Modeling Terrorist Beliefs and Motivations

Transcription

1 CREATE Research Archive Research Project Summaries 2012 Modeling Terrorist Beliefs and Motivations Heather Rosoff University of Southern California, Richard John University of Southern California, Follow this and additional works at: Part of the Risk Analysis Commons Recommended Citation Rosoff, Heather and John, Richard, "Modeling Terrorist Beliefs and Motivations" (2012). Research Project Summaries. Paper This Article is brought to you for free and open access by CREATE Research Archive. It has been accepted for inclusion in Research Project Summaries by an authorized administrator of CREATE Research Archive. For more information, please contact

2 National Center for Risk and Economic Analysis of Terrorism Events University of Southern California Los Angeles, California Modeling Terrorist Beliefs and Motivations October 2011 to August 2012 Heather Rosoff and Richard John University of Southern California "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 No 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 ST-061-RE0001 Department of Homeland Security

3 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 This annual report covers the seventh year of CREATE funding from October 2011 to August 2012, 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 for more information. Page 2 of 9

4 1. Modeling Terrorist Beliefs and Motivations Overview Research Accomplishments Research Products Presentations Conferences Education and Outreach Products 7 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 trade-offs 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 8 we further developed the adversary preference modeling (APM) methodology by (1) continuing to validate the APM through a case study with political/social/advocacy groups the Sea Shepherd Conservation Society (Sea Shepherd) with an action oriented agenda that is driven by specific motives, values, and objectives, and (2) beginning the development and use of a System Dynamics Simulation Model to further examine the structure of the terrorist groups organizational system, the interactions among its components, and how change in one area, for instance in the form of a defender countermeasure, affects the whole system and its parts over time. Research progress and accomplishments made throughout Year 8 are described in the subsequent section. 2. Research Accomplishments APM Model Validation During Year 8 we built an MAU model based on what could be identified about the Sea Shepherd online and from second-hand sources. We also interviewed 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 proxy 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 Page 3 of 9

5 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 1 is a graphic depiction of a preliminary objectives hierarchy for the Sea Shepherd. Figure 1. Sea Shepherd Objectives Hierarchy Ultimately, from this working model proxy utility distributions have been derived and are presented in Figure 2. 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 The remainder of the attack alternatives are still attractive, yet less 1 Page 4 of 9

6 Expected Utility 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) Figure 2. Proxy Expected Utility for Sea Shepherd The aforementioned findings were generated from a model developed and populated by Proxy 1, the developer of the APM model. Upon completion, the research team sought to validate the model and methodology using expert elicitation to 1) validate the framework, and 2) populate the model with values based on personal experiences with Sea Shepherd. The two models could then be compared to assess to what extent a model constructed by researchers using open-source materials would be compatible with one developed by a subject matter expert (Proxy2). Proxy2 s subject matter expertise was based on time spent with Sea Shepherd s founder and Executive Director, Paul Watson on one of Sea Shepherd s campaigns to defend whales from Japanese whalers. The elicitation sessions conducted with Proxy 2 took place over the phone during two sessions, each approximating 1 hour and 15 minutes. During these sessions, Proxy 2 was guided through the process of evaluating and revising, as desired, the model (objectives and attributes) constructed by Proxy 1. He then was taken through the process of populating the model with attribute values, risk attitudes and swing weights. When comparing model outputs for Proxy1 and Proxy2 (Figure 3), Whale Wars was found to provide the greatest estimated utility, 0.39 & 0.70 respectively. Proxy1 estimated that the Galapagos Islands alternative provided the least utility for Sea Shepherd (utility = 0.11), while Proxy2 found that the Galapagos alternative provided the third highest utility (utility = 0.49) among the alternatives. Proxy2 had more variability among the remaining five options than did Proxy1 who perceived the remaining five alternatives to be very similar in their expected utilities. Page 5 of 9

7 Figure 3. Campaign Alternatives: Expected Utilities by Proxy Probability distributions for each alternative were calculated using the SoftMax method with the temperature set to Campaigns defending cape fur seals, pilot whales, and harp seals received a zero probability when the SoftMax formula was applied to Proxy2 s value model. The results of both models are displayed in Figure 4 (with the Whale Wars alternative removed). Figure 4. Proxy Probability Distributions The information included herein is a preliminary report of progress to date. All material is still under development. Page 6 of 9

8 A System Dynamics Modeling Approach to the Operations of Terrorist Organization For this project, we developed a system dynamics model designed to capture the operations of a terrorist organization. More specifically, through this research we plan to (1) assess what outcome trajectories terrorist organizations desire, (2) how terrorist organizations objectives and values change over time and (3) how counter-terrorism efforts affect outcome trajectories and the evolution of terrorist organizations. From this research, a better understanding of terrorist organizations and their evolving motivations and values over time will be formulated. This knowledge is a critical component to decisions about allocating resources to minimize the impact of these organizations. We had 3 primary research questions: How can we effectively portray the impact of counter-terrorism measures to the resource network of a terrorist group over time? How will different counter-terrorism measures impact different aspects of a terrorist organization over time? How can we better allocate defense resources to more effectively dismantle or disengage a terrorist group from multiple perspectives? Model development included the creation of a causal loop diagram designed to capture the dynamic nature (through stocks and flows0 of a terrorist organization. Figure 5 is a snapshot of the working model. The complete model consists of several sub-systems, including members and sympathizers, funding, weapons of mass destruction capabilities, and attacks and casualties. Figure 5. Snapshot of Working SD Model of Terrorist Organization The example system shown in Figure 6 is the Members and Sympathizers Sub-System. There are two stocks, active members and sympathizers, as well as the corresponding inflows and outflows Page 7 of 9

9 for these stocks. Variables that are not caused by anything in the system, and thus can be treated as inputs, are exogenous and are labeled in brown. Variables labeled in red represent counterterrorism measures that impact the cause and effect scheme. The recruitment loop feeds on the logic that the more members there are, the more likely they can recruit more members. The blowback loop captures how as the number of people radicalizing an ideology to fit the goals of the group and the potential members, the more people will look into the group to satisfy a need or desire. These people are labeled potential members. The people who decide to join the cause become indicated members. As the number of indicated members increase, the popular support of the terrorist group will increase. Popular support is also impact by the fraction of the population of interest, the number of sympathizers, and the negative effect of counter-terrorism military units. Once people are inspired to follow the group, it increases the membership (density loop). The Density loop takes into account that as the number of active members operating in the open increases, the likelihood of them getting caught increases as well. The current number of active members impacts the current number in the open. Impact of a Traumatizing Event Multiplier Counter Terrorism Education to locals Effect of Member Density Number of people radicalizing an ideology Recruitment Loop <Opposition Fraction> Active members in the Open Density Loop Detention and Elimination of Terrorist Group Members Potential Members Fraction who become members Indicated Members per Month Blowback Loop Members Opposing the Group Increase in Members People inspired to follow the terrorist group Active members of a Terrorist Group Fraction Inspired to Follow Detention and Elimination Rate Combat Efficacy Fraction Potentially Sympathetic to Terrorist Group Rate of Sympathizers Becoming Members Fraction of Popular Support Population of Interest Popular Support of the Terrorist Group Negative Effect of CT Military Units Increase in Sympathizers Sympathizers of the Terrorist Group Rate of Sympathizers Opposing Group Opposition Fraction Figure 6. Members and Sympathizers Sub-System The goal for the end of year 9 is to complete model development, and have a report submitted for publication in a scholarly journal. 3. Research Products 3.1. Presentations Conferences John, R. S., Rosoff, H., Barrett, A., & Bier, V. Modeling effects of counterterrorism initiatives on reducing adversary threats. Decision Analysis Society sponsored presentation at the annual meeting for the Institute for Operations Research and Management Science (INFORMS), Charlotte, N.C., November 13-16, Page 8 of 9

10 John, R. S. & Rosoff, H. Exploiting biases and heuristics in adversary judgment and decision making. Decision Analysis Society sponsored presentation at the annual meeting for the Institute for Operations Research and Management Science (INFORMS), Charlotte, N.C., November 13-16, John, R. S., Rosoff, H., Barrett, A., & Bier, V. Value focused modeling of adaptive adversaries for informing countermeasure decisions. Research paper presented at the Society for Risk Analysis (SRA) annual meeting, Charleston, S.C., December 4-7, Education and Outreach Products Education and Outreach Initiatives (Please detail below) # # of Associate Professor of Psychology 1 # of Research Professors 1 # of students supported (funded by CREATE) 1 # of summer students 1 Funded by CREATE Richard John, Associate Professor of Psychology, USC School of Psychology Heather Rosoff, Research Professor, CREATE and Sol Price School of Public Policy Jason Jacobsen, Master of Public Administration Program, Sol Price School of Public Policy Summer Student Jason Chin, Columbia University Page 9 of 9