中文摘要
本文研究多层决策过程中人类与技术的最优整合问题。当每一层都能纠正已有错误、但也可能引入新错误时，谁应当拥有最终决定权？本文表明，将决策者的纠正能力以其新错误数进行标准化后，可得到一个一维质量指标，该指标决定了最优规则：将更高质量的技术部署在较后阶段。有趣的是，尽管最终层的质量最高，它未必带来最大的错误减少；相反，其作用取决于能否最小化新错误。人类的努力程度在各层之间呈非对称变化：较早阶段付出相对较低的努力并优先纠正错误，而较后阶段付出更高的努力并着重避免新错误。将模型应用于人工智能（AI）可发现，AI的生成能力使其更有可能充当最终决策者，从而减少对成本高昂的人类投入的需求，但代价是存在AI幻觉的风险。该理论框架还可推广至重复授权、自动化设计、贷款筛选、终身教职评审及其他多层决策情境等应用。本文已被Will Cong接收，用于“AI for Business and Finance Decisions”虚拟专刊。
Abstract
I study the optimal integration of humans and technologies in multilayered decision-making processes. When each layer can correct existing errors but may also introduce new errors, who should have the final authority? I show that a decision maker’s correction capability normalized by its new errors is a one-dimensional quality metric that determines the optimal rule: deploying higher quality technologies in later stages. Intriguingly, despite its highest quality, the final layer may not generate the greatest error reduction; instead, its role hinges on minimizing new errors. Human effort varies asymmetrically across layers: early stages exert relatively lower effort and prioritize error correction, whereas later stages exert higher effort and focus on avoiding new errors. Applying the model to artificial intelligence (AI) reveals that AI’s generative capabilities make it more likely to serve as the final decision maker, reducing the need for costly human input at the risks of AI hallucination. The theoretical framework also extends to applications including repeated delegation, automation design, loan screening, tenure review, and other multilayer decision-making scenarios. This paper has been accepted by Will Cong for the Virtual Special Issue on AI for Business and Finance Decisions.