A System Dynamics (SD) Approach to Modeling and Understanding Terrorist Networks

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A System Dynamics (SD) Approach to Modeling and
Understanding Terrorist Networks

• BAA-07-01-IFKA Proactive Intelligence (PAINT)
• Model Development
• Massachusetts Institute of Technology (MIT)
• Sloan School of Management
• Political Science Department
• Engineering Systems Division
• and National Security Innovations, Inc. (NSI)

V11 2007-02-22
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Agenda

• Team
• What is System Dynamics (SD) Modeling
• Why is SD Modeling important
• Challenge Problem to be addressed
• Example of SD Modeling
• Collaboration with other PAINT areas
• Metrics Validation
• Management of Model Complexity
• Key Sub-systems
• Tasks, Deliverables Timetable
• Conclusion

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Key Personnel

• Massachusetts Institute of Technology (MIT)
• Stuart Madnick, Sloan School of Management,
  Information Technologies School of Engineering,
  Engineering Systems Division
• Nazli Choucri, School of Humanities and Social
  Sciences, Political Science Department
• Michael Siegel, Sloan School of Management,
  Information Technologies
• National Security Innovations, Inc. (NSI)
• Robert Popp, Founder and Chairman
• Greg Ingram, Vice President for Operational
  Technology
• All Key Personnel have considerable experience
  with the organization and management of
  large-scale projects that combine modeling and
  diverse data with application requirements in
  related areas such as DARPAs Pre-Conflict
  Analysis and Shaping (PCAS) effort

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• Philosophy of System Dynamics
• Every action has consequences
• Often through complex non-linear feedback loops
• Human are good at understanding individual
  pieces,
• but difficult at comprehending the full impact

Do you feel crowded in and frustrated?
5
See if you can get a bit more space by pushing on
that wall
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Oops
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History of System Dynamics Modeling (SDM)

• SDM used as modeling simulation method over 30
  years
• Eliminate limitations of linear logics and
  over-simplicity
• Typical human assumptions and behaviors
• Better understanding system structure,
  behavior patterns,
• interconnections of positive negative
  feedback loops, and
• intended unintended consequences of action
• SDM has been applied to numerous domains, e.g.,
• Software development projects
• Process Improvement projects
• Crisis and threat in the world oil market
• Stability and instability of countries
• many many others
• SDM helps to uncover hidden dynamics in system
• Helps understand unfolding of situations
• Helps anticipate predict new modes
• Explore range of unintended consequences

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Appropriateness of Modeling Methodologies(adapted
from Axelrod, 2004 Modeling Security Issues of
Central Asia)

• Ideally, first three criteria should be Low, and
  the last three criteria should be High.
• The Criteria
• Construction Time. Time and effort needed for a
  modeler skilled in this methodology to build a
  useful model with input from users.
• User Prerequisites. Amount of technical
  background needed by the user to understand as
  well as use the model.
• Learning Time. Time and effort for a typical user
  with the necessary prerequisites needs to learn a
  specific model.
• Flexibility. Ease with which the modeler can
  modify the model to incorporate a new variable.
• Repertory size. The number of published models of
  this type with features that could be adapted for
  use as part of a model on issues relevant to
  security in central Asia.
• Transparency. The ease with which the user can
  discover anything in the model that might bias
  the results.

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Unique Capabilities of System Dynamics Modeling

• Objective input Utilize data to determine
  parameters affecting the causality of individual
  cause-and-effect relationships.
• Subjective (expert) judgment Represent and model
  cause-and-effect relationships, based on expert
  judgment.
• Intentions Analysis Identify the long-term
  unintended consequences of policy choices or
  actions taken in the short term
• Tipping point analysis Identify and analyze
  tipping points where incremental changes lead
  to significant impacts.
• Transparency Explain the reasoning behind
  predictions and outputs of the SD model.
• Modularity Can organize SD models into
  collections of communicating sub-models (e.g.,
  terrorism recruitment, economic development,
  religious intensity, regime stability)
• Scalability Use the modularity to increase
  complexity without becoming unmanageable.
• Portability Utilize the same basic SD model in
  different regions of the world without requiring
  re-formulation.
• Focusability Increase details in specific areas
  of the SD model to address specific (and possibly
  new) issues.

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PAINT Challenge Problem

• How should the Government analyze terrorist
  networks in the context of the political,
  religious, social and economic networks that
  intersect with, influence, and are influenced by,
  the terrorist network predict the formation,
  evolution, vulnerabilities, and dissolution of
  the network and identify strategies to shape or
  influence the network through selective action?

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Example of System Dynamics ModelingDissident
and Terrorist Network Escalation(very simplified)
Factors that affect rate of Flow
Flows
Avg Time as
Dissident
Stocks
Desired Time to
Appeasement
Remove
Terrorists
Fraction
Appeasement
Rate
Removed
Terrorists
Dissidents
Population
Terrorists
Removing
Terrorist
Becoming
Births
Terrorists
Recruitment
Dissident
Regime
Recruits Through
Opponents
Social Network
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Dissident and Terrorist Network Development
(slightly more detailed)
Fifth-order system of non-linear differential
equations gt 140 equations gt 100 feedback loops
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Sample of Structure to Equations Recruitment
Section
Stocks
Variables
Parameters
RO DT TP PDT FCWRO RO/TP TC PI CBOP
TCFCWRO RTSN CBOPCPR
P INTG(PG-BD)dtPo D INTG(TR-BD)dtDo T
INTG(TR)dtTo
FGR 0.001706 I 0.4 CPR 0.1
Flows
PG PFGR BD RTSN
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Example Intervention Policies Removing
Terrorists vs. Preventing Recruitment
Increased Removal Effectiveness
Avg Time as
Dissident
Desired Time to
Remove
Appeasement
Terrorists
Fraction
Appeasement
Rate
Removed
Dissidents
Terrorists
Population
Terrorists
Removing
Insurgent
Becoming
Births
Terrorists
Recruitment
Dissident
Propensity to
Commit Violence
Violent Incident
Intensity
Regime
Relative Strength
Recruits Through
Protest
Opponents
of Violent Incidents
Social Network
Intensity
Normal Propensity
Incident
to be Recruited
Intensity
Propensity to
Protest
Propensity to be
Recruited
Effect of Incidents on
Effect of Regime
Anti-Regime
Resilience on
Messages
Recruitment
Regime
Effect of Anti-Regime
Resilience
Message Effect Strength
Messages on
Perceived Intensity
Recruitment
Social
of Anti-Regime
Capacity
Messages
Political
Regime
Capacity
Legitimacy
Preventing Recruitment
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Example Intervention Policies Removing
Terrorists vs. Preventing Recruitment
Terrorists
27,000
25,250
23,500
21,750
20,000
2005
2006
2007
2008
2009
2010
Time (Year)
Removing terrorists has a limited effect
Preventing recruitment effects a sustained
reduction
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Collaboration with other PAINT areas
Architecture and Integration, Key Indicators,
Dynamic Gaming and Strategies

• Worked with other potential PAINT researchers,
  such as in PCAS.
• Expertise that we can contribute to the overall
  PAINT effort.
• Architecture and Integration
• Innovative IT Architectures for Integration are
  major research foci for our MIT group at MIT.
• Context Interchange Using Knowledge about Data
  to Integrate Disparate Sources, was projects
  under DARPAs Intelligent Integration of
  Information (I3) research program - further
  improved and tested in various environments,
  including a recent project to facilitate the
  integration of intelligence data.
• Key Indicators
• Key Indicators are important part of our
  proposed work on the PAINT effort. We have
  experience with identifying and understanding Key
  indicators in other projects.
• Dynamic Gaming and Strategies
• System Dynamics extensively used by MIT in
  dynamic gaming, called management flight
  simulators to demonstrate how managerial
  instincts often lead to counter-intuitive and
  erroneous results.

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What if Virtual / Gaming mode - Parameter
Inputs with Sliders
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Metrics Validation

• Many ways to validate a System Dynamics model
• 12 ways on p. 6 of proposal
• we will use all of them two are below
• Behavioral Reproduction
• Use past data (as well as other sources) to help
  determine parameters up to, say, two years ago.
• Each stock (e.g., number of terrorists) is a
  metric.
• Measure how well SD model projections match the
  following years
• planned changes, known 2 years ago, to policy are
  included.
• In PCAS effort, our SD model predictions were
  very accurate.
• System Improvement
• Does the model generate useful insights that are
  appreciated by decision makers?
• In PCAS effort, our results were presented to
  PACOM, etc.

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Managing Model Complexity

• A model should be as simple as possible and only
  as complex as needed. Unneeded complexity will
  be avoided in this project.
• The primary method to manage SD model complexity
  is the use of subsystems (which can be further
  decomposed into sub-subsystems, if needed.)
• Our current plan is divide our High Level Model
  (HLM) into at least three major subsystems
• (a) regime resilience
• (b) terrorist network activities and growth.
• (c) government capacity interactions with
  terrorist networks
• Each of these subsystems have internal dynamics
  as well as dynamic interactions with the other
  subsystems.
• Multi-level layer approach simplifies the
  complexity both in model development and
  refinements as well as model usage and
  understanding.
• Used very effectively in many SD modeling
  projects.

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Proposed Tasks Timetable(timetable on p. 16,
details of 36 tasks on pp. 23-26 of proposal)

• Working Integrated SD model delivered each year
  and improved each year.
• Phase 1 (18 months) Component Predictive Models
  Integrated into a Virtual World/Dynamic Gaming
  Collaborative
• Key task is to design, develop, and complete the
  High Level Model (HLM) including all sub-systems
  (a) regime resilience, (b) terrorist network
  activities and growth, and (c) government
  capacity and interactions with terrorists.
• Basic data for the HLM compiled to provide an
  empirical view of the overall model.
• Phase 2 (12 months) Prediction Using Specific
  Challenge Problem with Historical or Synthetic
  Data
• All subsystems enhanced focus on improving the
  regime resilience sub-system.
• Phase 3 (12 months) Prediction using Real World
  Data Instrumentation, Feedback and Fine tuning
• All subsystems enhanced focus on the terrorist
  network activities and growth sub-system
• Phase 4 (12 months) Grand Challenge Problem
  Influence Strategies for Alternative Futures
• All subsystems enhanced focus on the government
  capacity sub-system and interactions with
  terrorists development and analysis of
  strategies leading to better improved alternative
  futures.

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Conclusions

• System Dynamics methodology important and
  critical method for addressing the broad scope of
  PAINT.
• SD has been shown effective is related efforts
  (e.g., PCAS).
• We have assembled superb multi-disciplinary team
• We are committed to the success of PAINT.
• Thank you.

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Backup Slides For QA
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Quick Primer What (and Why) of System Dynamics

• Consider the domain of Software Development
• Knee jerk reaction to a project behind schedule
  is to add people.
• Brooks Law noted that Adding people to a late
  project, just makes it later
• Because the new people must be trained, this
  takes productive people off the project which
  was not obvious before.
• These points are now fairly well-known by most
  software developers but still naïve.
• Many other factors length of project, type of
  project, expertise of staff available, approach
  to and time needed to do training, stage of
  project, etc.
• Over the years, all of these individual factors
  have been well-studied individually but how do
  they interact ?
• System dynamics helps model study the dynamics
  of the interdependencies. Non-obvious outcomes
  frequently found.
• (e.g., sometimes Brooks is wrong! When and
  Why?)
• Source Software Project Dynamics An Integrated
  Approach, by T.K. Abdel-Hamid and S. Madnick,
  Prentice-Hall, 1991,
• and Fred Brooks, The Mythical
  Man-Month, 1975.

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Validation of System Dynamics Models

• Boundary Adequacy Does the selection of what is
  endogenous, exogenous, and excluded make sense?
• Structure Assessment Is the level of aggregation
  correct, and does the structure conform to
  reality?
• Dimensional Consistency Do the units of the
  model make sense, and does the model avoid the
  use of arbitrary scaling factors?
• Parameter Assessment Do the parameters have real
  life meanings, and are their values properly
  estimated?
• Extreme Conditions Do extreme parameter values
  lead to irrational behavior?
• Integration Error Does the behavior change when
  the integration method or time step are changed?
• Behavioral Reproduction How well does the model
  behavior match the historical behavior of the
  real system?
• Behavior Anomaly Does changing the loop
  structure lead to anomalous behavior consistent
  with the changes?
• Family Member How well does the model scale to
  other members within the same class of systems?
• Surprise Behavior What is revealed when model
  behavior does not match expectations?
• Sensitivity Analysis Do conclusions change in
  important ways when assumptions are varied over
  their plausible range? Changes in conclusions
  could be numerical changes, behavior mode
  changes, or policy changes.
• System Improvement Does the model generate
  insights that actually lead to the hoped for
  improvements?

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What if Virtual / Gaming mode - Parameter
Inputs with Sliders
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Example End-User (Non-Technical) Interface Design
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Resumes of Key Personnel - MIT

• Dr. Stuart Madnick is the John Norris Maguire
  Professor of Information Technology, Sloan School
  of Management and Professor of Engineering
  Systems, School of Engineering at the
  Massachusetts Institute of Technology. He has
  been a faculty member at MIT since 1972. He has
  served as the head of MIT's Information
  Technologies Group for more than twenty years. He
  has also been a member of MIT's Laboratory for
  Computer Science, International Financial
  Services Research Center, and Center for
  Information Systems Research. Dr. Madnick is the
  author or co-author of over 250 books, articles,
  or reports including the classic textbook,
  Operating Systems, and the book, The Dynamics of
  Software Development, which received the Jay
  Wright Forrester Award for "Best Contribution to
  the field of System Dynamics in the preceding
  five years" awarded by the System Dynamics
  Society. His current research interests include
  connectivity among disparate distributed
  information systems, database technology,
  software project management, and the strategic
  use of information technology. He is presently
  co-Director of the PROductivity From Information
  Technology Initiative and co-Heads the Total Data
  Quality Management research program. He has been
  active in industry, as a key designer and
  developer of projects such as IBM's VM/370
  operating system and Lockheed's DIALOG
  information retrieval system. He has served as a
  consultant to corporations, such as IBM, ATT,
  and Citicorp. He has also been the founder or
  co-founder of high-tech firms, including
  Intercomp, Mitrol, and Cambridge Institute for
  Information Systems, iAggregate.com and currently
  operates a hotel in the 14th century Langley
  Castle in England. Dr. Madnick has degrees in
  Electrical Engineering (B.S. and M.S.),
  Management (M.S.), and Computer Science (Ph.D.)
  from MIT. He has been a Visiting Professor at
  Harvard University, Nanyang Technological
  University (Singapore), University of Newcastle
  (England), Technion (Israel), and Victoria
  University (New Zealand).

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Resumes of Key Personnel (continued) - MIT

• Dr. Nazli Choucri is Professor of Political
  Science at the Massachusetts Institute of
  Technology, and Director of the Global System
  for Sustainable Development (GSSD), a distributed
  multi-lingual knowledge networking system to
  facilitate uses of knowledge for the management
  of dynamic strategic challenges. To date, GSSD
  is mirrored (i.e. synchronized and replicated) in
  China, Europe, and the Middle East in Chinese,
  Arabic, French and English. As a member of the
  MIT faculty for over thirty years, Professor
  Choucris area of expertise is on modalities of
  conflict and violence in international relations.
  She served as General Editor of the International
  Political Science Review and is the founding
  Editor of the MIT Press Series on Global
  Environmental Accord. The author of nine books
  and over 120 articles Professor Choucris core
  research is on conflict and collaboration in
  international relations. Her present research
  focus is on connectivity for sustainability,
  including e-learning, e-commerce, and
  e-development strategies. Dr. Choucri is
  Associate Director of MITs Technology and
  Development Program, and Head of the Middle East
  Program. She has been involved in research,
  consulting, or advisory work for national and
  international agencies, and in many countries,
  including Abu Dhabi, Algeria, Canada, Colombia,
  Egypt, France, Germany, Greece, Honduras, Japan,
  Kuwait, Mexico, North Yemen, Pakistan, Qatar,
  Sudan, Switzerland, Syria, Tunisia, Turkey
• Dr. Michael Siegel is a Principal Research
  Scientist at the MIT Sloan School of Management.
  He is currently the Director of the Financial
  Services Special Interest Group at the MIT Center
  For eBusiness. Dr. Siegels research interests
  include the use of information technology in
  financial risk management and global financial
  systems, eBusiness and financial services, global
  ebusiness opportunities, financial account
  aggregation, ROI analysis for online financial
  applications, heterogeneous database systems,
  managing data semantics, query optimization,
  intelligent database systems, and learning in
  database systems. He has taught a range of
  courses including Database Systems and
  Information Technology for Financial Services. He
  currently leads a research team looking at issues
  in strategy, technology and application for
  eBusiness in Financial Services.

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Resumes of Key Personnel (continued) NSI

• Dr. Robert Popp is cofounder of National Security
  Innovations (NSI), Inc., presently serving as its
  Chairman and CEO. Prior to NSI, Dr. Popp served
  as Executive Vice President of Aptima, Inc. Prior
  to Aptima, Dr. Popp served for five years as a
  senior government executive within the Defense
  Department one year at the Office of the
  Secretary of Defense as Assistant Deputy
  Undersecretary of Defense for Advanced Systems
  and Concepts, and four years at the Defense
  Advanced Research Projects Agency (DARPA). At
  DARPA, Dr. Popp served as Deputy of the
  Information Awareness Office (IAO) where he
  oversaw a portfolio of over 25 programs exceeding
  170M focused on novel IT-based tools for
  counter-terrorism, foreign intelligence and
  national security. Dr. Popp was also Deputy PM to
  Dr. Poindexter on the Total Information Awareness
  (TIA) program, a program that integrated and
  experimented with analytical tools in text
  processing, collaboration, decision support,
  foreign languages, predictive modeling, pattern
  analysis, and privacy. Dr. Popp also served as
  Deputy of the Information Exploitation Office
  (IXO), where he established a novel research
  thrust in stability operations and
  quantitative/computational social science
  modeling for nation state instability and
  conflict analysis. Prior to government service,
  Dr. Popp held senior positions with ALPHATECH,
  Inc. (now BAE Systems) and BBN. He has served on
  the Defense Science Board (DSB), is a Senior
  Associate for the Center for Strategic and
  International Studies (CSIS), and is a founding
  Fellow of the Academy of Distinguished Engineers
  at the University of Connecticut. Dr. Popp also
  served in the military from 1982 1988 as a
  Staff Sergeant in the US Air Force as an Aircraft
  Maintenance Technician of F106 fighters and B52
  bombers. Dr. Popp holds a Ph.D in Electrical
  Engineering from the University of Connecticut,
  and a BA/MA in Computer Science (summa cum laude,
  Phi Beta Kappa) from Boston University.
• Gregory J. Ingram is the Vice President for
  Operational Technology for National Security
  Innovations (NSI), Inc. He has twenty-four years
  of experience in the Army in the fields of
  Special Forces, Infantry, Civil Affairs, and
  Psychological Operations (PSYOP). Fifteen of his
  twenty-four years have been on active duty and
  the remainder in the reserves. He has deployed
  in various capacities to Lebanon, Italy, Chile,
  Korea, Haiti, Afghanistan, and Iraq. For the
  last five years, Greg has been heavily involved
  in developing, integrating, and operationalizing
  leading-edge technologies in the areas of
  knowledge discovery, planning and analysis, human
  language technologies, and quantitative social
  science methodologies. Greg served as the lead
  PSYOP/IO Planner in the Special Operations Joint
  Interagency Collaboration Center (SOJICC) and as
  an Operational Manager for the development of the
  PSYOP Planning and Analysis System (POPAS) as
  part of the PSYOP Global Reach (PGR) Advanced
  Concept Technology Demonstration (ACTD) at the
  United States Special Operations Command
  (USSOCOM).