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on Contract Theory and Applications |
| By: | Jussi Keppo; Yingkai Li |
| Abstract: | This paper studies optimal contract design in private market investing, focusing on internal decision making in venture capital and private equity firms. A principal relies on an agent who privately exerts costly due diligence effort and then recommends whether to invest. Outcomes are observable ex post even when an opportunity is declined, allowing compensation to reward both successful investments and prudent decisions to pass. We characterize profit maximizing contracts that induce information acquisition and truthful reporting. We show that three tier contracts are sufficient, with payments contingent on the agent's recommendation and the realized return. In symmetric environments satisfying the monotone likelihood ratio property, the optimal contract further simplifies to a threshold contract that pays only when the recommendation is aligned with an extreme realized return. These results provide guidance for performance based compensation that promotes diligent screening while limiting excessive risk taking. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.19405 |
| By: | Marina Halac (Yale University); Elliot Lipnowski (Columbia University); Daniel Rappoport (University of Chicago) |
| Abstract: | We study the design of a contest in which a designer uses performance-contingent rankings and information disclosure to motivate an agent to exert effort. The agent's ability is unknown, and the designer's objective is to maximize the agent's expected effort. We show that the optimal ranking is a simple "pass-fail" rule, and the optimal information policy provides the agent with the minimum information necessary to keep them motivated. The results have implications for the design of workplace evaluations, academic grading, and other competitive environments where relative performance is used to incentivize effort. |
| Date: | 2025–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2480 |
| By: | Ernesto Rivera Mora (University of Colorado, Boulder); Philipp Strack (Yale University) |
| Abstract: | We study mechanism design for a sophisticated agent with non-expected utility (EU) preferences. We show that the revelation principle holds if and only if all types are EU maximizers: if at least one type is a non-EU maximizer, randomizing over dynamic mechanisms generates a strictly larger set of implementable allocations than using static mechanisms. Moreover, dynamic stochastic mechanisms can fully extract the private information of any type who doesn't have uniformly quasi-concave preferences without providing that type any rent. Full-surplus extraction is possible in a broad variety of non-EU environments, but impossible for types with concave preferences. |
| Date: | 2025–12–30 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2481 |
| By: | Laura Doval; Alex Smolin |
| Abstract: | We study mechanism design when a designer repeatedly uses a fixed mechanism to interact with strategic agents who learn from observing their allocations. We introduce a static framework, calibrated mechanism design, requiring mechanisms to remain incentive compatible given the information they reveal about an underlying state through repeated use. In single-agent settings, we prove implementable outcomes correspond to two-stage mechanisms: the designer discloses information about the state, then commits to a state-independent allocation rule. This yields a tractable procedure to characterize calibrated mechanisms, combining information design and mechanism design. In private values environments, full transparency is optimal and correlation-based surplus extraction fails. We provide a microfoundation by showing calibrated mechanisms characterize exactly what is implementable when an infinitely patient agent repeatedly interacts with the same mechanism. Dynamic mechanisms that condition on histories expand implementable outcomes only by weakening incentive compatibility and individual rationality--a distinction that vanishes in transferable utility settings. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.17858 |