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Title: Heterogeneity in human populations, from structure to personality--a modeling and data approach
Authors: Chu, Olivia Jessica
Advisors: Tarnita, Corina E
Contributors: Quantitative Computational Biology Department
Keywords: complex adaptive networks
social behavior
social networks
stochastic dynamics
Subjects: Applied mathematics
Social research
Issue Date: 2021
Publisher: Princeton, NJ : Princeton University
Abstract: In human social systems, instances of heterogeneity abound. From the individuallevel to the population level, we see numerous examples of variation at different scales of human organization. At the population level, there is heterogeneity in population structure: society is organized into groups, which determine who meets and interacts with whom. These groups can be associated with family, school, jobs, hobbies, politics, and more. When we zoom in on these groups and consider the interactions and dynamics that exist within them, we then see behavioral variation and personality differences at the individual level: our behavior and personality can not only influence and be influenced by those we encounter, but it can also vary depending on social context. Taking a step back, we can also ask how groups even form in the first place, as well as how people assimilate into existing groups. Individual-level heterogeneity in behavior can therefore affect groups on two levels: first, as groups form, and second, within existing groups. In this dissertation, I explore heterogeneity at both the individual and population levels. I study dynamic changes in behaviors, opinions, and network structure using a combination of mathematical modeling, computational simulations, and empirical data collection and analysis. In the first chapter of this dissertation, I present a model based on evolutionary set theory, in which individuals are distributed across groups and imitate others based on their success. I explore how realistic group entry rules influence the evolution of cooperation in this model. In the second chapter, I investigate how individual-level heterogeneity in distinctiveness preferences in social environments can lead to group formation using a model of opinion dynamics. I also propose a complementary data collection study to evaluate how groups form on university campuses and how students assimilate into campus communities. In the third chapter, I bring together elements of individual and population-level heterogeneity to study the dynamics of polarization, using survey data from before and after the Euromaidan Revolution of 2014. Overall, this dissertation aims to address a variety of questions concerning heterogeneity in human populations using modeling and data approaches.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Quantitative Computational Biology

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