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.

翻译：分布式系统的涌现行为受到局部决策者可用信息的制约。因此，人们可能预期向决策者提供更多信息将提升系统性能；然而本研究发现情况未必如此。在多智能体最大覆盖问题中，我们发现即使智能体的目标与全局福利一致，告知智能体资源随机价值的实现信息也可能使均衡性能降低至原来的1/2。这一发现印证了分布式系统设计的重要准则：信息需谨慎共享。我们通过界定信息（完全或部分）揭示与不揭示情况下系统福利比值的上下界（即信息价值），进一步深化了这一认知。随后我们识别出优化最佳情形与最差情形均衡性能时涌现的权衡关系。