We present a dynamic contracting model in which the principal and the agent disagree about the resolution of uncertainty, and we illustrate the contract design in an application with Bayesian learning. The disagreement creates gains from trade that the principal realizes by transferring payment to states that the agent considers relatively more likely, a shift that changes incentives. In our dynamic setting, the interaction between incentive provision and learning creates an intertemporal source of "disagreement risk" that alters optimal risk sharing. An endogenous regime shift between economies with small and large belief differences is present, and an early shock to beliefs can lead to large persistent differences in variable pay even after beliefs have converged. Under risk-neutrality, "selling the firm" to the agent does not implement the first-best outcome because it precludes state-contingent trades.

ABSTRACT Twelve trillion dollars are allocated to private market funds that require outside investors to commit to transferring capital on demand. We show within a novel dynamic portfolio allocation model that ex‐ante commitment has large effects on investors' portfolios and welfare, and we quantify those effects. Investors are underallocated to private market funds and are willing to pay a larger premium to adjust the quantity committed than to eliminate other frictions, like timing uncertainty and limited tradability. Perhaps counterintuitively, commitment risk premiums increase with secondary market liquidity, and they do not disappear when investments are spread over many funds.

We investigate optimal project management in a setting plagued by an indefinite number of setbacks that are discovered en route to project completion. The contractor can cover up delays in progress due to shirking either by making false claims of setbacks or by postponing the reports of real ones. The sponsor optimally induces work and honest reporting via a soft deadline and a reward for completion that specifies a bonus for early delivery. Late‐stage setbacks trigger randomization between minimally feasible project extension and (inefficient) cancellation. Because extensions may be granted repeatedly, arbitrarily large overruns in schedule and budget are possible after which the project may still be canceled.