FutureLab on Game Theory & Networks of Interacting Agents, Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412, Potsdam, Germany.
GESIS - Leibniz Institute for the Social Sciences, Member of the Leibniz Association, Unter Sachsenhausen 6-8, 50667, Cologne, Germany.
Sci Rep. 2020 Jul 8;10(1):11202. doi: 10.1038/s41598-020-67102-6.
Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Here, we refine Granovetter's widely acknowledged theoretical threshold model of collective behavior as a numerical modelling tool for understanding social tipping processes and resolve issues that so far have hindered such applications. Based on real-world observations and social movement theory, we group the population into certain or potential actors, such that - in contrast to its original formulation - the model predicts non-trivial final shares of acting individuals. Then, we use a network cascade model to explain and analytically derive that previously hypothesized broad threshold distributions emerge if individuals become active via social interaction. Thus, through intuitive parameters and low dimensionality our refined model is adaptable to explain the likelihood of engaging in collective behavior where social-tipping-like processes emerge as saddle-node bifurcations and hysteresis.
社会触发性，即少数人引发大多数人参与集体行动，被认为是应对当代全球挑战的一个关键方面。在这里，我们将 Granovetter 广泛认可的集体行为理论阈值模型细化为一种数值建模工具，用于理解社会触发性过程，并解决迄今为止阻碍此类应用的问题。基于现实世界的观察和社会运动理论，我们将人群分为确定的或潜在的参与者，因此——与最初的表述不同——该模型预测了具有非平凡最终行为个体的份额。然后，我们使用网络级联模型来解释和分析得出，如果个体通过社会互动变得活跃，那么之前假设的广泛阈值分布就会出现。因此，通过直观的参数和低维性，我们的改进模型可以适应解释社会触发性过程作为鞍结分岔和滞后出现时参与集体行为的可能性。
