ISI Journals

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 mar- kets, social movements, and protest behavior.