We develop a model of data provision and data pricing in an environment with strategically interacting firms. The demand for data is generated by firms which seek to tailor their product positioning, or price, to either the individual or the aggregate demand. In turn, the data provider...

We find that sequential division of labor and deadlines help alleviate the free rider problem. Conversely,indivisibility of tasks, the number of partners, and asymmetries between agents are shown to increase the expected time to completion. The model may be applied to academic co-authorship, as...

A team must select among competing projects that differ in their payoff consequences for its members. Each agent chooses a project and exerts costly effort affecting its random completion time. When one or more projects are complete, agents bargain over which one to implement. A consensus...

We analyse strategic experimentation in which information arrives through fully revealing, publicly observable “breakdowns.” With hidden actions, there exists a unique equilibrium that involves randomization over stopping times. This randomization induces belief disagreement on the...

A monopolist sells informative experiments to heterogeneous buyers. Buyers differ in their prior information, and hence in their willingness to pay for additional signals. The monopolist can profitably offer a menu of experiments. We show that, even under costless information acquisition and...

This paper analyzes the impact of market structure on career concerns. Effort increases the probability that a skilled agent achieves a one-time breakthrough. Wages are based on assessed ability and on expected output. For any wage, the agent works too little, too late. Under short-term...

We develop a model of data pricing and targeted advertising. A monopolistic data provider determines the price to access "cookies," i.e., informative signals about individual consumers' preferences. The demand for information is generated by advertisers who seek to tailor their spending to the...