Many platforms deploy data collected from users for a multitude of purposes. While some are beneficial to users, others are costly to their privacy. The presence of these privacy costs means that platforms may need to provide guarantees about how and to what extent user data will be harvested...

We study the design of optimal Bayesian data acquisition mechanisms for a platform interested in estimating the mean of a distribution by collecting data from privacy-conscious users. In our setting, users have heterogeneous sensitivities for two types of privacy losses corresponding to local...

With the increasing ease with which information can be shared in social media, the issue of privacy has become central for the functioning of various online platforms. In this paper, we consider how privacy concerns affect individual choices in the context of a network formation game (where...

This paper develops a model of Bayesian learning from online reviews, and investigates the conditions for asymptotic learning of the quality of a product and the speed of learning under different rating systems. A rating system provides information about reviews left by previous customers. A...

We study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines a social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. But...