National Center for Risk and Economic Analysis of Terrorism Events
University of Southern California
Los Angeles, California
Modeling Terrorist Beliefs and Motivations
October 2010 to September 2011
Richard John
University of Southern California
richardj@usc.edu
&
Heather Rosoff
University of Southern California
rosoff@usc.edu
"This research was supported by the United States Department of Homeland Security through the
National Center for Risk and Economic Analysis of Terrorism Events (CREATE) under
Cooperative Agreement 2007-ST-061-RE0001. However, any opinions, findings, and
conclusions or recommendations in this document are those of the authors and do not necessarily
reflect views of the United States Department of Homeland Security or the University of
Southern California."
Cooperative Agreement No. 2010-ST-061-RE0001
Department of Homeland Security
December 31, 2011
ABOUT CREATE
The National Center for Risk and Economic Analysis of Terrorism Events (CREATE) was the
first university-based Center of Excellence (COE) funded by University Programs of the Science
and Technology (S&T) Directorate of the Department of Homeland Security (DHS). CREATE
started operations in March of 2004. This annual report covers the seventh year of CREATE
funding from October 2010 to September 2011, the first year under Cooperative Agreement
2010-ST-061-RE0001 from DHS. While the text of this report focuses on the seventh year, all
data tables, publications, lists of participants, students, and presentations and events are
cumulative from the inception of CREATE.
CREATE’s research mission is to develop advanced models and tools for risk assessment,
economic assessment, and risk management to counter terrorism. CREATE accomplishes this
mission through an integrated program of research, education, and outreach, spanning the
disciplines of economics, psychology, political science, industrial and systems engineering and
information science. CREATE develops models, analytical tools, methodologies and software,
and tests these tools in case analyses, representing critical homeland security investment and
policy decisions.
Due to the cross-cutting nature of research in risk, economics, and risk management, CREATE
serves the need of many client agencies at the DHS, including the Transportation Security
Agency, Customs and Border Protection, Immigration and Customs Enforcement, FEMA and the
US Coast Guard. In addition, CREATE has developed relationships with clients in the Offices
of National Protection and Programs, Intelligence and Analysis, General Council, Health Affairs,
and Domestic Nuclear Detection. Using a mix of fundamental and applied research, CREATE
faculty and students take both the long-term view of how to reduce terrorism risk through
fundamental research and the medium-term view of how to improve the cost-effectiveness of
counter-terrorism policies and investments through applied research.
Please visit www.create.usc.edu for more information.
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Modeling Terrorist Beliefs and Motivations
Richard John and Heather Rosoff, University of Southern California
richardj@usc.edu, rosoff@usc.edu
1.
2.
3.
3.1.
3.2.
4.
Modeling Terrorist Beliefs and Motivations Overview ........................................................................................3
Research Accomplishments ..................................................................................................................................3
Research Products .................................................................................................................................................8
Publications ......................................................................................................................................................8
Presentations – Conferences .............................................................................................................................8
Education and Outreach Products .........................................................................................................................8
1. Modeling Terrorist Beliefs and Motivations Overview
The objective of this research is to further develop our pioneering approach to adversary threat
assessment through the construction of random utility models of terrorist preferences. This work builds
on previous research efforts (Rosoff, 2009; Rosoff & John, 2009) that have used decision analysis models
and elicitation methods to: (1) construct of a value tree for a terrorist leader or organization using value
focused thinking (VFT), (2) construct a random multi-attribute utility model (RMAUM) capturing tradeoffs among conflicting objectives and single-attribute utility functions representing risk attitudes of
terrorist leaders, (3) construct probability distributions capturing key uncertainties for terrorist leaders
(e.g., attack success) and uncertainties in the utility function parameters provided by adversary experts.
Understanding the objectives and motivations that drive terrorist group behavior is critical. Current
methods for terrorism risk assessment focus on target vulnerability, terrorist capability and resources, and
attack consequence. What many researchers have yet to consider is the influence of terrorist group values
and beliefs in deciphering the root cause of their militant behavior. This understanding has the potential
to contribute to probabilistic estimates of terrorist threats.
During Year 7 we further developed the adversary preference modeling (APM) methodology by (1)
assessing how anti-terrorism resource allocations impact terrorist preferences for different attack
strategies and estimated relative likelihoods of various attack strategies, and (3) continuing to validate the
APM through a case study with political/social/advocacy groups with an action oriented agenda that is
driven by specific motives, values, and objectives.
To address the first question, we used the APM methodology to model evolving decision making of the
adversary and to capture adversary attack strategy preferences. Using this approach, we assume that the
attacker’s probability distributions over attack strategies (modes, targets, and timing) are conditional on
the countermeasures selected by the defender. Four anti-terrorist resources were considered – (1)
installation of city wide closed circuit television cameras (CCTV), (2) enhanced police presence near
targets of concern, (3) enhanced border security, including both technological and human barriers, and (4)
widespread installation and monitoring of detectors (biological, chemical, radiological and explosive) in
urban areas of concern. Each anti-terrorist resource was evaluated in terms of its impact upon proxy
terrorist’s attribute definitions, probability estimates and ultimately, estimates for the likelihood of
various attack strategies. To address the second question, we opted to test the validity of our APM
methodology through the development of a model for a domestic political action group – Sea Shepherd
Conservation Society (Sea Shepherd). Research progress and accomplishments made throughout Year 7
are described in the subsequent section.
2. Research Accomplishments
Adversary Modeling Using the APM Methodology
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With respect to our adaptive adversary model using the APM methodology, we demonstrate that
preferences and relative likelihoods are altered depending upon the anti-terrorism resource under
consideration and the proxy terrorist’s perceived risk posed by that resource.
For example, Figure 1 is a series of boxplots comparing attacker utility distributions for No
Countermeasure versus each of the countermeasures. The analysis shows that none of the
countermeasures impact the proxy terrorist leader’s attack preferences. Under all conditions, the attack
type with the highest expected utility is ‘no attack’. In fact, we also found that the countermeasures have
a slight effect of increasing the likelihood of ‘no attack’.
Adversary Utilitiy distributions, contingent on NO Countermeasures
0.9
0.8
0.7
0.6
Utility
0.5
0.4
0.3
0.2
0.1
0.0
95%
75%
Mean
25%
5%
Adversary Utility Distributions Contingent on CCTV Countermeasure
Adversary Utility Distributions Contingent on Police Countermeasure
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
Utility
Utility
0.9
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
95%
95%
75%
75%
Mean
Mean
25%
25%
5%
5%
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Adversary Utility Distributions Contingent on Border Security Countermeasures
Adversary Utility Distributions Contingent on Detector Countermeasures
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
Utility
Utility
0.9
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
95%
95%
75%
75%
Mean
Mean
25%
25%
5%
5%
Figure 1: Proxy Expected Utilities for No Countermeasure vs Countermeasures
Figure 2 illustrates the proxy’s likelihood estimates for each attack type conditional on no additional antiterror countermeasures. This figure shows that given the probability of the most preferred attack, the ‘no
attack’ option, dominates all other alternatives (.985) it is not surprising that countermeasure effects were
not found following countermeasure implementation. Figure 3 illustrates the proxy’s likelihood estimates
for each attack type assuming that one of the attack options will be selected by a terrorist leader (that is,
we excluded the ‘no attack’ alternative). Results indicate that the most preferred attack alternative was
the IED attack on gas stations in U.S. cities.
1
0.985
Probability of Attack
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.013
0.001
0.001
0.001
0
0
No Attack
IED attacks on gas Anthrax release in Dirty bomb attack Sarin gas attack on Pneumonic plague
stations in US
large US oil
on a major US
a subway system release in US train
cities
refineries
seaport
in a large US city
stations
Figure 2: Proxy Relative Likelihood of Attack for No Countermeasure and Conditional on No
Additional Countermeasures
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1
Probability of Attack
0.9
0.811
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.082
0.007
0
0
IED attacks on gas
stations in US cities
Anthrax release in Dirty bomb attack on Sarin gas attack on a
large US oil refineries a major US seaport
subway system in a
large US city
Pneumonic plague
release in US train
stations
Figure 3: Proxy Relative Likelihood of Attack with ‘No Attack’ Eliminated and Conditional on No
Additional Countermeasures
Interestingly, while countermeasure resources alter attribute scales and uncertainties and attack
probability estimates, attack strategies preferences could remain unaffected depending on the values
assigned to attribute weights and attribute utility functions capturing attitudes toward risk for each
alternative. Through the exploration of different anti-terrorism resources’ impact on proxy terrorist
preferences, insight is gained as to the extent to which government actions might influence terrorist
decision making.
APM Model Validation
During Year 7 we began building a model based on what could be identified about the Sea Shepherd
online and from second-hand sources. We also began interviewing Sea Shepherd stakeholders to evaluate
the accuracy and validity of the originally constructed second-hand model, and hence determining
whether the APM modeling approach could be validated.
With respect to the construction of the second-hand model, basic research (online resources and literature)
led to the development of a first set of objectives. Sea Shepherd’s goal is to “to end the destruction of
habitat and slaughter of wildlife in the world's oceans in order to conserve and protect ecosystems and
species.”1 Given this fundamental objective, five strategic objectives were identified: (1) Shape New
Policy, (2) Enforce Environmental Law, (3) Increase Organizational Capacity, (4) Minimize Operation
Costs and (5) Shape Public Opinion. In addition, Sea Shepherd campaign alternatives, scales and metrics
for the attributes, estimates of attitudes toward risk, and trade-offs among various objectives were
identified (more details available upon request). Figure 4 is a graphic depiction of a preliminary
objectives hierarchy for the Sea Shepherd.
1
http://www.seashepherd.org/who-we-are/
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Figure 4. Sea Shepherd Objectives Hierarchy
Ultimately, from this working model proxy utility distributions have been derived and are presented in
Figure 5. The range for attack options is from 0, being the worst possible attack, to 1.0, being the best
possible attack. Saving the Whales in the Antarctic Ocean clearly has the highest expected utility - .38.
The remainder of the attack alternatives are still attractive, yet less so, and cluster in the range of having
utilities from .16 to .20. This is true for all the alternatives except for Defending Sharks/Turtles in
Galapagos (utility = .11).
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Expected Utility
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0.38
0.20
0.19
0.18
0.16
0.16
0.11
Figure 5. Proxy Expected Utility for Sea Shepherd
The information included herein is a preliminary report of progress to date. All material is still under
development for the second hand model. In addition, interviews have been conducted and/or scheduled
for the second phase of the project (validation component) in Year 8.
3. Research Products
3.1. Publications
John, R. and Rosoff, H. (April 2011). Modeling Effects of Counterterrorism Initiatives for Reducing
Adversary Threats to Transportation Systems, Journal of Homeland Security, Retrieved from
http://www.homelandsecurity.org/journal/Default.aspx?t=355&AspxAutoDetectCookieSupport=1
3.2. Presentations – Conferences
John, R. S. & Rosoff, H. (June 2011). Value focused modeling for an adaptive adversary. Research paper
presented at the 4th Annual Workshop on Human Behavior and Security (SHB-2011), Pittsburgh, Pa.,
CMU, June 17-18, 2011.
John, R. and Rosoff, H. (April 2011). Modeling Effects of Counterterrorism Initiatives on Reducing
Adversary Threats to Transportation Systems. Invited paper presented at the DHS Fifth Annual
University Network Summit. Session Chair: Heather Rosoff.
John, R. and Rosoff, H. (December 2010). Modeling the Dynamic Bidirectional Influence of Attack
Threat and Countermeasure Decisionmaking. Invited paper presented at the Society for Risk Analysis
Annual Meeting, Salt Lake City, Utah. Session Chair: Heather Rosoff.
4. Education and Outreach Products
Education and Outreach Initiatives (Please detail below)
# of Postdoctoral Researchers
# of students supported (funded by CREATE)
#
1
2
Funded by CREATE
Heather Rosoff, Postdoctoral Researcher. University of Southern California (USC) School of Policy,
Planning and Development.
Gin Chen, Doctoral Student in Psychology. USC’s Department of Psychology.
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Jason Jacobsen, Master of Public Administration Candidate. USC’s School of Policy, Planning and
Development.
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