer strategic agents toward efficient collaboration. Concretely, we consider collaboration networks where each node is a task type held by one agent, and each task benefits from contributions made in their inclusive neighborhood of tasks. This network structure is known to the agents and the platform, but only the platform knows each agent's real location -- from the agents' perspective, their location is determined by a random permutation. We employ private Bayesian persuasion and design two families of persuasive signaling schemes that the platform can use to ensure a small total workload when agents follow the signal. The first family aims to achieve the minmax optimal approximation ratio compared to the optimal collaboration, which is shown to be $\Theta(\sqrt{n})$ for unit-weight graphs, $\Theta(n^{2/3})$ for graphs with constant minimum edge weights, and $O(n^{3/4})$ for general weighted graphs. The second family ensures per-instance strict improvement compared to full information disclosure.

翻译：协作对于实现集体目标至关重要。然而，其有效性常因个体的策略性行为而受损——这一事实在近期文献[Blum et al., 2021]中通过高昂的稳定代价（Price of Stability, PoS）得以体现。传统PoS分析隐含的假设是，个体完全了解其任务之间的相互关联。我们提供了一种通过信息设计实现策略性个体间高效协作的新视角。受机器学习领域协作日益重要的启发（例如协作联邦学习平台和数据合作社平台），我们提出了一个框架，其中平台比个体自身更了解其任务之间的关联关系。我们刻画了此类平台如何以及能在多大程度上利用其信息优势，引导策略性个体走向高效协作。具体而言，我们考虑协作网络，其中每个节点代表某个个体持有的一种任务类型，且每个任务能从其包含的邻域任务中受益。该网络结构对个体和平台均为已知，但只有平台知晓每个个体的真实位置——从个体视角看，其位置由随机排列决定。我们采用私有贝叶斯说服策略，设计了两种平台可用的有说服力的信号方案，以确保个体遵循信号时总工作量较小。第一类方案旨在实现与最优协作相比的极小化最大近似比，证明该比值在单位权重图中为Θ(√n)，在最小边权为常数的图中为Θ(n^{2/3})，在一般加权图中为O(n^{3/4})。第二类方案确保相较于完全信息披露，每个实例均能获得严格改进。