northrop grumman mission systems the pythagoras counterinsurgency application to support the marine...

Northrop Grumman Mission Systems

The Pythagoras Counterinsurgency Application To Support The Marine Corps Irregular Warfare

Study

In Progress Review #5

Mr. Edmund Bitinas 703-968-1196Ms. Donna Middleton 703-968-1657Ms. Brittlea Sheldon 703-968-1137Mr. Mitch Youngs 703-803-5997

Date: 22 May 2008

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Agenda

• Work To Date– Task 1 – Research– Task 2 – Develop Model– Task 3 – Implementation – Task 4 – Analysis – Task 5 – Recommend Modifications

• Status

– Task 6 – Reconfiguration• Sample Results

• VV&A Findings Regarding Pythagoras/COIN• The Road Ahead

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Task 1: Research

• USMC provided Akela Province Scenario• Identified numerous recent articles on Irregular

Warfare and Population Dynamics• Became familiar with Swing Function

Methodology

• Additional articles will be added as they are identified

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Task 2: 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

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Conceptual Model of a Population Segment

Insurgent Pro-COINIndifferentPro-Insurgent

Perception of COIN

Perception of InsurgencyEach 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

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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

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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

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Effect of Influence Estimation on Target Population

Population Segment A

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Insurgency Pro-Insurgency

Indifferent Pro-COIN

Orientation

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Initial Orientations

Revised Orientations

Population Segment A

Base Susceptibility * Strength of Event

Percent Change in Population Segment A from Initial State

Insurgency

Pro-Insurgency

Indifferent

Pro-COIN

-40.00%

-20.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Orientation

Per

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Percent Changefrom Initial State

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Example Influence Event

Insurgent Pro-Insurgent Indifferent Pro-COINInsurgent 0.850 0.120 0.025 0.005Pro-Insurgent 0.010 0.800 0.160 0.030Indifferent 0.005 0.185 0.700 0.110Pro-COIN 0.000 0.030 0.200 0.770

CounterInsurgencyCOIN Insurgency

Provide Security 8.000 4.000



Base Influence ValuesApply Influence

After MultiplyingAfter Normalizing

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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)

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Initial Implementation 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

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Initial Implementation In Pythagoras (cont.)

• Problem: In Pythagoras 1.10.5, one agent cannot influence all other agents simultaneously with different strengths

• Solution: Create ‘impulses’, multiple impulses equal one time step– One target population ‘lights up’– All other populations influence it– All populations change behaviors to prepare to

influence the next population in line– Results in dozens of alternate behaviors for each

population segment– Timing and sequencing are difficult

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Initial Implementation In Pythagoras (cont.)

• Added three generic attributes to Pythagoras 1.10.5.– Delta, Epsilon, and Zeta

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Task 3: Implementation (at OAD)

• Pythagoras 2.0.0 has been provided to OAD– Colombia (eight segment) was provided– Other new scenarios and test articles will also be

provided

• A short training session was conducted– Follow-up training via email and telephone is provided

as necessary

• Updates to Pythagoras 2.0.0 have been provided to OAD as changes and fixes have been made

Task 4: Analysis

• The Pythagoras implementation in Version 1.10.5 is ‘clumsy’– The problem is not object oriented (Pythagoras is object

oriented)– Impulses make expansion difficult

• Behavior changes have to be coordinated– Stealth factor of target goes to zero– Everybody has to pick up the right weapon– Events need their own impulse

– Population influence based on the influencing population’s current attribute value, not its most recent change

– Pythagoras software upgrades can improve the representation and make the scenario inputs simpler

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Task 5: Recommend Modifications

• TRAC-Monterey suggested Pythagoras improvements

• The ‘clumsy’ implementation of the Akela Province Scenario also suggested modifications were needed to ease scenario construction

• OAD agreed to software upgrades and improved functionality

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Changes from Pythagoras 1.10.5 to 2.0.0

• Migration to JAXB 2.0– JAXB 1.0 XML Binder has a

bug when working with large input files

– Will allow eventual import of terrain from GIS

– JAXB 2.0 is a completely different interface

• Upgrade to JAVA 1.5– Required to use JAXB 2.0– Stronger type casting has

resulted in thousands of compiler warnings

• Lists and iterators must be type cast

• Eliminated ‘special’ XML constructs

– 1.10.5 used ‘[X,Y]’ and ‘[R,G,B]’– 2.0.0 uses <x>X</x><y>Y</y>

and <r>R</r><g>G</g><b>B</b>

• Increased Play Box size– Max Play Box size is now 4000

x 4000

• Multi-dimensional visualization– Can map the variables: Red,

Green, Blue, Health, Attributes (1-10), X and Y position to the Playback display of Horizontal and Vertical Positions, Colors (Red, Green, Blue), and Transparency

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Changes from Pythagoras 1.10.5 to 2.0 (cont.)

• New Attribute Changer objects possessed by:

– Terrain,

– Weapons,

– Communication Devices or

– Agent Attribute Changes

• “Exclusive” and “Inclusive” options– Potential target must possess all or

any attributes, respectively

• Four categories for changes– Multiplier – Multiplies an attribute by

the given value

– Relative – Changes an attribute to become closer to the targeting agent’s value

– Absolute – Changes an attribute to the designated value

– Incremental – Adds or subtracts from the attributes current value

• Terrain may change Attributes – This can mimic the effect of a

Weapon of Mass Destruction (WMD)

– An Attribute Changer may be associated with the terrain

– Start and end times inputby the user

• Agent Attributes– Each Agent may hold up to ten

Attributes– Attributes may be normalized

(sum to 1000)– Attribute changes can be

accumulated or averaged– Attribute possession influences

targeting– Attributes may also effect

Sidedness (i.e. unit, friend, or enemy) calculations

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Changes from Pythagoras 1.10.5 to 2.0 (cont.)

• Agents may have up to ten of:– Sensors, – Weapons, – Communications Devices– Attributes

• Multiple targets per time step, requiring new Agent-Target pairing logic. An Agent

– Selects targets to put on its sorted target list

– Picks the best weapon for the best target

– Trims the list to the input maximum, removing targets for which the selected weapon is ineffective, and then low value targets

– Shoots each remaining target on its list in succession, one shot per target until the fire rate is reached, all rounds have been expended, or no further influence is possible

• Communication devices may possess Attribute Changers

– Agents listening on the channel with the Attribute Changer will receive attribute changes

– Attribute Changes ‘hop’ from agent to agent unless the agent passing the message along possesses his own Attribute Changer

– Agents receive a message only once per time step

• Behavior Change Measure of Effectiveness (MOE)

– Outputs Agent behavior name over time

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Status of Software Upgrades

• Pythagoras 2.0.0 was delivered to NPS in mid-April.– Up to version 19 of alpha– Two bugs fixed since the release

• User’s manual has been updated

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Changes To The Conceptual Model

• Added counter-insurgency affiliation– Members of the population actively performing

Counter-Insurgency activities

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Changes To The Conceptual Model (cont.)

• 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)

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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

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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

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Task 6: Reconfiguration

• Four segments of the Colombia Scenario (Displaced Persons, Urban Poor, Catholic Church, and Illicit Organizations) were modeled using the Pythagoras 1.10.5 to provide sample results as updates were being made from Pythagoras 1.10.5 to 2.0.0

• The original data was used to develop the initial model, using the time step of a month

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Task 6: Reconfiguration (cont.)

• Scenario development has continued 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

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• Vulnerability is implemented as an incremental Attribute Changer

• Displaced Persons Pro-FARC Example:– Displaced Vulnerability Matrix

FARC Pro-FARC Neutral Pro-GOVT

GOVT

FARC 99.8% 0.1% 0.0% 0.0% 0.0%

Pro-FARC 0.4% 99.5% 0.2% 0.0% 0.0%

Neutral 0.0% 0.7% 98.7% 0.5% 0.0%

Pro-GOVT 0.0% 0.0% 0.9% 99.1% 0.1%

GOVT 0.0% 0.1% 0.0% 0.1% 99.7%

Attribute 1 Attribute 2 Attribute 3 Attribute 4 Attribute 5

+4 0 +2 0 0

Displaced Persons Pro-FARC Attribute Changes

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• 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%

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• 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

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Pythagoras Colombia Scenario Results

Urban Middle Class Orientation Changes, No MAGTF action

The graph below shows the resulting orientation changes based on background vulnerabilities and interacting population segments

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0 2 4 6 8 10 12 14 16 18 20 22 24

Months

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FARC Pro-FARC Neutral Pro-COIN COIN

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Pythagoras Colombia Scenario Results (cont.)

MAGTF Off-Shore, Urban Middle Class Orientation Changes

The graphs on the following slides display the cumulative results of MAGTF actions on the Urban Middle Class

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0 2 4 6 8 10 12 14 16 18 20 22 24

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MAGTF On-Shore, Urban Middle Class Orientation Changes

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• The graphs demonstrate significant differences in the population dynamics as a result of implementing either on-shore or off-shore MAGTF actions

• This impact is seen directly in the changes in the Catholic Church population segment, there is a less significant impact on the indirectly affected population segments

• As the MAGTF directly targets more than one population segment, the differences between the on-shore and off-shore actions appear to be less significant

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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

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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)

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%

Ashore or Afloat ResultsPercent Population Pro-COIN, COIN

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No

MA

GT

F

Ash

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Afl

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Catholic Church Displaced Persons Illicit Organizations Military Old Money Police Urban MiddleClass

Urban Poor

Pe

rce

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Afloat has nearly equal or more Pro-COIN, COIN

Ashore or Afloat Results (cont.)Percent Population Pro-FARC, FARC

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No

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F

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No

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No

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No

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No

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No

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Catholic Church Displaced Persons Illicit Organizations Military Old Money Police Urban Middle Class Urban Poor

Pe

rce

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Po

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Afloat has the same or fewer Pro-FARC, FARC

39

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 increase the negative impact of Afloat

40

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

Deliverables

• Draft Final Report (April, 2008)• Pythagoras User’s Manual (March, 2008)

We have not yet received any Government comments on either

Last Steps

• Create a new Appendix for the report that describes the data farming runs and results

• Provide additional data to the Verification and Validation effort

• Fix any bugs that are found (none in May)• Incorporate Government comments (if any) into

the two final deliverables• Attend MORS in June

– Demonstrate Pythagoras– Present papers

43

Questions?

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