Multi-agent systems can be successfully described by kinetic models, which allow one to explore the large scale aggregate trends resulting from elementary microscopic interactions. The latter may be formalised as collision-like rules, in the spirit of the classical kinetic approach in gas dynamics, but also as Markov jump processes, which assume that every agent is stimulated by the other agents to change state according to a certain transition probability distribution. In this paper we establish a parallelism between these two descriptions, whereby we show how the understanding of the kinetic jump process models may be improved taking advantage of techniques typical of the collisional approach.

中文翻译：

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多智能体系统动力学描述中的马尔可夫跳跃过程和类碰撞模型

多智能体系统可以通过动力学模型成功地描述，它允许人们探索由基本微观相互作用产生的大规模聚集趋势。后者可以被形式化为类似碰撞的规则，本着气体动力学中经典动力学方法的精神，但也可以作为马尔可夫跳跃过程，它假设每个代理都受到其他代理的刺激以根据特定的转移概率改变状态分配。在本文中，我们建立了这两种描述之间的平行关系，由此我们展示了如何利用碰撞方法的典型技术来改进对动力学跳跃过程模型的理解。