Quantitative/Computational Social Science for the DoD AFCEA Luncheon November 17, 2011 Robert Popp, PhD President & CEO, NSI Inc

Strategic Threat Environment

Social Science Modeling Significant complexity, uncertainty and ambiguity in national security problems These problems not easily reduced or amenable to classical analytical methods Quantitative & Computational Social Sciences provide promising new methods, models & tools to inform decision-making Q/CSS supplements early warning analysts who have domain knowledge and expertise Theory –Theoretical underpinnings drive analysis wrt data requirements, model types and hypotheses tested –Theory must be operationalizable and falsifiable Data –Multiple levels: micro-macro, structural, events, … –Multidimensional: temporal, spatial, semantic, … Models –Multiple models lead to robust analyses – single models result in unexpected/disastrous outcomes

Landscape of Social Science Modeling Q/CSS is the branch of science that investigates human/social phenomena (cognition…conflict), at all levels of data aggregation (micro…macro), based on the direct application of tools from modern computing and quantification methods to advance the frontiers of knowledge about the social universe.

Example: Nation State Instability Challenge: results must be… –meaningful, believable, accurate, plausible, “actionable” –grounded in real data and reflect reality with respect to country issues –“objectively” derived based purely on models and data – not biased by modeler views –corroborative by country experts, or generate non-intuitive insights that counters conventional wisdom and/or questions analysts’ and/or decision-makers’ assumptions Hypothesize theories of instabilityHypothesize theories of instability Develop Q/CSS modelsDevelop Q/CSS models Collect relevant dataCollect relevant data Apply, test and refine Q/CSS models to countries of interestApply, test and refine Q/CSS models to countries of interest Generate and evaluate resultsGenerate and evaluate results Instability level

State Stability System Dynamics Model

Caution on Social Science Modeling 100 years of GNP vs. Energy Use data Data appears linear and highly amenable to regression and prediction Several 1975 low energy use scenarios considered to examine vulnerability to future supply constraints Actual results surprising and outside historical trend and 1975 low energy use scenarios Past experience never explored economy ’ s response to sustained high energy prices –“ Elasticities ” (e.g., ability to employ more efficient technology) much higher than ever imagined

Social Science Modeling Value-Add