AI 中文总结
该研究构建含社会影响的二元选项集体决策模型，推导静态网络中选项占优条件、网络平均度的双重作用，分析动态网络结果的影响因素，通过模拟验证理论预测。
AI 中文摘要
集体决策在生物界和人工社会中普遍存在，个体通常会基于选项的内在价值做出选择，但也会受到邻居选择的影响，从而产生局部社会影响。因此，一个重要且尚未解决的问题随之而来：当这种社会影响被整合到个体对选项选择的评估过程中时，它如何影响由图建模的结构化种群中的集体决策结果？为解决该问题，我们考虑一个带有社会影响的二元选项基线模型，假设个体不仅评估选项的内在价值，还会受到邻居选择的影响，提出了整合这两个方面的感知效用函数用于个体决策。通过理论分析，我们首先推导了静态加权连通图上某一选项的平均频率，给出该选项在种群中占优的数学条件，发现引入社会影响可放大优势选项的优势或弥补劣势选项的不足，还揭示了网络平均度对集体决策结果具有双重作用。此外，我们考虑该演化模型在不同图配置间切换的动态网络上的情况，理论分析表明演化结果不仅取决于每种网络配置的平均度，还取决于其预期持续时间。我们在静态和动态网络上进行计算机模拟以验证理论预测。
英文摘要
Collective decision-making is ubiquitous across the living world and artificial societies. Individuals often choose an option based on intrinsic values of options. However, individual decision-making is also swayed by neighbors' choices, generating local social influence. Hence, an important question arises naturally, yet remains unanswered: when such social influence is integrated into the individual evaluation process for option choices, how does it affect collective decision-making outcomes in structured populations modeled by graphs. To address this, we consider a baseline model of binary options with social influence and assume that individuals not only evaluate the intrinsic values of options, but are also influenced by their neighbors' choices. We propose a perceived utility function integrating these two aspects for individual decision-making. By means of theoretical analysis, we first derive the average frequency of an option on static weighted connected graphs and present the mathematical condition under which this option prevails in the population. We find that the introduction of social influence can amplify the advantage of a superior option or compensate for the deficiency of an inferior one. We also reveal that the average degree of network exerts a dual effect on collective decision outcomes. Furthermore, we consider our evolutionary model on dynamic networks switching among distinct graph configurations. Our theoretical analysis shows that the evolutionary outcomes depend not only on the average degree of each network configuration, but also on its expected duration. We perform computer simulations to verify our theoretical predictions on static and dynamic networks.
Journal refInformation Fusion 137 (2027) 104568
DOI：10.1016/j.inffus.2026.104568
