Unveiling hidden features of social evolution by inferring Langevin dynamics from data

2026-01-25

AI Summary

This study addresses the challenge of uncovering latent dynamical patterns underlying socio-cultural evolution, overcoming limitations of existing approaches that inadequately capture the uncertainty and continuity of historical trajectories. To this end, it introduces the Langevin dynamics framework into social evolution analysis for the first time, modeling evolutionary processes as continuous-time stochastic differential equations (SDEs). By integrating Bayesian inference with multiple imputation techniques, the proposed method simultaneously quantifies irreversibility, detects exogenous perturbations, and reconstructs missing data within a unified framework. This approach transcends the constraints of static models, effectively handling fragmented historical records and revealing the intrinsic stability, contingency, and dynamic mechanisms that shape societal evolution.

Technology Category

• Machine Learning: Evolutionary Learning
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Search and Optimization: Evolutionary Computation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Are there hidden dynamical common patterns in the evolution of social and cultural history? While the growing availability of digitized social data invites us to answer this question, prevailing quantitative methods often rely on deterministic snapshots or average effects. Such approaches overlook the continuous and inherently uncertain nature of historical trajectories. In this paper, we propose a framework for modeling historical dynamics as stochastic processes described by stochastic differential equations (SDEs). By viewing historical change through the lens of continuous-time dynamics, this framework provides a natural language to describe how structural trends and inherent random fluctuations interact to shape societal evolution. This approach allows us to handle the uncertainty in fragmentary historical records, moving beyond the dichotomy of structural determinism versus pure chance. We demonstrate that adopting this stochastic perspective unlocks a rich suite of analytical capabilities unavailable to static models. Specifically, we introduce methods to: (1) quantify the irreversibility; (2) detect exogenous perturbations; (3) perform multiple imputation for missing historical records. This framework offers a unified methodology for dissecting the stability, contingency, and dynamics of historical change.

Problem

• social evolution
• historical dynamics
• stochastic processes
• uncertainty
• hidden patterns

Innovation

• stochastic differential equations
• Langevin dynamics
• historical dynamics
• irreversibility
• multiple imputation