Optimal Integration: Human, Machine, and Generative AI

• 成果类型: Article; Early Access
• 署名作者: Zhong, Hongda
• 署名单位: University of Texas System; University of Texas Dallas; Centre for Economic Policy Research - UK
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.07401
• 发表日期: 2025
• 关键词: Markov matrices; multilayer decision making; error reduction; automation; DELEGATION

摘要

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.