We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam\/phish filtering, and recommender systems. When the attacker is exogenous, we show that improving the filter’s quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements to filter quality may sometimes decrease equilibrium payoffs.<\/p>\n","protected":false},"excerpt":{"rendered":"
We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam\/phish filtering, and recommender systems. 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