Validation Methodology for Agent-Based Simulations Workshop Perspectives on Agent-Based Simulation and VV&A Dr. Bob Sheldon Joint and External Analysis Branch Operations Analysis Division Marine Corps Combat Development Command 01 May 2007

Overview VV&A and Agent-Based Simulation (ABS) thoughts from Dr. George Akst, Senior Analyst, Marine Corps Combat Development Command (MCCDC) MORS historical perspectives on VV&A and ABS Personal reflections

It’s the data, stupid!  How do you come up with data for parameter Z = x.x %? Especially a problem for Irregular Warfare (IW)  Sometimes, model developers who are structuring algorithms don’t worry about data & assume data can be developed after the fact Dr. Kirk Yost data triage – consider data sources when building models –Generally accepted (produced regularly by some believable source) –Semi-valid (reasonable information derived from various sources) –Judgment and knobs  If you start with meaningless data, and execute a design of experiments with 2 10 runs (just because you can), then you will have 2 10 useless results To be useful, ABS need to provide more than just simplistic insights  ABS should go beyond being an automated tool that regurgitates SME intuition Perspectives from Dr. Akst

More Dilbert Data

Perspectives from Dr. Akst Two ends of the spectrum  Engineering-level model: should very closely predict how system would operate in the real world  Campaign-level model: measure relative differences that changes to forces, tactics, or equipment have on the outcome Trying to literally match a combat model’s results with some other set of results (real world, experiment, or another model) is not realistic What validation is:  Failure to invalidate after concerted effort  Ascertaining that results are “plausible” – no obvious logic flaws and results are “reasonable” and “relatively consistent” with past modeling results From “Musings on Verification, Validation, and Accreditation (VV&A) of Analytical Combat Simulations” Phalanx, September 2006

MORS Meetings on VV&A Simulation Validation (SIMVAL), October 1990 SIMVAL II, April 1992 SIMVAL '94, September 1994 Simulation Validation tutorial, MORSS & ALMC, 1995 (Pete Knepell) SIMVAL '99: Making VV&A Effective and Affordable, January 1999 Evolving Validation Topics in MORS  Descriptive validity, Structural validity, Predictive validity  Structural validation, Output validation  Conceptual Model validation, Data validation, and Output validation

MORS Meetings on ABS New Techniques: A Better Understanding of their Application to Analysis, November 2002  Included 1-day tutorial on Agent-Based Models Agent-Based Models and Other Analytic Tools in Support of Stability Operations, October 2005 Plus substantial coverage in MORSS working groups, e.g., WG 31 – Computing Advances in Military OR and WG 32 - Social Science Methods

Personal Reflections How to validate (or invalidate) counter- intuitive results (e.g., Surprise)  Clay Thomas “Analysis either verifies your intuition or educates your intuition.” Simple visualization helps validation  Gantt chart example for sortie generation  Provide visualization that SMEs understand

Personal Reflections (Cont’d) Ready access to source code helps  Example: Effect of (0,1) parameter Good mathematical documentation a plus

Comparing counter-intuitive results to “intuitive” results: a case study At a Project Albert workshop, the agent-based model Socrates gave counter-intuitive results  Simulation attrition results varied over 3 phases with 2 breakpoints  When I fit a Lanchester linear model to the results, the regions where the fit was “bad” corresponded to the counter-intuitive results  Drill-down investigation explained these anomalies  Mysterious results were due to scenario data & tuning parameters Personal Reflections (Cont’d) “Comparing the Results of a Nonlinear Agent-Based Model to Lanchester’s Linear Model” Maneuver Warfare Science 2002

Questions? Juan Muñoz, Five Seated Figures, 1996 Hirshhorn Museum and Sculpture Garden