Robust incentives for information acquisition

• 成果类型: Article
• 署名作者: Carroll, Gabriel
• 署名单位: Stanford University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2019.03.001
• 发表日期: 2019
• 页码: 382-420
• 关键词: information acquisition; Principal-expert problem; Restricted-investment contract; Robustness; Scoring rules; Worst case

摘要

A principal needs to make a decision, and contracts with an expert, who can obtain information relevant to the decision by exerting costly effort. The principal can incentivize effort by paying a reward based on the expert's reported information and on the true state of nature, which is revealed ex post. Both parties are financially risk-neutral, and payments are constrained by limited liability. The principal is uncertain about the expert's information acquisition technology: she knows some actions (experiments) that he can take to obtain information, but there may also be other experiments available. The principal seeks robustness to this uncertainty, and so evaluates any incentive contract using a worst-case criterion. Under quite general conditions, we show that the optimal contract is a restricted-investment contract, in which the expert chooses from a subset of the decisions available to the principal, and is then rewarded proportionally to the value of his designated decision in the realized state. (C) 2019 Elsevier Inc. All rights reserved.