Collaboration dynamics of R&D teams: theory and experiment

I explore the dynamics of collaboration under incomplete information, focusing on the tension between the benefits and costs of collaboration. Specifically, I examine the case of two agents: one is an incumbent with well-known ability, and the other is an entrant with unobservable ability. If the incumbent’s incentive to free ride depends on the entrant’s possible types and learns the collaborator’s type based on history, then accumulating the reputation of being a high-ability type will lead to a breakup of the partnership. The breakup occurs because the incumbent’s incentive to free ride increases if the entrant accumulates a high enough reputation. I design an experiment to study the incumbent’s incentives to free ride after observing different paths of the entrant’s reputation building. As predicted by theory, I find that reputation-building might hinder collaboration.