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Society For Risk Analysis Annual Meeting 2011

Risk Analysis on the Coast

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

M4-H
Symposium: Adversary Modeling for Terrorism Risk Analysis Applications

Room: R 11   3:30-5:10 PM

Chair(s): Sara Klucking

Sponsored by SDSG/DARSG

Homeland Security Presidential Directives (HSPD) -10, - 18, and -22, recognize the need for systematic, science-based, terrorism risk assessments that inform strategic planning and resource prioritization. To address this need, the Department of Homeland Security (DHS) Science and Technology (S&T) Terrorism Risk Assessments (Bioterrorism Risk Assessment [BTRA]; Chemical Terrorism Risk Assessment [CTRA]; Integrated Chemical, Biological, Radiological and Nuclear Terrorism Risk Assessment [ITRA]) were developed. The TRAs are comprehensive, probabilistic risk assessments that integrate the expert judgments of the intelligence and law enforcement communities with those from the scientific, medical, and public health communities. S&T is committed to continual improvement of the TRAs in support of decision makers. This panel showcases some of S&T’s recent investments in modeling the dynamic nature of an intelligent, adaptive adversary for risk analysis applications. The value of these and other approaches as a supplement or alternative to elicited static probabilities are being evaluated. The range of showcased approaches represents improvements to traditional probabilistic risk assessment as well as application of novel and existing methodologies to the problem.



M4-H.1  15:30  Value Focused Modeling of Adaptive Adversaries for Informing Countermeasure Decisions. John RS*, Rosoff H; University of Southern California   richardj@usc.edu

Abstract: The US has implemented numerous anti-terror countermeasures in response to perceived threats over the past decade, and efforts are underway to develop others. Unlike natural or accidental man-made disasters, terrorists are adaptive, and may shift their attack strategy when a new countermeasure is employed. This adaptive nature of adversaries creates unique challenges for a defender who must select among competing portfolios of countermeasures under resource constraints. Current methods for terrorism risk assessment focus on target vulnerability, terrorist capability and resources, and attack consequence, ignoring the importance of terrorist group values and beliefs in selecting a particular attack strategy. Understanding the objectives and motivations that drive adversary behavior is critical to the task of assessing the effectiveness of countermeasures designed to deter or mitigate an attack from an adaptive adversary. Modeling adversary values and beliefs has the potential to inform probabilistic estimates of adaptive attack behavior, and aid in the design and selection of anti-terror countermeasures. Using a value-focused decision framework, we assess values and beliefs from an adversary value expert (AVE) for specified terrorist leaders. Adversary motivations and values are represented formally in an objectives hierarchy specific to the context of attacking a transportation system. We then use a random utility modeling approach to compare the risk profiles of alternative transportation attack strategies and estimate the relative likelihood of an adversary (terrorist leader) selecting a particular attack strategy, conditional on various countermeasures selected by the (US) defender. Since we cannot collect information directly from terrorists, individuals who have studied contemporary terrorism as well as Islamic terrorist groups (such as Al Qaeda) served as AVEs for particular adversary group leaders. Results from this demonstration analysis are presented, and potential insights from the proposed analysis are highlighted.

M4-H.2  15:50  Modeling and risk assessment of terrorist-counterterrorist interactions with Multi-Agent Influence Diagrams. Sentz K*, Powell D, Ambrosiano J, Graves T; LANL   ksentz@lanl.gov

Abstract: The unique sophistication of an intelligent adaptive agent in terrorist risk assessment requires a novel methodology to model adversarial decision-making in response to offensive, defensive, and mitigative measures. The Multi-Agent Influence Diagram (MAID) [Koller, Milch (2003)] furnishes a promising approach by synthesizing multi-agent modeling, game theory, and probabilistic decision networks. We augment the MAID with an architecture that incorporates agent beliefs, values, and goals into the model structure. The multi-agent scenario and their respective strategies are intuitively represented by a decision graph where the agents’ strategies and expected utilities can be evaluated from the perspective of a game in terms of the Nash equilibrium or quantal response equilibrium. In this presentation, we discuss the value of modeling the terrorist-counterterrorist problem using the MAID approach, the natural risk metrics that the methodology affords, and the future extensions of this work.

M4-H.3  16:10  Adaptive Adversary Risk Analysis: Linking Models to Primary Data on Terrorist Behavior. Jackson BA*, Frelinger DR, Hart J, Kavanagh J, Loidolt B, Wallace BA; RAND Corporation   briananthonyjackson@yahoo.com

Abstract: Addressing adversary adaptation in risk analysis requires understanding the ways they can respond to new defensive or other changes. They have a variety of options, each with distinct direct and indirect risk effects. We demonstrate how adversary preferences among those options can be assessed through illustrative analyses of open source descriptions of past group behavior, content analysis of jihadist internet communications, and declassified seized al-Qa’ida documents.

M4-H.4  00:00  Adaptive Adversary Agent-Based Modeling for CBRN Terrorism Risk Analysis. Austin T*, Sageman M, Luckey T, Cameron J; The Boeing Company   tom.austin@boeing.com

Abstract: An agent-based methodology framework has been developed to model the behavior, decision making, and asymmetric tactics, techniques and procedures of an intelligent, adaptive and reactive adversary planning, preparing to execute an attack using chemical, biological, radiological or nuclear weapons of mass destruction (WMD). WMD terrorist attack likelihoods and risk assessments will be modeled by adaptive learning computer software agents who operate in a virtual world and follow planned and contingency-based rule sets that adapt to the defender’s world. The model framework is built on the cornerstone of the Observe, Orient, Decide and Act Loop process. This methodology was developed for the Department of Homeland Security Science & Technology Directorate requirement to build new terrorism risk analysis applications that provide the estimation of attack likelihoods and attack modes of potential terrorist WMD attacks against the U.S.

M4-H.5  16:50  Plural Models for Adaptive Adversary Modeling. Buede DM*, Ezell BC, Guikema SD, Lathrop JF, Mahoney SM, McLay LA, Post JM, Rothschild C; Innovative Decisions, Inc.   bezell@odu.edu

Abstract: This presentation describes work performed by Innovative Decisions, Inc. (IDI) on modeling adaptive adversaries for Terrorism Risk Assessments (TRAs) for the U.S. Department of Homeland Security (DHS). Terrorists are not homogeneous but differ widely in terms of motivations; decision making information, skills, and processes; and organizational or personal psychology. In addition, there will likely be some interaction between what the terrorist (Red) does and what the United States or home government (Blue) does. We are focus on strategic risk analyses of one to three years in the future. Our approach uses multiple modeling methods, plural modeling. These modeling methods will consider motivations or objectives of the adaptive adversaries, will address multiple decision making styles, and will be conditioned on Red’s perceptions of Red’s capabilities as well as Red’s perceptions of the defensive actions that Blue may take. This approach is founded on the principle that has been learned many times in the military/intelligence communities: that Blue should not assume that Red will do what Blue would do in a given situation, often called “mirroring”.



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