Modeling AI–Human Collaboration as a Multi-Agent Adaptation

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 a high capability human initiates the search and AI subsequently refines it, joint 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. Our results highlight that the effectiveness of AI–human collaboration depends critically on the division of labor in decision-making. Contrary to the prevailing view that AI should first generate options for humans to evaluate, we show that joint performance can be higher when AI augments the prior search of a high-capability human on a rugged landscape of strategic choices.