Group Abstraction for Large-Scale Agent-Based Social Diffusion Models
Group Abstraction for Large-Scale Agent-Based Social Diffusion Models
复制标题
基于大规模代理的社会扩散模型的群体抽象
DOI：
10.1007/978-3-642-25044-6_12
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Jan Treur
中科院分区：
文献类型：
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作者：
A. Sharpanskykh;Jan Treur
在本文中，提出了一种方法来处理复杂的动态大规模多智能体系统建模社会扩散过程。基于个体代理及其连接的局部属性，识别组和这些组的动态属性。为了确定这样的动态组属性提出了两种抽象方法：确定一个组不变量和近似组过程的加权平均的相互作用。通过将组视为替代大量交互代理的单个实体，这使得能够在更抽象的级别上模拟多代理系统。通过这种方式，可以显著提高大规模仿真的可扩展性。所开发的方法的计算特性的文件中解决。该方法是说明了一个集体决策模型。
In this paper an approach is proposed to handle complex dynamics of large-scale multi-agents systems modelling social diffusion processes. Based on local properties of the individual agents and their connections, groups and dynamic properties of these groups are identified. To determine such dynamic group properties two abstraction methods are proposed: determining a group invariant and approximation of group processes by weighted averaging of interactions. This enables simulation of the multi-agent system at a more abstract level by considering groups as single entities substituting a large number of interacting agents. In this way the scalability of large-scale simulation can be improved significantly. Computational properties of the developed approach are addressed in the paper. The approach is illustrated for a collective decision making model.