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This work is a technical approach to modeling false information nature,design, belief impact and containment in multi-agent networks. We present aBayesian mathematical model for source information and viewer's belief, and howthe former impacts the latter in a media (network) of broadcasters and viewers.Given the proposed model, we study how a particular information (true or false)can be optimally designed into a report, so that on average it conveys the mostamount of the original intended information to the viewers of the network.Consequently, the model allows us to study susceptibility of a particular groupof viewers to false information, as a function of statistical metrics of thetheir prior beliefs (e.g. bias, hesitation, open-mindedness, credibilityassessment etc.). In addition, based on the same model we can study falseinformation "containment" strategies imposed by network administrators.Specifically, we study a credibility assessment strategy, where everydisseminated report must be within a certain distance of the truth. We studythe trade-off between false and true information-belief convergence using thisscheme which leads to ways for optimally deciding how truth sensitive aninformation dissemination network should operate.

A Bayesian Model for False Information Belief Impact, Optimal Design, and Fake News Containment