An Epidemiological Modeling Take on Religion Dynamics

TL;DR

This work reframes religion dynamics as a diffusion process governed by an epidemiological multi-strain SIR model. By distinguishing passive believers, active missionaries, and religious elites across multiple religions and allowing endogenous mutation-like splits, it reproduces canonical patterns such as emergence, rapid expansion, long-run coexistence, and rise-and-fall movements. The authors validate the framework with stylized simulations, calibrations to diverse historical time-series, and a phase-transition analysis that maps regime boundaries in parameter space, revealing how small shifts in recruitment or retention can qualitatively reshape religious landscapes. The approach offers a transparent, mechanism-based tool for comparative inference and counterfactual exploration in sociological research, with limitations noted (e.g., homogeneous mixing) and clear directions for extending to networks, geography, and richer observables.

Abstract

Religions are among the most consequential social institutions, shaping collective identities, moral norms, and political organization across societies and historical periods. Nevertheless, despite extensive scholarship describing conversion, competition, and secularization, there is still no widely adopted formal model that captures religious dynamics over time within a unified, mechanistic framework. In this study, we propose an epidemiologically grounded model of religious change in which religions spread and compete analogously to co-circulating strains. The model extends multi-strain compartmental dynamics by distinguishing passive believers, active missionaries, and religious elites, and by incorporating demographic turnover and mutation-like splitting that endogenously generates new denominations. Using computer simulations, we show that the same mechanism reproduces canonical qualitative regimes, including emergence from rarity, rapid expansion, long-run coexistence, and transient rise-and-fall movements. A reduced calibration variant fits historical affiliation trajectories with parsimonious regime shifts in effective recruitment and disaffiliation, yielding interpretable signatures of changing social conditions. Finally, sensitivity analyses map sharp regime boundaries in parameter space, indicating that modest shifts in recruitment efficacy or retention among active spreaders can qualitatively alter long-run religious landscapes. These results establish a general, interpretable framework for studying religion as a dynamical diffusion process and provide a tool for comparative inference and counterfactual analysis in sociological research.

Figures (5)

• Figure 1: A schematic view of the model's structure for the case of two religions ($|\mathcal{R}|=2$). Red (small, top-facing) arrows indicate death.
• Figure 2: Replicate-mean population-share trajectories for three stylized parameterizations.
• Figure 3: Historical-record fitting case studies.
• Figure 4: Religion spread dynamics phase transition based on baseline conversion strength and missionary disaffiliation.
• Figure 5: ODE-ABS agreement across $n=99$ experiments with different parameter values over the three cases, like in Fig. \ref{['new_religion_threepanel']}, where each value i.