异质偏差下的集体决策：网络拓扑与易感性的作用
Collective decision-making with heterogeneous biases: Role of network topology and susceptibility
AI总结：
该研究基于考虑异质偏好的自旋模型，探究网络拓扑向稀疏随机图的调整对集体决策的影响，分析两种易感性度量与网络连通性的关系，为集体系统适应环境波动提供思路。
摘要：
群体做出准确集体决策的能力取决于多种因素的复杂相互作用，例如先验信息、偏差、社会影响以及互动网络的结构。本文中，我们研究了一种自旋模型，该模型考虑了异质偏好，并且能够对社会互动的非线性进行调控。在已有关于完全图和规则二维晶格的研究结果基础上，我们探究了将网络拓扑修改为（稀疏）随机图会如何影响集体决策。我们采用两种不同的易感性度量指标，来评估系统对内部和外部扰动的响应能力。我们特别研究了易感性的最大值如何依赖于网络连通性。基于研究结果，我们讨论了集体系统如何通过调整自身网络结构或社会互动的性质，来适应环境波动的变化，从而维持在最大易感性区间内。
英文摘要：
The ability of groups to make accurate collective decisions depends on a complex interplay of various factors, such as prior information, biases, social influence, and the structure of the interaction network. Here, we investigate a spin model that accounts for heterogeneous preferences and enables control over the non-linearity of social interactions. Building on previous results for complete graphs and regular 2D lattices, we investigate how the modification of network topology towards (sparse) random graphs can affect collective decision-making. We use two different measures of susceptibility to assess the responsiveness of the system to internal and external perturbations. In particular, we investigate how the maximum of susceptibility depends on network connectivity. Based on our findings, we discuss how collective systems might adapt to changes in environmental fluctuations by adjusting their network structure or the nature of their social interactions in order to remain in the region of maximal susceptibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
