A radically high-tech approach to the scientific study of (non)religious identity and transformation
MRC is reshaping the future of data-driven, multi-disciplinary demographic research by intentionally combining traditional social science methodologies with cutting-edge computer modeling and simulation.
Measuring religious change and forecasting the future of religious change are incredibly complicated tasks with a high possibility for error. Civic planners rely on demographic projections, regardless of how (in)accurate they might be. We are tackling multiple problems in this field through our advanced data-gathering techniques and agent-based models. Our work will improve the demographics of religion and system engineering and ultimately produce more accurate projections of religious change.
Can we measure (and predict) religious change?
Many nations critically require forecasts of the number of people of various religions for effective civic planning. Collective behaviors such as likely family size, economic security, and migration patterns are strongly correlated with religion. These collective behaviors inform policy and civic planning.
MRC is a collaborative project bringing together experts in demography, the scientific study of religion, and computational methods to create theory-based simulations of (non)religious and identity around the world and across generations. We combine traditional social-science methodologies with computer modeling and simulation to predict religious change in the real world. The idea is to create artificial societies with cognitively plausible and behaviorally believable AI agents and then measure the change in religiosity over time in those artificial societies.
Applying computational models allows us to see beyond the limitations of traditional population projections. Our models include more nuance in our projections, track how individual behaviors lead to group dynamics, and identify which factors are the most influential in religious change. Whereas traditional demographic methods impose calculations on monolithic groups, we see how group behavior arises from individual behavior. Ultimately, we hope to create more accurate, although still imperfect, glimpses into the future.