Optimal Integration: Human, Machine, and Generative AI
Zhong proposes a stacking theory for humans, tools, and generative AI across decision stages. Each layer can fix mistakes yet add new ones. A quality metric ranks who goes later. It is correction capability divided by error propensity. High quality agents belong last. Their job is to avoid errors rather than slash existing ones. The model also predicts asymmetric effort. Applications include AI as final arbiter, delegation, screening. Limits include errors and no empirical calibration.
What it examines
I study multilayer decisions where each stage can fix prior mistakes but also add new ones. A theoretical model defines one quality metric—error-correction divided by new errors—and shows optimal ordering: place higher-quality agents later. The framework compares humans and generative AI and guides authority assignment across stages.
What it concludes
Results imply the last stage need not remove most errors; it should minimize new ones. Human effort skews earlier toward correction and later toward caution. Applications include AI governance, automation, loan screening, tenure review, and repeated delegation. Limitations: stylized assumptions. Future work: empirical validation, learning dynamics, hallucination mitigation.
Evidence objects
Zhong advances a layering theory where humans, tools, and generative AI sequentially correct yet may introduce errors. A quality metric, $q=\frac{\text{correction}}{\text{new-error}}$, uniquely orders stages: higher-quality agents optimally appear later downstream.
key_findings bullet 1 · key_findings · validation V0
Counterintuitively, highest-quality layers shouldnt maximize error cuts; their job is minimizing new errors. Model predicts asymmetric effort: early stages lighter, correction-focused; later heavier, caution-focused, with closed-form ordering and comparative statics.
key_findings bullet 2 · key_findings · validation V0
Actionable authority rule spans delegation, automation, loan screening, tenure review. Model suggests generative AI as final arbiterbroad correction, hallucination riskreducing human input. Limits: simplified errors, no calibration, measurement challenges, oversight.
key_findings bullet 3 · key_findings · validation V0
Introduces a general, multi-layer human--machine--GenAI decision framework with a one-dimensional quality metric $Q=\frac{\text{corrections}}{\text{new errors}}$ guiding sequencing and authority. It yields novel insights on asymmetric effort allocation and plausibly positions GenAI as final decision-maker despite hallucinations. Impactful for designing hedge-fund review/governance pipelines, yet contributions are theoretical, cross-domain, and incrementally novel only.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
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.
Source row: 1495 · abstract type: unknown