Modeling conflict in a social system using diffusion equations

In this paper the problem of modeling social conflicts of various types with the help of diffusion equations is discussed. The main approaches to and methods of mathematical modeling in contemporary humanitarian sciences are outlined. The main concepts of social conflicts, means of their classification and interpretation – including ethnic-social, religious, and other conflicts – are considered. The notion of a conflict in a social system is defined in terms of mathematical modeling. A model based on the Langevin diffusion equation is introduced. The model is based on the idea that all individuals in a society interact by means of a communication field. This field is induced by each individual in the society and forms informational interaction between individuals. An analytical solution of the system of equations is given in the first approximation for a diverging type of diffusion. It is shown that even for a simple case of the interaction of two groups of individuals the developed model makes it possible to discover characteristic laws of a conflict in a social system. It allows determining the effect of social distance in a society on the conditions of generation of such processes, with account of external effects or a random factor. Based on the analysis of the phase portraits for the given system, it has been concluded that there exists a stability region within which the social system is stable and non-conflicting.

[1]  Fernando Buendía,et al.  Self-organizing market structures, system dynamics, and urn theory , 2013, Complex..

[2]  Alexandr Y. Petukhov,et al.  The theory of information images: Modeling based on diffusion equations , 2016 .

[3]  R S Unger,et al.  Anatomy of a crisis. , 1989, Ontario dentist.

[4]  Lincoln P. Bloomfield,et al.  Managing international conflict : from theory to policy : a teaching tool using CASCON , 1997 .

[5]  Kristina Lerman,et al.  Spectral Clustering with Epidemic Diffusion , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Georgi Georgiev,et al.  Self-organization in non-equilibrium systems , 2015 .

[7]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[8]  S. E. Khaikin,et al.  Theory of Oscillations , 2015 .

[9]  L. Coser.  Two Methods in Search of a Substance , 1984 .

[10]  Frank Schweitzer,et al.  Phase transitions in social impact models of opinion formation , 2000 .

[11]  Mason A. Porter,et al.  Comparing Community Structure to Characteristics in Online Collegiate Social Networks , 2008, SIAM Rev..

[12]  Steven C Bankes,et al.  Agent-based modeling: A revolution? , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Jonathan W. D. Mason,et al.  Consciousness and the structuring property of typical data , 2012, Complex..

[14]  K. Boulding.  General Systems Theory---The Skeleton of Science , 1956 .

[15]  Jingjing Yang.  Functions of social conflict in tourism: tourism’s impacts on the Kanas Tuva and Kazakh Settlements, Xinjiang, China , 2012, European Journal of Tourism Research.

[16]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Michael J. Prietula,et al.  Simulating organizations: computational models of institutions and groups , 1998 .

[18]  Marie Weisz,et al.  Would Be Worlds How Simulation Is Changing The Frontiers Of Science , 2016 .