Predictive Analysis for Social Processes II: Predictability and Warning Analysis

TL;DR

This paper introduces a new systems theory-based approach for predictive analysis of social processes, enabling assessment of predictability, early warning indicators, and scalable predictions, demonstrated through diverse case studies.

Contribution

It develops a computationally tractable framework for predicting complex social processes, addressing the challenge of identifying early indicators and improving forecast reliability.

Findings

Assessment of process predictability achieved

Identification of measurable predictive indicators

Discovery of reliable early warning signals

Abstract

This two-part paper presents a new approach to predictive analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces a multi-scale, stochastic hybrid system modeling framework for these systems. In Part II of the paper we develop a systems theory-based, computationally tractable approach to predictive analysis for these systems. Among other capabilities, this analytic methodology enables assessment of process predictability, identification of measurables which have predictive power, discovery of reliable early indicators for events of interest, and robust, scalable prediction. The potential of the proposed approach is illustrated through case studies involving online markets, social movements, and protest behavior.