Modeling causal relationships in sociocultural systems using ensemble methods

Abstract

Analyzing the political, military, economic, social, information, and infrastructure (PMESII) effects in a sociocultural system requires models that capture the causal and predictive dynamics. However, given the complexity of PMESII factors and the diversity of available data sources, accurately modeling causal relationships requires incorporating multiple domains of study and a variety of analytic methods. In this paper, we present an ensemble approach to modeling causal relationships of sociocultural systems, applying insights from machine learning where ensembles consistently outperform individual approaches. We describe three different types of ensemble models and combinations and explore the application of this approach in experiments using both synthetic and real-world datasets.