Dynamic Evolution Of Social Sysyems Based On Game Theory

Recently, researches on the self-organized evolution of social system have received much attention, including new models and new evolution mechanisms emerging in large numbers. In this thesis, we introduced some game models into the investigation of the self-organized evolution of social systems. The main work is to investigate the influence of different network topologies and the related parameters of the game models on the self-organized evolution of social system. In chapter 1 of the thesis, we gave a brief review on the theory of self-organization, the research progress and several typical models of the social system. In chapter 2, we introduced the game models and network topologies. In chapter 3, 4 and 5, we studied the self-organized evolution in social system by using the Monte-Carlo numerical simulation method. The main results are as follows: (1) When the system reaches a steady state after a long time evolution, a few nodes in the network will gather a large number of particles, which satisfies the law of Pareto distribution; the distribution of particle quantities in those nodes with large particle size satisfies Zipf's law, which is in good agreement with the results by empirical analysis; (2) As compared with the evolution results under the regular network and small-world network, the self-organized evolution of social system under scale-free network will result in higher income,higher cooperation rate, and higher degree of particle aggregation; the self-organized evolution of social system under a certain network topology will also result in higher income, higher cooperation rate, and higher degree of particle aggregation by increasing the parameters of the management efficiency, the payoffs per unit time and the cooperation efficiency. Moreover, it is also found that the income of each strategy will be reduced with the increase of the risk coefficient. The summary and prospect is given in chapter 6.