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Consensus and conflict are modelled in the context of interacting pairs of agents who may have very diverging sentiments regarding some particular issue. simulations using the model display character- istics of complexity. Agents are modelled using Beta probability density functions whose parameters determine the agent’s opinion and resistance to change after an interaction, and a third independent parameter that determines the agent’s influence. interactions among groups of agents with both aligned and opposing sentiments are simulated. the results indicate that in most cases a form of consensus is reached eventually, but for opposed agents, it is not possible to tell which agents that consensus will favour. proofs of convergence are given in the cases where the initial state is one of consensus, and when it is one of conflict.
beta distribution, conflict, consensus, convergence, influence, resistance, sentiment, simulation
[1] Rzevski, G. & Skobelev, P., Managing Complexity. WIT Press: Boston, 2014.
[2] Rzevski, G., A practical methodology for managing complexity. Emergence: Complexity & Organization, 13(1/2), pp. 38–56, 2011.
[3] Epstein, J.M., Agent-based computational models and generative social science. Complexity, 4(5), pp. 41–60. 1999. https://doi.org/10.1002/(sici)1099-0526(199905/06)4:5<41::aid-cplx9>3.3.co;2-6
[4] Epstein, J.M., Modeling civil violence: an agent-based computational approach. Proceedings of the National Academy of Sciences of the USA, 99, pp. 7243–7250, 2002. https://doi.org/10.1073/pnas.092080199
[5] Lemos, C., Coelho, H. & Lopes, R.J., Agent-based modeling of social conflict, civil violence and revolution: state-of-the-art-Review and further prospects. European Conference on Multi-Agent Systems (EUMAS), pp. 124–138, 2013.
[6] Granovetter, M., Threshold models of collective behavior. American Journal of Sociology, 3(6), pp. 1420–1443, 1978. https://doi.org/10.1086/226707
[7] Doran, J. Iruba: An Agent-Based Model of the Guerrilla War Process. Proceedings 3rd Conference of the European Social Simulation Association, Koblenz, pp. 198–205, 2005.
[8] Kim, J.W. & Hanneman, R.A., A computational model of worker protest. Journal of Artificial Societies and Social Simulation, 14(3), 2011. https://doi.org/10.18564/jasss.1770
[9] Gurr, T.R., Why Men Rebel, Princeton, 1970.
[10] Jones, A.J., Game Theory: Mathematical Models of Conflict. Horwood Publishing: West Sussex, 2000.
[11] Axtell, R.L., Epstein, J.M. & Young, H.P., The Emergence of Classes in a Multi-Agent Bargaining Model. Social Dynamics, eds. S.N. Durlauf & H.P. Young), MIT Press: West Sussex, 2001.
[10] W eisbuch, G., Persistence of discrimination: Revisiting Axtell, Epstein and Young, available at https://arxiv.org/pdf/1706.02573.pdf, 2017
[11] L iggett, T.M., Interacting Particle Systems, Springer, 1985
[12] Axelrod, R., Dissemination of culture: a model with local convergence and global polarization. Journal of Conflict Resolution, 41, pp. 203–226, 1997. https://doi.org/10.1177/0022002797041002001
[13] M iguel, M.S., Eguiluz, V.M., Toral, R. & Klemm, K., Binary and multivariate stochastic models of consensus formation. Computing in Science & Engineering, 7(5), pp. 67–73, 2005. https://doi.org/10.1109/mcse.2005.114
[14] Burt, G. Conflict, Complexity and Mathematical Social Science, Vol. 15, Emerald Group, 2010.
[15] N amatame, A. & Chen, S-H., Agent-based Modelling and Network Dynamics. Oxford University Press: Oxford, 2016.