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Verification and Sharing in the Market for News




15: Management

Faculty Supervisor:

Gonzalo Cisternas

Faculty email:


Apply by:



Gonzalo Cisternas, gcistern@mit.edu; please include resumé and unofficial transcript

Project Description

Misinformation campaigns/efforts have a long history, but have recently gained considerable prominence thanks to advancements in the online world. The project is generally aimed at understanding “markets for news” – namely, the creation and consumption of information relevant for decision-making – that is mediated by online platforms (e.g., Facebook) when the possibility of fake news is a concern. The main innovation relative to the small emergent literature is to examine how individual incentives – those of the platform, users, and news producers – affect equilibrium outcomes such as the prevalence of fake news. For example, if one observes a piece of news in Facebook, one may choose to incur in personal efforts to verify it before sharing it. These verification choices depend both on how prevalent fake news are and on the quality of the platform’s mechanism to “filter” truthful from fake news. At the same time, the prevalence of fake news is affected by the aforementioned filter and the users sharing decisions. Will an improvement in a platform’s filter lead to less misinformation flowing through the network? To answer this question, we give a deeper look at the interplay between public (i.e., platform) and private (i.e., user) news filters. Our findings therefore intend to inform policies that alleviate fake news’ propagation in digital platforms while accounting for strategic behavior.


Previous exposure to Mathematica and Matlab are required. Knowledge of basic Microeconomics and Game Theory is preferable but not mandatory. The student will be asked to perform numerical simulations, review related literature, and potentially search for data to validate the model. Importantly, the student will work in close relationship with the supervisor, so this can result in a substantial -- and broad -- knowledge experience.