The complexity of problem-solving human social systems: Structural vs dynamic complexity

Research in the social sciences often describes social complexity through a combination of structure, organization, and behavior within human social systems. In this paper, I argue that these aspects, while important, are conceptually distinct. Specifically, I distinguish between structural complexity—the organizational properties of a system—and dynamic complexity—the patterns of behavior and interaction within the system. To illustrate this distinction, I present three agent-based models of collective problem-solving: a hierarchical model, a random network model, and a hybrid of the two. These models are used to demonstrate how different forms of complexity can be measured and how they affect system performance. Several metrics are proposed to quantify structural and dynamic complexity, and model simulations show that the structurally complex hierarchical model is more efficient at solving problems than the dynamically complex network model. The simulations confirm the widespread intuition that systems with high structural complexity are effective for solving known problems, while systems with high dynamic complexity are more flexible. However, I also show that the hierarchical model is less robust against error than the network model. Finally, the proposed metrics provide a foundation for rigorous empirical research on the complexities of human social systems.Author summary: Human social systems, like organizations or societies, are complex. This research distinguishes between two key forms of complexity: structural complexity, which refers to how a system is organized, and dynamic complexity, which describes the unpredictability of how it behaves. These concepts are explored using three simple formal models: a hierarchical system, like a company with a clear chain of command, a network system, where individuals interact more freely, and a combination of the two, that might come closest to a real-world organization. The results show that systems with higher structural complexity are more efficient at solving familiar problems. However, they may struggle with new challenges due to their rigid structure. In contrast, more dynamic systems are flexible and adaptable, but less efficient and stable. This work highlights the trade-off between efficiency and robustness in human social systems. Systems designed for efficiency often lack adaptability, while those built for flexibility may face unpredictability. The findings help us understand how to design systems—whether organizations or societies—that can balance efficiency with the ability to cope with uncertainty and error.