Multiagent Maximum Coverage Problems: The Trade-off Between Anarchy and Stability

Networked systems are ubiquitous in today's world with examples spanning from ecology to the social and engineering sciences. Much of the research in networked systems is analytical, where the focus is on characterizing (and potentially influencing) the emergent collective behavior. A more recent trend of research focuses on the design of networked systems capable of achieving diverse and highly coordinated collective behavior in the absence of centralized control. Focusing on the well-studied class of maximum coverage problems, our first result demonstrates that any agent-based algorithm relying solely on local information induces a fundamental trade-off between the best and worst case performance guarantees, as measured by the price of anarchy and price of stability. Our second results demonstrates how to use an additional piece of system-level information to breach these limitations, thereby improving the system's performance.