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# Irregular Warfare Project - PowerPoint PPT Presentation

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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MCCDC Operations Analysis Division (OAD) January 2008

Present Status of the MCCDC OAD Irregular Warfare (IW) Project

Project Goal: Develop a prototype methodology for analyzing a USMC IW problem in-house

• IW Modeling Challenge

• Conceptual Model

• Scenario Background

• Data Acquisition

• How IW Data is used in our Model

Military OR Analyst Comfort Zone

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

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

* Statistics for Business and Economics by Anderson, Sweeney, and Williams

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

Civilian Population

FARC

GoC

• 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

• 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

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

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

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

• 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

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

• 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:

• 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

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?”

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

Multiply

Normalize

Example: Urban Poor

• Influence of other population segments

• “What words would this population segment use to describe another population segment?”

Influence of “x-axis” on “legend”

Multiply

Normalize

Example: Urban Poor

• 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