Complex of Description Models for Analysis and Control Group Behavior Based on Stochastic Cellular Automata with Memory and Systems of Differential Kinetic Equations

A. Smychkova, D. Zhukov
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Abstract

This paper considers the complex of models for the description, analysis, and modeling of group behavior by user actions in complex social systems. In particular, electoral processes can be considered in which preferences are selected from several possible ones. For example, for two candidates, the choice is made from three states: for the candidate A, for candidate B and undecided (candidate C). Thus, any of the voters can be in one of the three states, and the interaction between them leads to the transition between the states with some delay time intervals, which are one of the parameters of the proposed models. The dynamics of changes in the preferences of voters can be described graphically on diagram of possible transitions between states, on the basis of which is possible to write a system of differential kinetic equations that describes the process. The analysis of the obtained solutions shows the possibility of existence within the model, different modes of changing the preferences of voters. In the developed model of stochastic cellular automata with variable memory at each step of the interaction process between its cells, a new network of random links is established, the minimum and the maximum number of which is selected from a given range. At the initial time, a cell of each type is assigned a numeric parameter that specifies the number of steps during which will retain its type (cell memory). The transition of cells between states is determined by the total number of cells of different types with which there was interaction at the given number of memory steps. After the number of steps equal to the depth of memory, transition to the type that had the maximum value of its sum occurs. The effect of external factors (such as media) on changes in node types can set for each step using a transition probability matrix. Processing of the electoral campaign's sociological data of 2015–2016 at the choice of the President of the United States using the method of almost-periodic functions allowed to estimate the parameters of a set of models and use them to describe, analyze and model the group behavior of voters. The studies show a good correspondence between the data observed in sociology and calculations using a set of developed models. Under some sets of values of the coefficients in the differential equations and models of cellular automata are observed the oscillating and almost-periodic character of changes in the preferences of the electorate, which largely coincides with the real observations.
基于记忆随机元胞自动机和微分动力学方程组的群体行为分析与控制描述模型复合体
本文考虑了复杂社会系统中用户行为对群体行为的描述、分析和建模模型的复杂性。特别是,可以考虑从几个可能的偏好中选择偏好的选举过程。例如,对于两个候选人,从三个状态中进行选择:候选人A,候选人B和未决定(候选人C)。因此,任何选民都可以处于这三种状态中的一种,并且他们之间的相互作用导致状态之间以一定的延迟时间间隔进行转换,这是所提出模型的参数之一。选民偏好变化的动态可以用州与州之间可能发生的转变的图表来描述,在此基础上可以写出描述这一过程的微分动力学方程系统。对所得解的分析表明,在模型内可能存在改变选民偏好的不同模式。所建立的随机元胞自动机模型在单元间相互作用过程的每一步都具有可变记忆,该模型建立了一个新的随机链路网络,在给定范围内选择最小和最大数量的随机链路。在初始时,每种类型的单元被分配一个数字参数,该参数指定保留其类型(单元内存)的步数。细胞在不同状态之间的转换是由在给定的记忆步数下与之相互作用的不同类型细胞的总数决定的。在步数等于内存深度之后,转换到其总和最大的类型。外部因素(如媒体)对节点类型变化的影响可以使用转移概率矩阵为每个步骤设置。利用准周期函数的方法处理2015-2016年美国总统选举期间的选举活动社会学数据,可以估计一组模型的参数,并用它们来描述、分析和建模选民的群体行为。这些研究表明,在社会学中观察到的数据与使用一套开发模型的计算之间存在良好的对应关系。在元胞自动机的微分方程和模型的某些系数集合下,观察到选民的偏好变化具有振荡性和几乎周期性的特征,这与实际观察结果基本吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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