Peace and Conflict: Journal of Peace Psychology, Vol 32(1), Feb 2026, 1-15; doi:10.1037/pac0000807
Dramatic events—including coups d’état, pandemics, nation-wide protests, and natural disasters—can dramatically alter a society by challenging the status quo, a process called social change. The goal of this article was to explain the background, capabilities, and potential use of the social change algorithm (SCA), a theory-driven algorithm based on Bayesian probabilistic decision trees that models social change. The SCA models how a disruptive event provokes a transition from one societal state to another. First, we describe the numerical formulation of the SCA. Second, we present theoretical models and interpret the resulting process of social change with matching historical events. Third, we use empirical data (the early COVID-19 pandemic in Canada and in the United States and past coups d’état and elections) to test the SCA’s modeling capacities. Overall, we find that the SCA modeled different forms of social change, matching the chronology of how events impacted society. Thus, the SCA is an algorithm that can be used to better comprehend the process of social change to ultimately mitigate its potential consequence: violence, the avoidable harm to basic human needs. (PsycInfo Database Record (c) 2026 APA, all rights reserved)