Complex Adaptive Systems : Computational Modeling and Simulation in the Social Sciences

Abstract

This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems (CAS) theory to unveil the underlying mechanisms of self-organization, nonlinear evolution, and emergence in social systems. By integrating mathematical models, agent-based modeling, network dynamic analysis, and hybrid modeling approaches, the study applies CAS theory to case studies in economic markets, political decision-making, and social interactions. The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena, such as market fluctuations, opinion polarization, and sudden outbreaks of social movements. This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture, but also offers valuable decision-support tools for public policy formulation, social governance, and risk management. Emphasizing the importance of interdisciplinary approaches, this work outlines future research directions in high-performance computing, artificial intelligence, and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.