Reia Sandro M, Herrmann Sebastian, Fontanari José F
Instituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970 São Carlos, São Paulo, Brazil.
Department of Information Systems and Business Administration, Johannes Gutenberg-Universität, Jakob Welder-Weg 9, 55128 Mainz, Germany.
Phys Rev E. 2017 Feb;95(2-1):022305. doi: 10.1103/PhysRevE.95.022305. Epub 2017 Feb 9.
The solution of today's complex problems requires the grouping of task forces whose members are usually connected remotely over long physical distances and different time zones. Hence, understanding the effects of imposed communication patterns (i.e., who can communicate with whom) on group performance is important. Here we use an agent-based model to explore the influence of the betweenness centrality of the nodes on the time the group requires to find the global maxima of NK-fitness landscapes. The agents cooperate by broadcasting messages, informing on their fitness to their neighbors, and use this information to copy the more successful agents in their neighborhood. We find that for easy tasks (smooth landscapes), the topology of the communication network has no effect on the performance of the group, and that the more central nodes are the most likely to find the global maximum first. For difficult tasks (rugged landscapes), however, we find a positive correlation between the variance of the betweenness among the network nodes and the group performance. For these tasks, the performances of individual nodes are strongly influenced by the agents' dispositions to cooperate and by the particular realizations of the rugged landscapes.
解决当今复杂的问题需要组建特别行动小组,其成员通常在很长的实际距离和不同的时区进行远程连接。因此,了解强制通信模式(即谁能与谁通信)对小组绩效的影响很重要。在这里,我们使用基于代理的模型来探索节点的中介中心性对小组找到NK适应度景观全局最大值所需时间的影响。代理通过广播消息进行合作,向邻居通报自身的适应度,并利用这些信息模仿邻居中更成功的代理。我们发现,对于简单任务(平滑景观),通信网络的拓扑结构对小组绩效没有影响,并且越中心的节点越有可能首先找到全局最大值。然而,对于困难任务(崎岖景观),我们发现网络节点之间中介中心性的方差与小组绩效之间存在正相关。对于这些任务,单个节点的绩效受到代理合作倾向和崎岖景观特定实现的强烈影响。