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Irregular Warfare Project

Irregular Warfare Project. MCCDC Operations Analysis Division (OAD) January 2008. Purpose. Present Status of the MCCDC OAD Irregular Warfare (IW) Project. Project Goal: Develop a prototype methodology for analyzing a USMC IW problem in-house. Agenda. IW Modeling Challenge

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Irregular Warfare Project

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  1. Irregular Warfare Project MCCDC Operations Analysis Division (OAD) January 2008

  2. Purpose Present Status of the MCCDC OAD Irregular Warfare (IW) Project Project Goal: Develop a prototype methodology for analyzing a USMC IW problem in-house

  3. Agenda • IW Modeling Challenge • Conceptual Model • Scenario Background • Data Acquisition • How IW Data is used in our Model

  4. The IW Modeling Challenge Military OR Analyst Comfort Zone Killer-Victim Adjudication Weapon Pk Combat Model Lethality Armor Thickness Survivability Vehicle Speed IW Domain Population Response Influence IW Model Attitude Susceptibility Behavior Information Ops The Challenge: Different data, different algorithms, different MOEs

  5. IW Modeling:Expectation Management “Soft Sciences” typically have much lower statistical correlation than “Hard Sciences” • As a practical matter, for typical data found in the social sciences, values of r2 as low as .25 are often considered useful. For data in the physical and medical sciences, r2 values of .60 or greater are often found; in fact, in some cases, r2 values greater than .90 can be found.* Modeling human behavior involves a higher level of uncertainty than modeling traditional force-on-force combat * Statistics for Business and Economics by Anderson, Sweeney, and Williams

  6. Population Segments Conceptual Model of Civilian Population Civilian Population Insurgency Behavior Orientation FARC Pro-FARC Neutral Pro-GoC GoC FARC = Revolutionary Armed Forces of Colombia GoC = Govt of Colombia

  7. Conceptual Model of Civilian Population FARC GoC

  8. Colombia Scenario • Background • MAGTF Mission: • Refugee Camp Security • Humanitarian Assistance / Disaster Relief • 2 Possible Courses of Action (COAs) • Sea-Based • Shore-Based • Provide: • Joint “Cultural” Prep of the Operational Environment • Plausible Range of Civilian Population Behaviors

  9. SME Interviews • Selecting SMEs • 2 SMEs obtained via MCIA • SME credentials • Analyst & cultural SME communication challenge • Analysts need numbers, e.g., probabilities, percentages • Cultural SMEs are non-quantitative thinkers

  10. Scenario Data Colombia • “Operation Pacific Breeze” • Background • MAGTF Mission: • Refugee Camp Security • Humanitarian Assistance / Disaster Relief • 2 Possible Courses of Action (COAs) • Sea-based • Shore-based • Goal – Civilian Population Govt Support • Cultural data narrowly focused on this region • Data is not accurate for the rest of Colombia

  11. Cultural Data Required Step 0: Define population segments Elicit data for each population segment • Prevalence of current behavior patterns • Perceived needs are affected based on three factors (using Narrative Paradigm) • Natural tendency of the population segment • The population segment’s narrative with respect to the insurgency • Effect of current events on population segment (impact) • How the population segment reacts to a given COA • Effect of other population segments on a population segment (influence) • How the population segment reacts to the narratives offered by other population segments

  12. ColombiaPopulation Segments • Illicit Organizations • Catholic Church • Police • Military • Displaced Persons • Urban Poor • Urban Middle Class • Old Money • Cultural Behavioral Data • Orientation (Initial, Tendency) • Impact Of MAGTF COAs • Influence Of Population Segment Interactions

  13. Orientation Data • Initial orientation • “How do the actions of this population segment support the insurgency (FARC) or the Government of Colombia (GoC)?” • Natural tendency of orientation • “Given no external influences, over time, how would the actions of this population segment change to support the FARC or the GoC?” • Captured as data for a Markov transition matrix Example: Urban Poor

  14. FARC Pro-FARC Neutral Pro-GoC GoC 100% 80% 60% 40% 20% 0% Catholic Displaced Illicit Military Old Money Police Urban Middle Urban Poor Church Persons Organizations Class Initial Orientation

  15. Data Elicitation • Charles Osgood’s Semantic Differential • Osgood’s method is a development of the Likert Scale in that Osgood adds in three major factors or dimensions of judgment: • EVALUATIVE (good - bad) • POTENCY (strong - weak) • ACTIVITY (active - passive) • Semantic Differential is widely used in advertising and marketing research, including questionnaires, interviews and focus groups. The versatility of uses with bipolar adjectives and the simplicity of understanding them have made it ideal for consumer questionnaires and interviews. • There are several large scale surveys done, providing data on EPA values for over 1000 different actions, emotions and people, led by David Heise, Department of Sociology, Indiana University Rolled up to a single parameter = E * sqrt (P2+A2) Translates SME words to a quantitative measure

  16. Impact of COAsElicitation • “What words would this population segment use to describe MAGTF ‘sea-based’ operations?” • ‘Positive words’ averaged to measure leaning more towards GoC (right) • ‘Negative words’ averaged to measure leaning more towards FARC (left) • “What words would this population segment use to describe MAGTF ‘shore-based’ operations?”

  17. Shore - left Shore - right Sea - left Sea - right 1.0 0.8 0.6 0.4 0.2 Influence Left -- Influence Right 0.0 Illicit Organizations Displaced Persons Urban Poor Catholic Church -0.2 -0.4 -0.6 -0.8 -1.0 Impact of Shore/Sea Base • Left means the sea/shore base COA causes the actions of the population segment to lean towards the FARC • Right means the sea/shore base COA causes the actions of the population segment to lean towards the GoC

  18. Impact of Shore Base Multiply Normalize Example: Urban Poor

  19. Influence Elicitation • Influence of other population segments • “What words would this population segment use to describe another population segment?”

  20. Influence of other Segments Influence of “x-axis” on “legend”

  21. Influence of Population Interactions Multiply Normalize Example: Urban Poor

  22. Quo Vadis? • Run new version of Pythagoras with data on 8 population segments • Perform sensitivity analysis on cultural data variables (using Design of Experiments) • Solicit feedback from cultural SMEs on Pythagoras results

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