ENTROPIC DYNAMICS IN SOCIETAL SYSTEMS: INTEGRATING SOCIAL PHYSICS, COMPUTATIONAL MODELING, AND STATISTICS FOR UNDERSTANDING SOCIAL CHANGE

Abstract

This dissertation delves into using entropy, a fundamental concept in thermodynamics and information theory, for analyzing social dynamics. Entropy relies on a probability distribution over states, which is crucial for quantifying social systems’ complexity, unpredictability, and self-organization behavior. Through an interdisciplinary approach encompassing social physics, agent-based modeling, and sentiment analysis, the research investigates the role of entropy and its underlying probability distribution in three key areas: residential segregation, financial systems, and sentiment fluctuations in online social networks. By integrating entropy-based models that leverage the probability distribution over states, the research aims to enhance the understanding of complex social phenomena and provide practical insights for policymakers, urban planners, and social media experts. The findings demonstrate the potential of entropy as a unifying framework for studying social sciences, economics, and digital social systems, highlighting the growing relevance of probability distributions in decoding patterns of social dynamics. The dissertation contributes to the theoretical basis for modeling and predicting the complexity of social networks using entropy and its associated probability distribution, with significant implications for various domains.