UNCLASSIFIED 1 Top 3 M&S Challenges for SSTR Operations Developing Epistemologically Sound Standards for Analysis Developing Adequate Technology to Represent Social Phenomena Recognizing and Concentrating on Growth Areas

UNCLASSIFIED 2 IW Modeling: Still the same KIND of thing Traditional OR Simulation: Analyzing what you DO know –It was OK to assume away maneuver in conventional simulation because we could assume symmetrical abilities –It had a “hard to argue with” nature Representations of the physics based phenomena: reasonable, agreed upon What we didn’t know was covered in Monte Carlo Simulation –It explored phenomena we didn’t know by walking through it Far superior to SME guestimates! Computational Social Simulation –Still, basically, the same thing!! By definition, Uncoventional warfare simulation is other-than force-upon- force, and can not be physics-based However, we can still capture what we know and use Monte Carlo on what we don’t –Modeling is still putting the crux of the problem in what we capture, and using it to gain partial knowledge of outcome patterns. –The point is to move unknowns off the modeling part, onto the Monte Carlo part, and come out with ranges of the possible and probable We can argue about what we know in social science, but at least we can represent major schools

UNCLASSIFIED 3 Developing Epistemologically Sound Standards for Analysis –We are not describing, we are analyzing We can not assume away the problem: “heroic assumptions” We can not use equations that are in principle unknowable We don’t want our models to just tell us what we tell them to tell us –We have to walk out problems for analysis We can not just accept deterministic methods with questionable equations –If in a model the equations are unknown, then it should be tried with a range of plausible equations Emergent phenomena that fits data is valid phenomena. That which explores what happens in recombined circumstances given acceptable assumptions –Lower level rules –Autonomous Behavior –Explore the results Assumptions should be acceptable, and sensitivities not lie in the unknown areas –For example, if the model represents human learning, see if it is robust to the learning method, not depending on particular kinds of machine learning. –Acceptable assumptions usually means: use measureables »Mental phenomena, like “consumer confidence” can be measured

UNCLASSIFIED 4 Developing Adequate Technology To Represent Social Phenomena This is a knowledge representation problem –Calculus is an adequate representation of phenomena in physics –Statistical Methods do not adequately capture meaning and the crux of the problem in social phenomena Thin description vs. Thick description Knowledge representations from other fields are often hammers that see social science as a nail –Fluid Dynamics from Physics: We may like the way relations are drawn on a System Dynamics diagram, and want to down play the numbers, but exact numbers matter to the answers they give –Contagion from Epidemiology Context matters to the spread of behaviors and beliefs

UNCLASSIFIED 5 Recognizing and Concentrating on Growth Areas Growth Area: Develop technologies that represent thick description in a way amenable to computation –Computation is essential for space exploration –Thick description is essential to the development of insightful generalizations: it’s the crux of the issue Growth Area: The dynamics by which human actions come to have different meanings in different contexts Growth Area: Micro-Macro integration –How do Macro social patterns come from micro actions? Methodologies that have been around 50 years that can not change in principle are already well understood