Inferring Stochastic Dynamics Reveals Atypicality and Irreversibility in Social Change
Youngkyoung Bae, Hajime Shimao, Seungwoong Ha, Luna Yang, David Wolpert
Human social systems bear striking similarities, yet similar conditions can give rise to markedly different historical trajectories. A central challenge for quantitative social science is therefore to use time-series data to identify features common to the evolution of different social systems without obscuring the variation distinguishing individual histories. Here, we address this challenge by fitting stochastic differential equations (SDEs) to ensembles of observed trajectories through spaces of socially relevant variables. Importantly, the SDE allows the average tendency and variability of change to depend on the current state of the system. This flexibility allows us to use the SDE to analyze myriad aspects of social dynamics. Specifically, the fitted SDEs identify regions of the state space where stochastic variability is greater, allowing a broader range of plausible subsequent changes. The fitted SDE also allows us to quantify the dynamical irreversibility of observed trajectories and how that irreversibility depends on the values of the social variables. In addition, we show how the SDEs can be used to identify statistically atypical transitions in individual trajectories, indicative of exogenous perturbations impacting the associated society. We apply these tools to three domains: modern political economy, quantitative history, and cultural analytics. This reveals state-dependent change in the organization of modern political-economic systems; a broad but stochastic tendency toward increasing social complexity in historical societies; and time-dependent reorganization of lexical dynamics in English. Looking forward, we argue that our framework provides a set of powerful quantitative tools that can be used to investigate a broader range of longitudinal social phenomena.
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