Optimal incentive contracts for teams must account for how individual effort affects not only overall output but also the incentives and productivity of other team members.
In two-stage contracts with non-myopic agents, achieving truthful play from the agent requires careful consideration of information asymmetry; while constant second-stage incentives are necessary for continuous type spaces, discrete type spaces allow for non-trivial truthful mechanisms, and introducing an adjustment mechanism can further enhance flexibility in incentive design.
Cutoff transfers, which provide a fixed reward only when the agent's report is sufficiently accurate, are optimal for incentivizing information acquisition across a wide range of settings, particularly when the signal distribution exhibits increasing elasticity above one.
This work explores online contract design from various perspectives, including heterogeneous and homogeneous agents, non-myopic behavior, and team production models.