Early warning signals from the periphery

Manfred Füllsack (), Daniel Reisinger, Marie Kapeller and Georg Jäger
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Manfred Füllsack: Innovation and Sustainability Research at the University of Graz
Daniel Reisinger: Innovation and Sustainability Research at the University of Graz
Marie Kapeller: Innovation and Sustainability Research at the University of Graz
Georg Jäger: Innovation and Sustainability Research at the University of Graz

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 29, 665-685

Abstract: Abstract Studies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system’s components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

Keywords: Critical transitions; Early warning signals; Social interaction; Agent-based modeling; Equation-based modeling; Hysteresis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00142-8

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