Title: A System Dynamics (SD) Approach to Modeling and Understanding Terrorist Networks
1A 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
2Agenda
- 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
3Key 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?
5See if you can get a bit more space by pushing on
that wall
6Oops
7History 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
8Appropriateness 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.
9Unique 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.
10PAINT 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?
11Example 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
12Dissident and Terrorist Network Development
(slightly more detailed)
Fifth-order system of non-linear differential
equations gt 140 equations gt 100 feedback loops
13Sample 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
14Example 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
15Example 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
16Collaboration 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.
17What if Virtual / Gaming mode - Parameter
Inputs with Sliders
18Metrics 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.
19Managing 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.
20Proposed 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.
21Conclusions
- 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.
-
22Backup Slides For QA
23Quick 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.
24Validation 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?
25What if Virtual / Gaming mode - Parameter
Inputs with Sliders
26Example End-User (Non-Technical) Interface Design
27Resumes 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).
28Resumes 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.
29Resumes 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).