Rethinking current models in social psychology: A Bayesian framework to understand dramatic social change

Dramatic social change ( DSC ) is the new normal, affecting millions of people around the world. However, not all events plunge societies into DSC . According to de la Sablonnière (2017, Front. Psychol ., 8 , 1), events that have a rapid pace of change, that rupture an entire group's social and normative structures, and that threaten the group's cultural identity will result in DSC . This perspective provokes important unanswered questions: What is the chance that a DSC will occur if an event takes place? And, when will other societal states arise from such events? Addressing these questions is pivotal for a genuine psychology of social change to emerge. The goal of this article was to describe a methodology that attempts to answer these questions via a probabilistic decision tree within a Bayesian framework. According to our analysis, a DSC should occur 6.25% of the time that an event takes place in a stable society (68.75% of the time for incremental social change, 12.5% for inertia, and 12.5% for stability). The Bayesian probabilistic decision tree could be applied to specific event and thus serve as a guide for a programmatic study of social change and ultimately inform policymakers who need to plan and prepare for events that lead to DSC .