Platforms for Efficient and Incentive-Aware Collaboration

Collaboration is crucial for reaching collective goals. However, its potential for 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., 2021a]. 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 across strategic agents using information design. Inspired by the increasingly important role collaboration plays in machine learning (such as platforms for collaborative federated learning and data cooperatives), we propose a framework in which the platform possesses more information about how the agents' tasks relate to each other than the agents themselves. Our results characterize how and to what degree such platforms can leverage their information advantage and steer strategic agents towards efficient collaboration.

Concretely, we consider collaboration networks in which each node represents 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. On the other hand, the real location of each agent in the network is known to the platform only -from the perspective of the agents, their location is determined by a uniformly random permutation. We employ the framework of private Bayesian persuasion and design two families of persuasive signaling schemes that the platform can use to guarantee a small total workload when agents follow the signal. The first family aims to achieve the minmax optimal approximation ratio compared to the total workload in the optimal collaboration, which is shown to be Θ( √ n) for unit-weight graphs, Θ(n 2 3 ) for graphs with edge weights lower bounded by Ω(1), and O(n 3 4 ) for general weighted graphs. The second family ensures per-instance strict improvement in the total workload compared to scenarios with full information disclosure.