Optimal Integration: Human, Machine, and Generative AI
研究了多层决策中人类与技术的整合，提出按质量排序部署技术，生成式AI因生成能力更可能成为最终决策者，减少人力成本但存在幻觉风险。
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 was accepted by Will Cong, finance.