Date of Award

9-13-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Operational Sciences

First Advisor

John O. Miller, PhD

Abstract

Opinion dynamics is the study of how opinions in a group of individuals change over time. A goal of opinion dynamics modelers has long been to find a social science-based model that generates strong diversity -- smooth, stable, possibly multi-modal distributions of opinions. This research lays the foundations for and develops such a model. First, a taxonomy is developed to precisely describe agent schedules in an opinion dynamics model. The importance of scheduling is shown with applications to generalized forms of two models. Next, the meta-contrast influence field (MIF) model is defined. It is rooted in self-categorization theory and improves on the existing meta-contrast model by providing a properly scaled, continuous influence basis. Finally, the MIF-Local Repulsion (MIF-LR) model is developed and presented. This augments the MIF model with a formulation of uniqueness theory. The MIF-LR model generates strong diversity. An application of the model shows that partisan polarization can be explained by increased non-local social ties enabled by communications technology.

AFIT Designator

AFIT-ENS-DS-18-S-044

DTIC Accession Number

AD1063478

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