COMPUTATIONAL SOCIAL SCIENCE

Department of Computational Social Science Seminar Abstract

Monday, November 5: 2-4 p.m.
Founders Hall, Room 468
Arlington Campus

Doctoral Dissertation Defense: Agent-Based Modeling in Intelligence Analysis

Aaron Frank, PhD Candidate
Department of Computational Social Science
George Mason University

ABSTRACT: This study considers examines how Agent-Based Modeling (ABM) can transform current analytic tradecraft within the intelligence community. It provides a normative theory of intelligence analysis that argues for the careful consideration, development, and implementation of a model-centric analytic tradecraft, particularly for the production of strategic intelligence. Rather than focus on prior cases of intelligence failures in order to demonstrate how intelligence analysts, collectors, managers, and policy-makers have erred in the past, this project provides theoretical arguments in favor of establishing a new analytic tradecraft and demonstrates, if only in a rudimentary fashion, what this new approach may look like in practice.

In this study, several persistent debates within intelligence studies, familiar to a small group of scholars and theoretically-minded professionals, are reimagined in light of the challenges and opportunities that new modeling and simulation capabilities provide. At its simplest, most conservative application, ABM extends the ongoing development of Structured Analytic Techniques (SATs), the centerpiece of contemporary analytic tradecraft. At the maximum, ABMs can reframe analytic practice, synthesizing the longstanding and opposing views as to whether intelligence analysis is an art or a science, and in the process transform the most challenging and interesting aspect of the intelligence profession—the relationship between intelligence producers and consumers.