We develop an agent-based simulation to formalize AI-human collaboration as a function of task structure, advancing a generalizable framework for strategic decision-making in organizations. Distinguishing between heuristic-based human adaptation and rule-based AI search, we model interactions across modular (parallel) and sequenced (interdependent) tasks using an NK model. Our results reveal that in modular tasks, AI often substitutes for humans - delivering higher payoffs unless human expertise is very high, and the AI search space is either narrowly focused or extremely broad. In sequenced tasks, interesting complementarities emerge. When an expert human initiates the search and AI subsequently refines it, aggregate performance is maximized. Conversely, when AI leads, excessive heuristic refinement by the human can reduce payoffs. We also show that even "hallucinatory" AI - lacking memory or structure - can improve outcomes when augmenting low-capability humans by helping escape local optima. These results yield a robust implication: the effectiveness of AI-human collaboration depends less on context or industry, and more on the underlying task structure. By elevating task decomposition as the central unit of analysis, our model provides a transferable lens for strategic decision-making involving humans and an agentic AI across diverse organizational settings.

本研究通过代理基础模拟，形式化了人工智能与人类的协作，解决了任务结构对决策的影响。新颖地将启发式人类适应和规则基础的AI搜索区分开来，发现根据任务的模块化和序列化特性，AI和人类的协作表现出不同的效果，提供了促进有效协作的条件与框架，具有重要的组织决策意义。

将人工智能与人类协作建模为多智能体适应