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

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