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

Abstract

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.