This repository contains the simulation code accompanying the research paper on adaptive search dynamics across co-evolving systems using NKC models. The code implements the generation and analysis of rugged fitness landscapes under varying levels of internal complexity (K) and external coupling (C) across multiple interacting systems (N). The simulations explore how structural properties like modularity, complementarity, and interdependence affect system adaptability and fitness optimization. The code is designed for easy extension to more complex co-evolutionary search scenarios and can serve as a base for future work in organizational search, evolutionary computation, and multi-agent strategy modeling.
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 lowcapability 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.