Article
Social Conflicts Studied by Statistical Physics Approach and Monte Carlo Simulations
Proceedings (MDPI)
(2019)
Abstract
Statistical physics models of social systems with a large number of members,
each interacting with a subset of others, have been used in very diverse domains such as culture
dynamics, crowd behavior, information dissemination and social conflicts. We observe that such
models rely on the fact that large societal groups display surprising regularities despite individual
agency. Unlike physics phenomena that obey Newton’s third law, in the world of humans the
magnitudes of action and reaction are not necessarily equal. The effect of the actions of group n on
group m can differ from the effect of group m on group n. We thus use the spin language to describe
humans with this observation in mind. Note that particular individual behaviors do not survive in
statistical averages. Only common characteristics remain. We have studied two-group conflicts as
well as three-group conflicts. We have used time-dependent Mean-Field Theory and Monte Carlo
simulations. Each group is defined by two parameters which express the intra-group strength of
interaction among members and its attitude toward negotiations. The interaction with the other
group is parameterized by a constant which expresses an attraction or a repulsion to other group
average attitude. The model includes a social temperature T which acts on each group and quantifies
the social noise. One of the most striking features is the periodic oscillation of the attitudes toward
negotiation or conflict for certain ranges of parameter values. Other striking results include chaotic
behavior, namely intractable, unpredictable conflict outcomes.
Keywords
- social conflicts; statistical physics approach; complex systems; mean-field theory; Monte Carlo simulation
Disciplines
Publication Date
November 17, 2019
Citation Information
H. T. Diep, Miron Kaufman and Sanda Kaufman. "Social Conflicts Studied by Statistical Physics Approach and Monte Carlo Simulations" Proceedings (MDPI) Vol. 46 (2019) p. 4 Available at: http://works.bepress.com/miron_kaufman/67/