Irregular Warfare Project

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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 - PowerPoint PPT Presentation

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  • Irregular Warfare ProjectMCCDC Operations Analysis Division (OAD) January 2008

  • PurposeProject Goal: Develop a prototype methodology for analyzing a USMC IW problem in-housePresent Status of the MCCDC OAD Irregular Warfare (IW) Project

  • AgendaIW Modeling ChallengeConceptual Model Scenario BackgroundData AcquisitionHow IW Data is used in our Model

  • The IW Modeling ChallengeIW DomainCombat ModelWeapon PkArmor ThicknessVehicle SpeedLethalitySurvivabilityIW ModelInfluenceSusceptibilityInformation OpsAttitudeBehaviorPopulation ResponseKiller-Victim AdjudicationMilitary OR Analyst Comfort ZoneThe Challenge: Different data, different algorithms, different MOEs

  • IW Modeling:Expectation ManagementSoft Sciences typically have much lower statistical correlation than Hard SciencesAs 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.** Statistics for Business and Economics by Anderson, Sweeney, and Williams Modeling human behavior involves a higher level of uncertaintythan modeling traditional force-on-force combat

  • FARCPro-FARCNeutralPro-GoCGoCConceptual Model ofCivilian PopulationInsurgency Behavior OrientationCivilianPopulationFARC = Revolutionary Armed Forces of Colombia

    GoC = Govt of Colombia

  • FARCGoCConceptual Model ofCivilian Population

  • Colombia ScenarioBackground 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 EnvironmentPlausible Range of Civilian Population Behaviors

  • SME InterviewsSelecting SMEs2 SMEs obtained via MCIASME credentialsAnalyst & cultural SME communication challengeAnalysts need numbers, e.g., probabilities, percentagesCultural SMEs are non-quantitative thinkers

  • Scenario Data Cultural data narrowly focused on this region Data is not accurate for the rest of ColombiaColombia 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 RequiredStep 0: Define population segments

    Elicit data for each population segmentPrevalence of current behavior patternsPerceived needs are affected based on three factors (using Narrative Paradigm)Natural tendency of the population segmentThe population segments narrative with respect to the insurgencyEffect 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

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

  • Orientation DataInitial orientationHow do the actions of this population segment support the insurgency (FARC) or the Government of Colombia (GoC)?

    Natural tendency of orientationGiven 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 matrixExample: Urban Poor

  • Initial Orientation

  • Data ElicitationCharles Osgoods Semantic DifferentialOsgoods 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 Translates SME words to a quantitative measureRolled up to a single parameter = E * sqrt (P2+A2)

  • Impact of COAsElicitationWhat 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?

  • 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 GoCImpact of Shore/Sea Base

  • Impact of Shore BaseExample: Urban PoorMultiplyNormalize

  • Influence ElicitationInfluence of other population segments What words would this population segment use to describe another population segment?

  • Influence of other SegmentsInfluence of x-axis on legend

  • MultiplyNormalizeInfluence of Population Interactions Example: Urban Poor

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

    Good Afternoon!..

    Describe picture.Shown here are the purpose of this presentation and the goal of the Irregular Warfare Project.

    My focus will be on . Short.Traditional combat analysis approachVIV, CASTFOREM, Combat XXI, spreadsheets

    Look at force-on-force attrition, logistics, C2This is a map of Colombia and within the circled area are two provinces on the Pacific coast. The primary city in this area is Buenaventura, a seaport which is a predominant thru-way for drug traffic. Although it has a small upper class and a growing middle class, Buenaventura is populated mainly by urban poor and displaced persons who have been driven from their villages by the insurgency and crime that is gripping Columbia.

    The two key players in the insurgency are 1) the insurgents, the Revolutionary Armed Forces of Colombia (FARC), and 2) the counterinsurgents, the Government of Colombia (GoC). Other critical players are the militias, the drug traffickers, the Colombian Army and the police. All have a presence in Buenaventura. It is a fomenting hot bed with a crime rate many times higher than New York City.

    In our problem scenario, a tsunami has struck the area indicated in red on the map, destroying much of Buenaventura.

    A Marine Air-Ground Task Force (MAGTF) has been sent to the area as part of a Joint, Combined, Interagency Task Force at the request of the GoC with the mission as shown here.

    The MAGTF Staff is considering the two courses of action shown on the slide.This is a map of Colombia and within the circled area are two provinces on the Pacific coast. The primary city in this area is Buenaventura, a seaport which is a predominant thru-way for drug traffic. Although it has a small upper class and a growing middle class, Buenaventura is populated mainly by urban poor and displaced persons who have been driven from their villages by the insurgency and crime that is gripping Columbia.

    The two key players in the insurgency are 1) the insurgents, the Revolutionary Armed Forces of Colombia (FARC), and 2) the counterinsurgents, the Government of Colombia (GoC). Other critical players are the militias, the drug traffickers, the Colombian Army and the police. All have a presence in Buenaventura. It is a fomenting hot bed with a crime rate many times higher than New York City.

    In our problem scenario, a tsunami has struck the area indicated in red on the map, destroying much of Buenaventura.

    A Marine Air-Ground Task Force (MAGTF) has been sent to the area as part of a Joint, Combined, Interagency Task Force at the request of the GoC with the mission as shown here.

    The MAGTF Staff is considering the two courses of action shown on the slide.Our interviewer, LT Robin Marling, engaged the SMEs in conversation while including questions like the one shown here. After a while, using a laptop, she showed the SME a pie chart similar to the one shown here. The colors go from red, representing behaviors directly supporting the FARC, to blue, representing behaviors directly supporting the GoC. She then adjusted the chart to suit the SME.The pie chart shown here represents the initial behavioral of the Urban Poor population segment in the Buenaventura area, as given to us by the SMEs.This was done for each population segment.When the SME was satisfied with the distribution of behaviors as represented by the pie chartit was not difficult to back out the quantitative data as shown here. These percentages are input to the Pythagoras agent-based simulation environment.Markov transition matrices like this one. Each cell represents the probability that people in the Urban Poor population segment that are currently exhibiting row behavior will change their actions to column behavior. The rows, of course, sum to 1.0 (100%).The cells on the diagonal are coded blue and show that during relatively short periods of time there is not much to expect in changes of behavior. Nevertheless, there is behavioral change over time and the cells coded in yellow indicate values greater than 0.01 (0.1%).Notice that cells above the diagonal indicate a shift in behavior patterns towards the GoC ( a shift to the right) whereas cells below the diagonal indicate a shift towards the FARC (a shift to the left). This matrix is input to the Pythagoras agent-based simulation environment. Pythagoras does not carry out Markov matrix computations but uses the values to represent the dynamic sidedness of the agents as it interacts with events and other agents. Dr Osgoods Semantic Differential offers a method that can allow easier interviewing of subject matter experts.When we normalize the second matrix so that the row values sum to one, we obtain the bottom matrix, another Markov matrix that displays new Urban Poor behavior patterns reflecting the impact of the MAGTF establishing itself ashore.

    Notice from the yellow-coded cells in the bottom matrix that the shift in behavior is towards the GoC side of the spectrum.

    This matrix is input to the Pythagoras agent-based simulation environment.

    The procedure shown on this slide is the same as on the MAGTF impact slide. Notice that the 0.16 value from the previous slide is in the Right block. Its absolute value would be in the Left block if it were negative. The value in the remaining block is always 0.05.

    The bottom matrix represents the Urban Poor behavior tendencies after applying the interactive influence of the Urban Middle Class. It displays a behavioral shift towards the GoC (as evidenced by increases in values in the matrix above the diagonal) and is input to the Pythagoras agent-based simulation environment.

    There is still much to learn from running the abbreviated four population segment simulation, but we are looking forward to running the entire eight population segment simulation and analyzing those results.