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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 – PowerPoint PPT presentation

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


1
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
2
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

3
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

4
  • 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
6
Oops
7
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


8
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.

9
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.

10
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?

11
Example of System Dynamics Modeling Dissident
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
12
Dissident and Terrorist Network Development
(slightly more detailed)
Fifth-order system of non-linear differential
equations gt 140 equations gt 100 feedback loops
13
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
14
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
15
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
16
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.

17
What if Virtual / Gaming mode - Parameter
Inputs with Sliders
18
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.

19
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.

20
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.

21
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.

22
Backup Slides For QA
23
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.

24
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?

25
What if Virtual / Gaming mode - Parameter
Inputs with Sliders
26
Example End-User (Non-Technical) Interface Design
27
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).

28
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.

29
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).
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