Optimal Scoring for Dynamic Information Acquisition
Yingkai Li, Jonathan Libgober · 2024
This paper concerns the design of contracts to incentivize experts to acquire information for predicting a future state when doing so is costly and when this information acquisition is private. This problem may arise in a variety of situations, often when the contract designer expects to make a high-stakes decision---for instance, whether to launch a military attack in response to a perceived threat, whether to make a sizable investment, or whether to approve a risky vaccine to fight a budding pandemic. In these cases, whether a given action is best (e.g., invasion, investment, approval, respectively, for the above examples) may depend on the underlying state, which we will take as binary in this paper for simplicity. Notice that this problem features the combination of moral hazard (since the principal cannot monitor the expert's effort) and endogenous adverse selection (since the expert's actions generate private information).