Viewing social isolation as a complex dynamical system: A theoretical and computational framework

Abstract

Abstract Social isolation is a major public health concern linked to increased risk for both psychiatric and physical health conditions. Yet despite the potential consequences of social isolation, our understanding of its nature and how it emerges and evolves over time remains limited. We propose that social isolation should be understood and analyzed as a complex dynamical system. First, we introduce core principles of dynamical systems theory and describe how they can be applied to better understand social isolation. Second, we formalize a dynamical systems model using differential equations. Third, we present simulations based on the differential equations showing how changes in system dynamics may increase or decrease the likelihood of individuals entering a state of social isolation. Fourth, we provide a brief simulation-recovery analysis demonstrating model parameter identifiability from intensive longitudinal data designs. Finally, we offer a simulated example of how intensive longitudinal data could be used to identify signs of transitions between healthy and isolated states. Overall, this framework, both theoretical and computational, helps elucidate the dynamic nature of social isolation and may ultimately inform empirical research and personalized interventions capable of identifying those at risk for transitioning into a state of isolation.

Affiliated Institutions

• Neurobehavioral Systems US
• University of California, Los Angeles US
• Boston University US

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