1. Irregular Warfare Analysis and Validation with the Social Impact Model
Dr. Deborah Duong
Mr. Gerald Pearman
CPT Richard Brown

2. analysis and validation
Purpose and Agenda
Purpose: To show the usefulness of the Social Impact Model (SIM) for Irregular Warfare (IW)
analysis and validation
Agenda
SIM background.
Practical applications of the SIM.
SIM’s enabling technologies.
Proof of concept application to Tactical Wargame (TWG).
Results.
Summary.
Social Impact Model

3. SIM Background
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4. Background
The Social Impact Model (SIM) performs adjudication, analysis and validation of social impact in US Army TRADOC Analysis Center’s (TRAC) Tactical Wargame (TWG).
TWG and the models within the SIM are focal points of TRAC’s Irregular Warfare Analysis Capability (IWAC) initiative.
IWAC is the one of the largest IW analysis efforts in the Department of Defense.
The SIM is a federation of stand alone models and tools.
The Cultural Geography (CG) model at the Population level (TRAC-Monterey).
The Nexus Network Learner Model (NNL) at the Individual level (OSD,TRAC- Monterey).
More models to be added in 2011.
SIM middleware integrates and analyzes the models.
SIM middleware extends the eXtensible Behavioral Model (XBM) framework (OUSDI, TRAC-Monterey, CTTSO).
XBM implements basic versions of TRAC’s IW analysis designs.
The models and tools of the SIM are designed to solve problems in IW adjudication, analysis, and validation.
Each SIM component applies advanced artificial intelligence technologies to solve problems.
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5. SIM Components
The models that comprise the SIM take a principled approach to IW modeling, enabled by advanced technologies.
CG and NNL both include:
Cognitive Agent Based Modeling so that social phenomena is computed from first principles and is emergent rather than hard coded.
Bayesian Networks so that data can be read into and tracked in the model in a flexible manner.
Reinforcement Learning so that agents learn new behaviors from motivations over time, the true causes of new social structure, rather than being pre-determined towards desired structures.
Social Networks to emphasize the relational aspect of social structure.
Representation of the major schools of social theory (interpretive, materialist).
SIM middleware applies TRAC’s analysis and validation methods to the models, including:
Combinatorial Game Theory.
Probabilistic Ontologies (with logical and Bayesian inference engines).
Information Theory for model comparison and validation.
Markov Processes.
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6. Practical Applications of the SIM
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7. SIM enables you to…
Apply wargame data to rigorous simulation analysis.
Model an IW game of perceptions.
Translate disparate data between IW models.
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8. SIM Can Apply Wargame Data to Rigorous Simulation Analysis
SIM enables wargames to be run multiple times.
Most IW analyses incorporate wargames, but human-in-the-loop wargames are resource intensive and cannot span the realm of possibilities.
Risk based analysis requires that the analyst considers the realm of possibilities and their likelihoods.
Models can cover the possibilities if, when they are run out many times stochastically, moves are entered as a human player would enter them.
Human players respond to enemy moves and to the environment.
SIM enters moves using human player strategies from an actual wargame.
Both interviews and statistics on actual moves are used.
SIM can apply player strategies to disparate models.
Strategies in SIM are represented separately from model moves and then translated to models.
SIM can also translate strategic moves to models that were not used in the wargame.
SIM offers a rigorous method to improve wargames.
SIM enables measurement of the measurement space.
SIM models player perception, enabling analysts to test the effect of what human players can (or cannot) observe.
SIM exposes how to “game the game” .
Identifies design errors in models, helping analysts to improve models for adjudicating human games and for multiple stochastic runs.
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9. SIM can model an IW “Game of Perceptions”
SIM enables the modeling of IW moves and countermoves.
SIM turns multiple model runs into games of strategy.
Kilcullen described IW conflict as “co-evolutionary,” capturing the importance of moves and countermoves. His phrase, “Perception is Reality” depicts the idea that IW is a game of perception.
The movie, “The Battle of Algiers,” captures the importance of the timing of moves and countermoves in IW showing how insurgents went from unpopular to widespread public support by forcing the hand of the host nation and making the nation appear oppressive.
SIM enables the modeling of deception in Information Operations.
Automated players have mental models of the world that may be incorrect, so that one automated player may deceive another.
SIM enables the testing of IW doctrine and strategy.
Commander’s Intent, Goals, Decision Points, Branches and Sequels, can be tested and compared.
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10. SIM Translates Disparate Data between IW Models
SIM enables analysts to apply IW data from one unique situation in the world to a different but unique situation in a model.
Input data, data traded between federated models, calibration data, and testing set data all have uncertainty of match as well as translation difficulties because of the different concepts used to arrange different data.
SIM translates data to ensure one model “means the same thing” as another model.
If there is no exact match, SIM translates data probabilistically.
SIM enables hybrid modeling.
Analysts can couple models “loosely” , at the level of general patterns, or tightly, at the level of details.
SIM can integrate models at different levels of aggregation, for example, “tactical” and “operational/strategic”.
SIM enables data to be compared “apples to apples”.
Validation needs to be at the level of statistical patterns rather than single outcomes.
The real world is just one possible world, and simulations model many possible worlds.
We know a simulation is good if what is rare in the simulation is rare in the world, and what is common in the simulation is common in the world (under right circumstances).
SIM enables analysts to express data in statistical patterns and dynamics.
After SIM translates data to a common lexicon, SIM compares data at the level of statistical patterns and dynamics to calculate a ‘distance’ score that measures the statistical distance between dynamic patterns.
SIM’s distance score allows comparison of different versions of the same model, different scenarios, and models against data for a calibration or a validation score.
SIM can validate a model against multiple real-world data sources with uncertain matches to the model.
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11. SIM’s Enabling Technologies
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12. SIM Applies Advanced Technologies to IW Problems
Combinatorial Game Theory models strategic games.
Probabilistic Ontologies arrange and translate data.
Information Theory finds relations in data and compares data.
Markov Processes help analyze and validate models.
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13. Combinatorial Game Theory (Game Trees)
SIM uses Strategic Data Farming (SDF), an application of combinatorial game theory to optimize player moves according to player’s goals and strategies to assess best courses of action (COA).
Role players are modeled as automated agents that look ahead to results of moves assuming players are trying to achieve goals, in a simple, general cognitive model.
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14. Strategic Data Farming (SDF)
SDF models aspects of the Military Decision Making Process (MDMP).
Automated role players have the following as part of their COA strategy:
Decision Points: Points at which players will consider a change to its COA (nodes of the game tree).
COA Options: Options to consider at each decision point, specifying conditions under which it will be exercised and possible moves (branches of the game tree).
Goals: Ways to evaluate the situation within the move selection algorithm. Goals can also be interpreted as measures of effectiveness (MOEs) (game tree leaf evaluation).
Mental Models: Presumed strategy of other players (i.e., belief levels, decision points, COA options, move selection).
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15. Game Tree Example – Africa Use Case
Popular support levels (from Nexus):
G = 0.57
R = 1.0
Evaluation Function GE – (each side attempts to maximize their evaluation function):
GE = ((1-R)+G)/2 = 0.28
RE = 1-GE = 0.72
Disrupt alliance
between tribe J
and tribe D.
Conduct Civil Affairs.
Green
Action 1. 2.
GE: 0.5
GE: 0.35 (after looking ahead)
GE: 0.5
GE: 0.25 (after looking ahead)
Make tribe O, a green ally,
appear to harm tribe J.
Make green appear
to harm tribe J.
Red
Reaction 3. 4. 3. 4.
RE: 0.35
RE: 0.65
RE: 0.75
RE: 0.5
Without looking ahead, Green’s actions seem the same (both are .5). But by looking ahead to how Red would react, he finds action Disruption (action 1) (GE=1-.65=.35) is better than CA (action 2) (GE=1-.75=.25).
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16. Proof of Concept Application to TWG
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17. Player Strategy, Goals, Decision Points
As a first step to determine player strategy/intent, goals, decision points, and moves, the study team assessed all role player surveys and actual moves.
The matrix below is an excerpt of the assessment. The study team identified over 20 viable strategies from the surveys and moves.
Role Player
Intent (from surveys and actual moves)
Goal (Measurable evaluation function in SDF based on goal). Source is surveys or most likely common sense from knowledge of TWG.
Decision Point (measurable)
Moves
Don't Do Moves
Perceptions
CF Company
Security
Maximize Green and Blue OAB and decrease Red OAB
Green OAB decreases one step or if Red OAB increases one step.
Provide Security
Provide Legal
Red: Red trying to disrupt infrastructure, intimidate the population and prove that Blue and Green cannot protect population centers.
Dismounted Patrol
Hero Payments
Condolence Payments
Green OAB remains the same or Red OAB remains the same.
The study team also assessed point-wise mutual information (PMI) scores to determine strategy, goals, and decision points. The study team calculated PMI scores to determine the co-occurrence of kinetic or non-kinetic strategies and popular support levels.
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18. Using Point-wise Mutual Information (PMI)
PMI is a concept from Information Theory. PMI tells how uniquely a sign, such as popular support score, is associated with another sign, such as an action. We used PMI to tell how players actually reacted to popular support scores.
PMI quantifies the discrepancy between the probability of coincidence of two outcomes given their joint distribution and the probability of their coincidence given only their individual distributions, applying the equation1:
Tally of Green Moves versus Green popular support category.
Y-variable Move Categories (# of moves)
Blue kinetic
Blue non-kinetic
Green kinetic
Green non-kinetic
Red kinetic
Red non-kinetic
Totals
X-variable popular support observations
green OAB is PA
1958
776
2734
green OAB is PP
450
112
562
green OAB is NS
200
2674
2874
green OAB is NP
151
753
904
green OAB is NA
This category never occurred.
2759
4315
7074
Resulting PMI Scores [-1, +1].
-1 = never occurs together, 0 = independent, +1 = complete co-occurrence.
Pointwise mutual information told us how uniquely related moves were to certain popular support levels and changes in those levels. Our PMI analysis revealed that players only looked at their own popularity, and not at anyone elses.
Y-variable PMI Scores
Blue kinetic
Blue non-kinetic
Green kinetic
Green non-kinetic
Red kinetic
Red non-kinetic
X-variable
green OAB is PA
green OAB is PP
green OAB is NS
green OAB is NP 1
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19. Scenario Definition Scenario 1 – TWG 2010 implementation.
Scenario 1 mimics TWG 2010 implementation. Specifically, blue, green, and red role players attempted to maximize their own popularity. Blue and green wanted to become as popular as possible. Red was satisfied not to become unpopular.
Scenario 2 – Excursion. Same as TWG 2010 implementation, except blue attempted to maximize green popularity vice blue popularity.
Scenario 1.
Role Player
Goal
Decision Point
Strategy
Blue
Maximize blue popularity
Blue popularity increases
Kinetic Action
Blue popularity decreases
Non-kinetic Action
Green
Maximize green popularity
Green popularity increases
Green popularity decreases
Red
Maximize red popularity
Red popularity increases
Red popularity decreases
Note that one’s popular support is just an indicator and we now have a framework to measure the measurement space by studying the effect of player access to different indicators. We do so, with a set of runs that mimics the twg 2010 player strategies, where each player attempted to maximize their own popularity, and a set that does an excursion where blue maximizes greens popularity rather than its own.
Scenario 2.
Role Player
Goal
Decision Point
Strategy
Blue and Green
Maximize green popularity
Green popularity increases
Kinetic Action
Green popularity decreases
Non-kinetic Action
Red
Maximize red popularity
Red popularity increases
Red popularity decreases
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20. Iterative Hub and Spoke Architecture
Hybrid Model
Hybrid Model
Inference Engine
Inference Engine
Pave/CG Mediation Ontology
(Inference Engines:
Probabilistic Inference (Bayesian networks)
Logical Inference (Jena Micro OWL))
Pave/Nexus Mediation Ontology
(Inference Engines:
Probabilistic Inference (Bayesian networks)
Logical Inference (Jena Micro OWL))
Updated
Indicators
Pave
Hub
Ontology
Updated
Indicators
CG Move
CG Adjudication
Nexus Adjudication
Nexus Move
CG Ontology
(Cultural Geography Model)
Nexus Ontology
(Nexus Model)
Legend:
Input / Output
Ontology
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21. TWG 2010 Probabilistic Ontologies
CG ontology. Defines CG moves.
Nexus ontology. Defines Nexus moves.
PAVE ontology. Hub ontology for model. Contains PAVE moves and role player strategies, goals, and decision points.
PAVE CG Mediation ontology. Performs dynamic translation of PAVE tasks to CG moves.
PAVE Nexus Mediation ontology. Performs dynamic translation of PAVE tasks to Nexus moves.
Tactical Wargame 2010 ontology. Maintains states of the automated role player, such as OAB level/popularity and state of individual move.
Multi-Resolutional Bayesian ontology. Defines the macro and micro agents that are used to integrate multi-resolutional models.
TEO ontology. Defines events and outcomes.
Design of Experiment (DOE) ontology. Abstracts the concept of strategies, goals, and decision points in a doctrinal manner.
ProbOnt ontology. Holds the representation of the Bayesian networks that determine selection of events and outcomes.
Pakaf ontology. Holds the moves to the Helmand/PAKAF scenario.
PakafCgMediation. Automatically translates CG TWG moves to Helmand/PAKAF moves.
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22. Probabilistic Ontology Relationships
DOE
TEO
PAVE
ProbOnt
MultiResolutionalBayes
PaveCgMediation
ProbOnt
MultiResolutionalBayes
PaveNexusMediation CG
Nexus
Inheritance Hierarchy and Relationship Structure
of SDF Probabilistic Ontologies
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23. Logical Inference Indicators in Action
The logical inference engine (Jena Micro OWL) classified when decision points were triggered.
Yellow portion in the ontologies below indicate inferred states for coalition forces player, calculated from indicator definitions.
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24. Implementation of Event Probabilities
The study team implemented a probabilistic translation from the moves of one model to another using Bayesian networks.
SIM added Bayesian Networks, such as the one below, directly to the probabilistic ontology representation.
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25. Probabilistic Inference in Action
The example below illustrates two probabilistic translations from the same PAVE move (‘CS_CF’ – cordon and search by coalition force).
Micro agent #3 generated three CG events from the PAVE task, whereas micro agent #1 generated two CG events from the same PAVE task.
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26. Results
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27. Scenario 1 (TWG 2010). Blue Moves/Green Popularity
Level of Violence (blue player actions)
K = Kinetic
M = Medium Kinetic
N = Non-Kinetic
Popularity of Green
P = Popular
U = Unpopular
Level of Violence
Kinetic Medium Kinetic Non-Kinetic
.50
.43
.10
K P
M P
N P
Start Scenario
.14
.10
.20
.07
.70
Unpopular Popular
Level of Green Support
.36
.50
.40
.10
.29
.14
K U
M U
N U
.21
.40
.36
9/17/2018
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28. Scenario 2 (Excursion). Blue Moves/Green Popularity
Level of Violence (blue player actions)
K = Kinetic
M = Medium Kinetic
N = Non-Kinetic
Popularity of Green
P = Popular
U = Unpopular
Level of Violence
Kinetic Medium Kinetic Non-Kinetic
.14
.10
K P
M P
N P
Start Scenario
.10
.50
.14
.28
.70
Unpopular Popular
Level of Green Support
.50
.14
.10
.71
.14
K U
M U
N U
.57
.14
.28
.43
Probabilistic Distance from Scenario 1: 0.09
9/17/2018
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29. Validation – Markov Processes from Model and Real World
Popularity of Green
P = Popular
U = Unpopular
Level of Violence
K = Kinetic
M = Medium Kinetic
N = Non-Kinetic
State Definition
Model State Transitions
Real World (Afghan Nationwide Quarterly Assessment Review)
Apply Kullback-Leibler (KL) divergence to measure probabilistic distance between Markov processes:
Score of 0 means exact same Markov process. Score of 1 means the most different Markov process possible. Example above generated KL score = 0.21.
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30. Summary
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31. SIM IW Analysis and Validation Capabilities
Flexibility to define state spaces (i.e. type of actions by role player –popularity level) that align with the study questions based on doctrine and cognitive-based measurement spaces.
Ability to run out the wargame with perception based moves and countermoves keeping track of the likelihood of outcomes for risk based analysis.
Ability to examine model dynamics vice simply end of run output via Markov processes to assess tipping points.
Ability to compare data based on statistical patterns vice single outcomes.
Flexibility to translate, compare, and surrogate data.
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32. Questions and Comments
POC: Deborah Duong
Social Impact Model