Date of Award

Fall 12-9-2021

Document Type

Honors Project

Degree Name

Bachelor of Science


Computer Science

Department Chair or Program Director

Anewalt, Karen

First Advisor

Stephen Davies

Major or Concentration

Computer Science


I present an agent-based model, inspired by the opinion dynamics
(OD) literature, to explore the underlying behaviors that may induce
societal polarization. My agents interact on a social network, in which
adjacent nodes can influence each other, and each agent holds an array
of continuous opinion values (on a 0-1 scale) on a number of separate
issues. I use three measures as a proxy for the virtual society’s “po-
larization:” the average assortativity of the graph with respect to the
agents’ opinions, the number of non-uniform issues, and the number
of distinct opinion buckets in which agents have the same opinions
after the model reaches an equilibrium.
I look at multiple model parameters that affect polarization. The
first is the density of edges in the network: this corresponds to the
average number of meaningful social connections that agents in a so-
ciety have. First, I find that lower edge density results in higher levels
of assortativity for Erd ̈os-R ́enyi graphs. The second is the level of
“openness” and “disgust” of agents to differing opinions; i.e., how
close or distant a neighboring node’s opinion on an issue must be to
an agent’s own before the agent will adjust its opinion on a different is-
sue. I refer to this novel mechanism as cross-issue influence. Through
this mechanism, I find that when agents in the model are less open
to new opinions, there will be less consensus on any given issue for
all agents in the model. Additionally, I find that there will be fewer
distinct opinion buckets and therefore higher polarization in models
where agents follow a cross-issue influence mechanism compared to
same-issue influence.

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