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Northrop Grumman Mission Systems

Northrop Grumman Mission Systems

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Northrop Grumman Mission Systems

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  1. Northrop Grumman Mission Systems The Pythagoras Counterinsurgency Application To Support The Marine Corps Irregular Warfare Study ABSVal Workshop #4 Mr. Edmund Bitinas 703-968-1196 Ms. Donna Middleton 703-968-1657 Ms. Brittlea Sheldon 703-968-1137 Mr. Mitch Youngs 703-803-5997 Date:9 July 2008

  2. Agenda • Work To Date • Develop Model • Implementation • Analysis • Sample Results • Findings Regarding Pythagoras/COIN

  3. Develop Model • Built conceptual model in Excel™ • Demonstrated Population Dynamics Methodology • Provided visual depiction of Population Dynamics • Created simple version of event impact • Used in SME interviews • Built conceptual model in Java™ • Greater flexibility • Faster modification/upgrade • Used Excel as postprocessor

  4. Perception of COIN Pro-COIN Insurgent Pro-Insurgent Indifferent Active COIN Conceptual Model of a Population Segment Perception of Insurgency • Each Population Segment Has Its Own “Bubbles” – i.e. Orientations • The people within each Bubble may change over time • Top arrows indicate movement toward the COIN • Bottom arrows indicate movement toward Insurgency • “Return” arrows indicate people remaining within the Bubble

  5. Effect of Influence Estimation • Actions affect a specific population segment or segments • Actions have a duration • Strength of the influence of any action multiplies the values on the exit arrows • Perception of COIN affects the top arrows • Perception of Insurgency affects the bottom arrows • Return Arrows are affected as follows: • Insurgent to Insurgent affected by Perception of Insurgency • Pro-Insurgent to Pro-Insurgent affected by Perception of Insurgency • Pro-COIN to Pro-COIN affected by Perception of COIN • Indifferent to Indifferent is affected by the square root of the product of the Perception of Insurgency and the Perception of COIN • Result is normalized

  6. Conceptual Model Expressed In Spreadsheet Model Form • Sum of all exit arrows equals 100% • One arrow feeds back to original bubble (the Return arrow) • Initial fraction of population segment in each bubble defined by demographics • Initial value on each arrow defined by insurgency susceptibility for the population segment

  7. Perception of COIN Pro-COIN Insurgent Pro-Insurgent Indifferent Active COIN Effect of Influence Estimation on Target Population Population Segment A Base Susceptibility * Strength of Event

  8. Example Influence Event Base Influence Values Apply Influence After Normalizing After Multiplying

  9. Formulation Decisions • The population drifts naturally and in response to actions • Peoples minds change for reasons not being modeled • The model also addresses the impact of Insurgent and COIN events • The timeframe basis for interaction persuasiveness table is one week. • Order of precedence for changes (highest to lowest): • Interaction Estimation Transition Effect on the targeted population (the Direct Effect) • Persuasiveness/Allure Transition Effect (now replaced by Salience) on population segments receiving information about events (the Indirect Effect) • Background Susceptibility Transition (the Ongoing Effect)

  10. Implementation Options In Pythagoras • Three alternative representations • Each agent represents 600 people • Too many agents • Slow run times • Each agent represents the entire segment • Ensures messages have equal weight • Each group is represented • Many fewer agents • Low fidelity • Each agent represents 1% of the segment • Messages have equal weight • Better fidelity than Option Two • Better reflects available media

  11. The Latest Conceptual Model Persuasion and Allure replaced by Salience Based on Charles Osgood’s Semantic Differential Widely Used In Advertising And Market Research Combination of three factors Potency (P) Factor (Strong – Weak) Activity (A) Factor (Active – Passive) Evaluative (E) Factor (Good – Bad) Salience = E √(A2 +B2) 11

  12. Salience Related to the Conceptual Model • Revised the implementation of Salience (to replace Persuasion and Allure) in the conceptual model: • An average Orientation for the two interacting population segments is calculated. (using 1 = FARC through 5 = COIN) • A Delta value is calculated based on the difference of the average of the two population segments • The Delta value is then multiplied by the Salience value to obtain the weight and direction of the influence • A positive value weights the target population’s tendencies towards the MAGTF • A negative value weights the target population’s tendencies towards the Insurgency

  13. Sample Salience Calculation • Example using the influence of the Displaced Persons on the Urban Poor • Calculate average orientation of Urban Poor and Displaced Persons • Average orientation of Urban Poor: • 1x0.058 + 2x0.0917 + 3x0.6575 + 4x0.1512 + 5x0.0416 = 3.03 • Average orientation of Displaced Persons = 2.63 • Calculate Delta: • 2.63 - 3.03 = -0.398 • Multiply Salience value (taken from Salience matrix) by Delta to obtain the weight of influence • -0.398 x -0.259 = 0.103

  14. Pythagoras Configuration • Scenario development completed in Pythagoras 2.0.0 • Attributes 1 through 5 are used to represent the range of insurgency orientations • Attribute Changers represent the population tendencies, the influence between population segments, and the influence of the MAGTF actions • Communication devices represent interactions and possess the Attribute Changers which will do the influencing • Each agent represents 1% of a population segment. These agents are divided into classes based on the initial population segment orientation distribution

  15. Configuration (cont.) Vulnerability is implemented as an incremental Attribute Changer Displaced Persons Pro-FARC Example: Displaced Vulnerability Matrix Displaced Persons Pro-FARC Attribute Changes 15

  16. Configuration (cont.) Salience is implemented as a relative Attribute Changer. Implementing the example of the influence of the Displaced Persons on the Urban Poor (Slides 27 to 28): Average Orientation, Urban Poor: 3.03 Average Orientation, Displaced Persons: 2.63 Salience Value: -0.259 (-26%) Because the Urban Poor population falls to the right of the Displaced Persons, the negative salience value is going to shift the population even more to the right. The Displaced Persons possess relative attribute changers to bring the Urban Poor Attributes 4 and 5 closer to them by 26% 16

  17. Configuration (cont.) MAGTF Influence Estimation The Shore Based MAGTF action influences the Displaced Persons by 0.721 to the right and 0.117 to the left The action is implemented as a multiplier Attribute Changer, multiplying the left Attributes by 1.17, the and the right Attributes by 7.21 based on the orientation sector being targeted 17

  18. Pythagoras Colombia Scenario Results The graph below shows the resulting orientation changes based on background vulnerabilities and interacting population segments Urban Middle Class Orientation Changes, No MAGTF action

  19. Pythagoras Colombia Scenario Results (cont.) The graphs on the following slides display the cumulative results of MAGTF actions on the Urban Middle Class MAGTF Off-Shore, Urban Middle Class Orientation Changes 19

  20. Pythagoras Colombia Scenario Results (cont.) MAGTF On-Shore, Urban Middle Class Orientation Changes 20

  21. Pythagoras Colombia Scenario Results (cont.) The graphs demonstrate significant differences in the population dynamics and final outcome as a result of implementing either on-shore or off-shore MAGTF actions In the off-shore scenario, there is a steady increase in the COIN sector of the Urban Middle Class Population Segment, while in the on-shore scenario, the COIN population decreases. The percentage of FARC and pro-FARC in the Urban Middle Class Segment reaches near 0% in the off-shore scenario, while approximately 5% of the population in the on-shore scenario remain FARC and Pro-FARC 21

  22. Further Development of Colombia Scenario The Study Team has completed sensitivity runs to determine if the results are robust for changes in inputs The Study Team performed data farming runs to examine the interactions among populations, and look for outliers Suggestions from the study sponsor have been incorporated into the model 22

  23. Further Development-Implied Study Objectives The Study Team has advanced the art (and science) of modeling irregular warfare Pythagoras can be used to model population dynamics. It is accomplished through: Algorithm construction Data identification Data collection Data interpretation (Words To Numbers) Data preparation Analytic processes (e.g., Data Farming) 23

  24. Ashore or Afloat? • Base Case run indicates Afloat, but • The data is soft (words to numbers) • The methodology is one of many • Performed Data Farming runs to increase confidence in the answer • Fifty (50) data farming runs • Modified influence and salience by between -25% and +25% using soft rules (uniform distribution) • Modifying MAGTF influence -10% to +10%

  25. Ashore or Afloat Results Afloat has nearly equal or more Pro-COIN, COIN

  26. Ashore or Afloat Results (cont.) Afloat has the same or fewer Pro-FARC, FARC

  27. Validation “Which COA is better?” cannot be answered with much confidence “What is the chance that ashore is better than afloat?” can be answered with greater confidence More pro-Government sentiment if Marines stay afloat Lower pro-FARC sentiment if Marines stay afloat Marine arrival has a polarizing effect (fewer neutrals) Marine arrival in either case increases anti-Government sentiments of the Illicit Organizations and the Military Afloat seems to usually do less harm. There is no factor in our influence estimation that BOTH reduces the negative impact of Ashore AND increases the negative impact of Afloat 27

  28. Validation (cont.) Because the current Markov chain will eventually return to the same steady state, regardless of MAGTF action, once the MAGTF leaves, we need to consider: Does the MAGTF commander care about leaving a lasting impression? At what point in time do we measure ‘better’? Pythagoras could change the final steady state as a function of one or more population segments exceeding or falling below some target value. However, this data was not collected 28

  29. Questions?