The Cost of Informing Decision-Makers in Multi-Agent Maximum Coverage Problems with Random Resource Values

TL;DR

This paper investigates how providing information to decision-makers in multi-agent maximum coverage problems can sometimes decrease overall system performance, highlighting the importance of careful information sharing.

Contribution

It reveals that informing agents about resource realizations can reduce equilibrium welfare and provides bounds on the value-of-informing in such systems.

Findings

Informing agents can halve the equilibrium performance.

Bounds on system welfare with different information levels are established.

Trade-offs exist between optimizing best-case and worst-case equilibria.

Abstract

The emergent behavior of a distributed system is conditioned by the information available to the local decision-makers. Therefore, one may expect that providing decision-makers with more information will improve system performance; in this work, we find that this is not necessarily the case. In multi-agent maximum coverage problems, we find that even when agents' objectives are aligned with the global welfare, informing agents about the realization of the resource's random values can reduce equilibrium performance by a factor of 1/2. This affirms an important aspect of designing distributed systems: information need be shared carefully. We further this understanding by providing lower and upper bounds on the ratio of system welfare when information is (fully or partially) revealed and when it is not, termed the value-of-informing. We then identify a trade-off that emerges when optimizing the performance of the best-case and worst-case equilibrium.