Heterogeneous Bounds of Confidence: Meet, Discuss and Find Consensus!
Heterogeneous Bounds of Confidence: Meet, Discuss and Find Consensus!
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DOI：
10.1002/cplx.20295
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发表时间：
2010-03-01
期刊：
影响因子：
2.3
通讯作者：
Lorenz, Jan
中科院分区：
文献类型：
--
作者：
Lorenz, Jan
有界置信度下的连续意见动态模型表明，在一个关键的全球范围内的信心，共识和极化阶段之间的急剧转变。本文研究了置信度的异质界。令人惊讶的结果是，一个社会的代理人有两个不同的界限的信心（开放和封闭的思想代理人）可以找到共识，即使这两个界限的信心显着低于一个同质社会的关键界限的信心。基于代理的模拟和代理密度的时间演化的数值计算的例子所示的现象。该结果适用于Deffuant、Weisbuch和其他人的有界置信度模型（Weisbuch等人，Complexity 2002，7，55-63），以及Hegselmann和Krause的模型（Hegselmann和Krause，Journal of Artificial Societies and Social Simulation 2002，5，2）。因此，给定平均置信水平，置信界限的多样性增加了达成共识的机会。这种增强的缺点是，意见动态变得怀疑集群的严重漂移，其中开放的思想代理可以拉封闭的思想代理向另一个集群的封闭的思想代理。因此，最终的共识可能不像均匀置信度下的均匀初始意见分布那样位于意见区间的中心。它可以位于极端位置。通过实例证明了这一点，同时也表明，置信度的异质界的扩展极大地丰富了动力学的复杂性。(C)2009 Wiley Periodicals，Inc.复杂性15：43-52，2010
Models of continuous opinion dynamics under bounded confidence show a sharp transition between a consensus and a polarization phase at a critical global bound of confidence. In this paper, heterogeneous bounds of confidence are studied. The surprising result is that a society of agents with two different bounds of confidence (open-and closed-minded agents) can find consensus even when both bounds of confidence are significantly below the critical bound of confidence of a homogeneous society. The phenomenon is shown by examples of agent-based simulation and by numerical computation of the time evolution of the agents density. The result holds for the bounded confidence model of Deffuant, Weisbuch, and others (Weisbuch et al., Complexity 2002, 7, 55-63), as well as for the model of Hegselmann and Krause (Hegselmann and Krause, Journal of Artificial Societies and Social Simulation 2002, 5, 2). Thus, given an average level of confidence, diversity of bounds of confidence enhances the chances for consensus. The drawback of this enhancement is that opinion dynamics becomes suspect to severe drifts of clusters, where open-minded agents can pull closed-minded agents towards another cluster of closed-minded agents. A final consensus might thus not lie in the center of the opinion interval as it happens for uniform initial opinion distributions under homogeneous bounds of confidence. It can be located at extremal locations. This is demonstrated by example, which also show that the extension to heterogeneous bounds of confidence enriches the complexity of the dynamics tremendously. (C) 2009 Wiley Periodicals, Inc. Complexity 15: 43-52, 2010