  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.
