Keywords

SOCIAL PHYSICS, COMPUTATIONAL MODELING, ENTROPIC, 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 ex- parts. 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.

Completion Date

2024

Semester

Summer

Committee Chair

Dr. Alexander Mantzaris Dr. Mengyu Xu Dr. Larry Tang Dr. Ozlem Garibay

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Statistics and Data science

Degree Program

Big Data Analytics

Format

application/pdf

Release Date

8-15-2029

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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Restricted to the UCF community until 8-15-2029; it will then be open access.

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