Recent years have seen major political crises throughout the world. Most recently, the US was swept by a wave of protests, urban riots, and violent confrontations between left- and right-wing extremists.

Understanding how future crises will unfold and assessing the resilience of different countries to various shocks is of foremost importance in averting the human costs of state breakdown and civil war.

In a recent publication (Turchin et al. 2018) we proposed a novel transdisciplinary approach to modeling social breakdown, recovery, and resilience. This approach builds on recent breakthroughs in macrosocial dynamics (and specifically structural-demographic theory), statistical analysis of large-scale historical data, and dynamic modelling.

Our main goal is to construct a series of probabilistic scenarios of social breakdown and recovery. We called this approach—similar to ensemble forecasting in weather prediction—multipath forecasting (MPF).

In this article I develop a “prototype” of the MPF engine with the goal of illustrating the utility a fully developed version may have. I first apply the computational model to the period of American history from the beginning of the nineteenth to the end of the twentieth century, with the goal of parameterizing the model and testing it against data. Then I use the parameterized model to forecast the dynamics of instability in the USA beyond 2020 and illustrate how the MPF engine can be used to explore the effects of different policy interventions.

P. Turchin, Multipath Forecasting: the Aftermath of the 2020 American Crisis, pre-print (2021)