Predictive Analysis for Social Processes I: Multi-Scale Hybrid System Modeling

TL;DR

This paper introduces a multi-scale hybrid system framework for modeling and predicting complex social processes, demonstrated through a SARS epidemic case study, advancing understanding and forecasting capabilities.

Contribution

It presents a novel multi-scale stochastic hybrid system model for social processes and a rigorous, computational approach for their predictive analysis.

Findings

Effective modeling of social dynamics and intrinsic factors

Successful application to SARS epidemic prediction

Enhanced understanding of social process interplay

Abstract

This two-part paper presents a new approach to predictive analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the appeal of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process predictive analysis.