Among all tools used to understand collective human behavior, few tools have been
as successful as agent-based models (ABMs). These models have been particularly
effective at describing emergent social behavior, such as spatial segregation in
neighborhoods or opinion polarization on social networks. ABMs are particularly common
in ...
Among all tools used to understand collective human behavior, few tools have been
as successful as agent-based models (ABMs). These models have been particularly
effective at describing emergent social behavior, such as spatial segregation in
neighborhoods or opinion polarization on social networks. ABMs are particularly common
in the study of opinion and belief dynamics, being used by fields ranging from
anthropology to statistical physics. These models, much like the social systems they
describe, often do not have unique output variables, scales, or clear order parameters.
This lack of clearly measurable emergent behavior makes such complex ABMs difficult to
study, ultimately limiting their application to cases of empirical interest. In this paper, we
introduce a series of approaches to analyze complex multidimensional ABMs, drawing
from information theory and cluster analysis. We use these approaches to explore
a multi-level agent-based model of ideological alignment introduced by Banisch and
Olbrisch to extend Mäs and Flache’s argument communication theory of bi-polarization.
We use the tools introduced here to perform a thorough analysis of the model for small
system sizes, identifying the convergence toward steady-state behavior, and describing
the full spectrum of steady-state distributions produced by this model. Finally, we show
how the approach we introduced can be easily adapted for larger implementations, as
well as for other complex agent-based models of social behavior.