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

• Conceptual Model

• Scenario Background

• Data Acquisition

• How IW Data is used in our Model

IW Domain

The IW Modeling Challenge

Combat Model

Weapon Pk

Armor Thickness

Vehicle Speed

Lethality

Survivability

IW Model

Influence

Susceptibility

Information Ops

Attitude

Behavior

Population Response

Killer-Victim AdjudicationMilitary OR Analyst Comfort Zone

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.*

* 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

Modeling human behavior involves a higher level of uncertaintythan modeling traditional force-on-force combat

FARC Pro-FARC Neutral Pro-GoC GoC

Conceptual Model ofCivilian Population

Insurgency Behavior Orientation

CivilianPopulation

PopulationSegments

FARC = Revolutionary Armed Forces

of Colombia

GoC = Govt of Colombia

FARC

GoC

Conceptual Model ofCivilian Population

The desire to fulfill, alleviate, or eliminate perceived needs motivates behavioral

change

Different people in the same situation will not have the same perceived needs

Psychological Operations Doctrine

Narrative Paradigm

• People are essentially storytellers

• The world is a set of stories from which each individual chooses the ones that match his or her values

• Although people claim "good" reasons for their decisions, these reasons include history, culture, & perceptions about the status and character of the other people involved

Colombia Scenario•Background• MAGTF Mission:

• Refugee Camp Security• Humanitarian Assistance / Disaster Relief

• 2 Possible Courses of Action (COAs)

• Sea-Based• Shore-BasedProvide:

Joint “Cultural” Prep of the Operational Environment

Plausible Range of Civilian Population Behaviors

SME Interviews

• 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

Scenario Data

• Cultural data narrowly focused on this region• Data is not accurate for the rest of Colombia

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 Required

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)1. Natural tendency of the population segment

• The population segment’s narrative with respect to the insurgency

2. Effect of current events on population segment (impact)• How the population segment reacts to a given COA

3. 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

Orientation Data

• 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

FARC Pro-FARC Neutral Pro-GoC GoCUrban Poor 5.8% 9.2% 65.7% 15.1% 4.2%

FARC Pro-FARC Neutral Pro-GoC GoCFARC 99.4% 0.3% 0.2% 0.0% 0.0%

Pro-FARC 0.1% 98.0% 1.3% 0.5% 0.0%Neutral 0.0% 0.4% 99.1% 0.5% 0.0%

Pro-GoC 0.0% 0.0% 1.2% 98.5% 0.3%GoC 0.0% 0.0% 0.1% 0.1% 99.8%

Initial Orientation

0%

20%

40%

60%

80%

100%

CatholicChurch

DisplacedPersons

IllicitOrganizations

Military Old Money Police Urban MiddleClass

Urban Poor

FARC Pro-FARC Neutral Pro-GoC GoC

Data Elicitation

• 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:

• 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 measure

Rolled up to a single parameter = E * sqrt (P2+A2)

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?”

Word Impact

agree with 1.05

authorize 1.21

believe 1.62

please 2.93

appreciate 3.24

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Illicit Organizations Displaced Persons Urban Poor Catholic Church

Infl

uen

ce

Lef

t

--

Infl

uen

ce

Rig

ht

Shore - left Shore - right Sea - left Sea - right

• 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

Impact of Shore/Sea Base

Impact of Shore Base

Example: Urban Poor

Multiply

Normalize

Left Right2.11 7.22

FARC Pro-FARC Neutral Pro-GoC GoCFARC 99.4% 0.3% 0.2% 0.0% 0.0%

Pro-FARC 0.1% 98.0% 1.3% 0.5% 0.0%Neutral 0.0% 0.4% 99.1% 0.5% 0.0%

Pro-GoC 0.0% 0.0% 1.2% 98.5% 0.3%GoC 0.0% 0.0% 0.1% 0.1% 99.8%

FARC Pro-FARC Neutral Pro-GoC GoCFARC 2.10 0.03 0.02 0.00 0.00

Pro-FARC 0.00 2.07 0.10 0.04 0.00Neutral 0.00 0.01 3.87 0.03 0.00

Pro-GoC 0.00 0.00 0.03 7.11 0.02GoC 0.00 0.00 0.00 0.00 7.20

FARC Pro-FARC Neutral Pro-GoC GoCFARC 97.8% 1.2% 0.8% 0.2% 0.0%

Pro-FARC 0.1% 93.8% 4.4% 1.7% 0.0%Neutral 0.0% 0.2% 98.9% 0.9% 0.0%

Pro-GoC 0.0% 0.0% 0.4% 99.4% 0.3%GoC 0.0% 0.0% 0.0% 0.0% 99.9%

Influence Elicitation

• Influence of other population segments – “What words would this population segment

use to describe another population segment?”

Word Influence

unsuccessful -2.41

impotent -1.53

cowardly -2.50

inexperienced -2.25

mediocre -1.47

average -0.14

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Catholic Church Displaced Persons Illicit Organizations Urban Poor

Infl

uen

ce

Catholic Church Displaced Persons Illicit Organizations Urban Poor

Influence of other Segments

Influence of “x-axis” on “legend”

Multiply

Normalize

Influence of Population Interactions

Example: Urban Poor

Left Right0.05 0.16

FARC Pro-FARC Neutral Pro-GoC GoCFARC 99.4% 0.3% 0.2% 0.0% 0.0%

Pro-FARC 0.1% 98.0% 1.3% 0.5% 0.0%Neutral 0.0% 0.4% 99.1% 0.5% 0.0%

Pro-GoC 0.0% 0.0% 1.2% 98.5% 0.3%GoC 0.0% 0.0% 0.1% 0.1% 99.8%

FARC Pro-FARC Neutral Pro-GoC GoCFARC 0.05 0.00 0.00 0.00 0.00

Pro-FARC 0.00 0.05 0.00 0.00 0.00Neutral 0.00 0.00 0.09 0.00 0.00

Pro-GoC 0.00 0.00 0.00 0.16 0.00GoC 0.00 0.00 0.00 0.00 0.16

FARC Pro-FARC Neutral Pro-GoC GoCFARC 98.0% 1.1% 0.8% 0.2% 0.0%

Pro-FARC 0.1% 94.2% 4.1% 1.5% 0.0%Neutral 0.0% 0.2% 98.9% 0.9% 0.0%

Pro-GoC 0.0% 0.0% 0.4% 99.4% 0.3%GoC 0.0% 0.0% 0.0% 0.0% 99.9%

Quo Vadis?

• 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