Abstract
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.
Highlights
Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges
Global climate change has been frequently noted as one prominent contemporary social problem that could trigger and might be addressed through collective behaviour[12,13,14]. Empirical evidence for such complex contagion of interlinked individuals leading to collective action has been found for both online[15,16,17] and offline[18] social networks
It has been established above that the emergent thresholds follow from microscopic characteristics of each individual as well as its embedding in a social context. For the latter it will turn out that the share of others, i.e., the threshold fraction, that must join into an action before a contingent individual does so, too need not be widely distributed or even heterogeneous at all across the population in order to produce a widespread distribution for the emergent threshold. We study how such characteristics and interactions on the micro-level determine one’s emergent threshold by using a simulation model of social contagion that has been studied in the past to model binary decisions with externalities and resulting cascading dynamics[29]
Summary
Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Global climate change has been frequently noted as one prominent contemporary social problem that could trigger and might be addressed through collective behaviour (such as the emergent ‘Fridays for Future’[11] movement)[12,13,14] Empirical evidence for such complex contagion of interlinked individuals leading to collective action has been found for both online[15,16,17] and offline[18] social networks. Complementing empirical studies, recent conceptual models of complex contagion incorporate the spreading of an action, behaviour or trait through a complex network[22,23,24,25,26] They often aggregate an individual’s surrounding over time[27,28] or abstract space[29] to accumulate exposure to a considered trait such that at a certain point the individual adopts that trait as well. While by design the model is very flexible, it has mainly been used for illustrative and theoretical purposes (including most applications outlined above), but hardly applied as a numerical modeling tool
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