时变随机环境中的多智能体模型
Multiagent models in time-varying and random environment
摘要：
本文研究了具有时变类型变化的多智能体模型。假设存在根据内部系统的状态被分类为$r$类型的$N$代理的封闭系统;每个代理通过内部状态的内部动态或通过其他内部状态之间的不同内部状态的频率来改变其类型，例如，多项抽样利用非时齐马氏过程的弱收敛准则和第二类Volterra积分方程理论，研究了当$N$趋于无穷大时，主体类型的经验分布的渐近性态.我们还证明了这些模型在随机环境中演化的收敛定理。
英文摘要：
In this paper we study multiagent models with time-varying type change. Assume that there exist a closed system of $N$ agents classified into $r$ types according to their states of an internal system; each agent changes its type by an internal dynamics of the internal states or by the relative frequency of different internal states among the others, e.g., multinomial sampling. We investigate the asymptotic behavior of the empirical distributions of the agents' types as $N$ goes to infinity, by the weak convergence criteria for time-inhomogeneous Markov processes and the theory of Volterra integral equations of the second kind. We also prove convergence theorems of these models evolving in random environment.
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