We quantify the e\ufb00ect of Bayesian ignorance by comparing the social cost obtained in a Bayesian game by agents with local views to the expected social cost of agents having global views. Both benevolent agents, whose goal is to minimize the social cost, and sel\ufb01sh agents, aiming at minimizing their own individual costs, are considered. When dealing with sel\ufb01sh agents, we consider both best and worst equilibria outcomes. While our model is general, most of our results concern the setting of network cost sharing (NCS) games. We provide tight asymptotic results on the e\ufb00ect of Bayesian ignorance in directed and undirected NCS games with benevolent and sel\ufb01sh agents. Among our \ufb01ndings we expose the counter-intuitive phenomenon that \u201cignorance is bliss\u201d: Bayesian ignorance may substantially improve the social cost of sel\ufb01sh agents. We also prove that public random bits can replace the knowledge of the common prior in attempt to bound the e\ufb00ect of Bayesian ignorance in settings with benevolent agents. Together, our work initiates the study of the e\ufb00ects of local vs. global views on the social cost of agents in Bayesian contexts.<\/p>\n","protected":false},"excerpt":{"rendered":"
We quantify the e\ufb00ect of Bayesian ignorance by comparing the social cost obtained in a Bayesian game by agents with local views to the expected social cost of agents having global views. Both benevolent agents, whose goal is to minimize the social cost, and sel\ufb01sh agents, aiming at minimizing their own individual costs, are considered. 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