Overcoming communication restrictions in collectives

The performance of distributed systems generally depend on the actions and interactions of a large number of independent components (e.g., agents, neurons). Such "collectives" are often subject to communication restrictions, making it difficult for the components to coordinate their actions to provide good system level performance. In this article, we address that coordination problem and derive four agent utility functions that make different tradeoffs between alignedness between agent and system utilities and the signal-to-noise each agent encounters. The results show that these utility functions outperform both traditional methods and previous collective-based methods by up to 75% in systems with communication restrictions.