Northrop Grumman Mission Systems

1. Northrop Grumman Mission Systems The Pythagoras Counterinsurgency Application To Support The Marine Corps Irregular Warfare Study Colombia Scenario 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:25 March 2008

2. Perception of COIN Insurgent Pro-Insurgent Indifferent Pro-COIN Perception of Insurgency Examining Changes in Affiliations • 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

3. 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

4. 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. • Order of precedence for changes (highest to lowest): • Interaction Estimation Transition Effect on the targeted population (the Direct Effect) • Salience Transition Effect on population segments receiving information about events (the Indirect Effect) • Background Susceptibility Transition (the Ongoing Effect)

5. Modeling the Eight Population Segments • Each agent represents 1% of a population segment • Each population segment has 100 agents • This set-up allows small, but influential groups such as the Catholic Church (324 persons) to have the opportunity to reach as many people as would the Urban Poor (159,510 persons)

6. Modeling the Eight Population Segments • Each agent represents 1% of a population segment. These agents are divided into classes based on the initial population segment orientation distribution • Each agent’s Attributes 1 through 5 represent a percentage of the 1% of the population in a certain orientation group, with 1000 as 100% and 0 as 0%. These attributes change over time based on the various influences, representing changes from the initial orientation distribution 6

7. Modeling the Colombia Scenario in Pythagoras • 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

8. Applying Vulnerability in Pythagoras Vulnerability, described as a Markov effect, has been implemented as an incremental Attribute Changer. The diagonal values have been set to 0. Pythagoras has an attribute normalization feature that will re-adjust the attribute values to sum to 1000 Displaced Persons Pro-FARC Example: Displaced Vulnerability Matrix Displaced Persons Pro-FARC Attribute Changes 8

9. Salience Related to 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

10. 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

11. Pythagoras Implementation of Salience 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% 13

12. Conceptual Model 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

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

14. Pythagoras Implementation of Influence Estimation 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, and the right Attributes by 7.21 based on the orientation sector being targeted 17

15. Further Development-Implied Study Objectives Can Pythagoras be used to model population dynamics, and, if so, how is that accomplished? Algorithm construction Data identification Data collection Data interpretation (Words To Numbers) Data preparation Analytic processes (e.g., Data Farming) Should the MAGTF deploy ashore or should it remain afloat? 18

16. Questions?