Barry C. Ezell

Probabilistic Risk Analysis and Terrorism Risk

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.

Using plural modeling for predicting decisions made by adaptive adversaries

Incorporating an appropriate representation of the likelihood of terrorist decision outcomes into risk assessments associated with weapons of mass destruction attacks has been a significant problem for countries around the world. Developing these likelihoods gets at the heart of the most difficult predictive problems: human decision making, adaptive adversaries, and adversaries about which very little is known. A plural modeling approach is proposed that incorporates estimates of all critical uncertainties: who is the adversary and what skills and resources are available to him, what information is known to the adversary and what perceptions of the important facts are held by this group or individual, what does the adversary know about the countermeasure actions taken by the government in question, what are the adversarys objectives and the priorities of those objectives, what would trigger the adversary to start an attack and what kind of success does the adversary desire, how realistic is the adversary in estimating the success of an attack, how does the adversary make a decision and what type of model best predicts this decision-making process. A computational framework is defined to aggregate the predictions from a suite of models, based on this broad array of uncertainties. A validation approach is described that deals with a significant scarcity of data.

Cyber risk to transportation, industrial control systems, and traffic signal controllers

This paper is a result of a cyber risk assessment with a goal of increasing awareness to operators of infrastructure, managers, and political leadership. Senior executives and political leaders have a very limited understanding of industrial control systems (ICS) and of the crucial role ICS provide to public/private infrastructure, industry, and military systems. Therefore, to accomplish our purpose, we conducted a cyber-risk study focusing on a bridge tunnel ICS and a cyber event that tampered with traffic light operation—two scenarios of concern for senior leaders. In this paper, we present the analytic approach, discuss our model and simulation, and analyze the results using a notational data and generic system description. As a result of this study, we were able to discuss the importance of controls systems with senior leaders. We were able to demystify what we mean by “cyber”, showing that it is possible through simulation to inject the effects of cyber scenarios of concern into simulations to assess impact. Most importantly, during a system audit, ICS operators with decades of engineering experience began to realize that the ICS is vulnerable to willful intrusion.

A Simulation-Based Optimization Approach to a Lost Sale Stochastic Inventory Model

This paper describes a stochastic inventory model where the control review system is periodic; demand contains auto-correlated components; and categorized as a lost sale case. The authors propose a simulation-based optimization based on using a combination of simulated annealing, pattern search, and ranking and selection methods to search and approximate solutions to this problem. Simulated annealing is employed to stochastically nominate and pre-select solutions in a decision space. Pattern search is used to systematically define a grid of competitive neighbors around pre-selected solutions. Ranking and selection is used to evaluate the performance of such competing pre-selected alternatives. On one hand, results show that service level in terms of filling rates deteriorate as the autocorrelation grows and is ignored. In contrast, service levels were kept almost invariable to the effects of the serially correlated components for solutions suggested using the proposed algorithm.

An improvement selection methodology for key performance indicators

Key performance indicators (KPIs) are critical measures for determining the health of a manufacturing plant in relationship to the plant’s goals. In today’s competitive environment, manufacturers cannot be careless about their business; in fact, they must ensure that their KPIs are effective and use them to make improvements when necessary. This paper describes a method for suggesting improvements to a manufacturer’s KPIs, based on the results achieved from a workshop to score the KPI on a number of predefined criteria. The approach uses a prospect theory approach to weight the scoring. Different problem formulations were derived that allow for both recommendations for improvements and the recommendations for disinvestments to over-performing KPIs. The authors applied the developed approach to two workshop outputs, each from independent manufacturers, and the results highlighted the significant difference between the two manufacturers in terms of improvement priorities and KPI assessment. The optimal improvement suggestions were compared to those found through a fast heuristic. It was determined that given the underlying assumptions of the approach that the heuristic solutions were just as adequate as the optimal ones.

Identifying Factors that Influence Terrorist Decisions and Target Selection

Currently, the U.S. Department of Homeland Security (DHS) elicits probabilistic judgments from the intelligence community on actions terrorists may take to attack the continental U.S. For example, how likely is the adversary to choose agent ‘x’ over agent ‘y’ or target ‘a’ over target ‘b’? Eliciting these types of judgments is difficult and time consuming. The National Academies and others have suggested that a better approach may be to elicit information on adversary’s preferences, perceptions, and capabilities and use this information to calculate probabilities of interest to DHS. Some terrorist groups are thinking about using weapons of mass destruction (WMD), each with its own values, perceptions of reality, and capabilities. This presentation details the findings on the factors & relationships among factors that lead to a terrorist decision to initiate an attack against the continental U.S as well as target selection. To accomplish this, we assembled international experts in WMD, adversary modeling, political science, terrorism, psychiatry, social sciences as well as experts from national laboratories, the Commonwealth of Virginia State Fusion Center, and Hampton Roads Emergency Management.This paper provides a summary of the findings from an Adaptive Adversary Workshop. In this paper, we provide an overview of the motivation for and design of the workshop as well as 19 emerging themes. The purpose of the workshop was to illicit expert opinions on terrorist decision-making and target selections in an effort to improve our understanding of adversaries (individuals, local/regional groups, transnational groups, states) who may initiate a bioterrorism attack in the form of releasing biological agents upon U.S. interests. Furthermore, these expert opinions are intended to be used to inform Bayesian Belief Network (BBN) models of terrorist networks. These models must be informed or populated with substantive information about the intelligent and adaptive adversary who may initiate an attack. To this end, a conceptual framework, informed broadly by the social sciences community, is intended to capture the terrorists’ motivations, methods, and decision calculi.

Using Analytical Network Process Decision Methodology to Analyze and Allocate Resources in the U.S. Army Training Support System

Deciding on an appropriate training solution mix at the strategic level of U.S. Army training support system enterprise to support warfighter preparation is a complex matter. One of the most important problems is integrating qualitative and quantitative multiple sources of influential information. There are many goals to accomplish while they are constantly changing. However, the best training solution mix option that both minimizes resource impact and maximizes training throughput must be selected. The objective of this paper is to introduce a decision-making methodology based on the Analytical Network Process (ANP) for the U.S. Army Training Support System (ATSS). The methodology assists in the evaluation of training alternatives to help strategic decision makers to select the best mix of training components and strategies. An application of the proposed methodological framework is performed in real world example. The problem involves deciding the right mix of training solutions for urban operation training among a group of selected options.

Base camp facility layout

The primary purpose of the project presented was to aid in the development of an optimal military base camp facility layout by studying and identifying proximity relationships between essential facility components. These relationships were determined through extensive research, surveys, and interviews with key stakeholders. Three separate relationship charts were created representing the mean, median, and mode response scores from research and an online survey of critical base camp stakeholders from around the world. These charts were then used in conjunction with score and rectilinear distance matrices to organize the data for software optimization. The Computerized Relative Allocation of Facilities Technique (CRAFT) was used in the solution for a near-optimal layout for three base camps with 15 facilities. These three layouts were then compared to each other as well as to standard base camps found in Theater Construction Management System (TCMS) software and Camp Bondsteel.

Comparison of Approaches for Adversary Modeling Decision Support for Counterterrorism

Intelligent adversaries are a fundamental component of terrorism risk analysis. Unlike natural and engineering hazards, intelligent adversaries adapt their behavior to the actions of the defender. They adapt to observed, perceived, and imputed likely future actions by those defending the system they are attempting to influence. Risk assessment models need to consider these potential adaptive behaviors to be able to provide accurate estimates of future risk from intelligent adversaries and appropriately support risk management decision making.

Analysis of the Cost of Emergency Managers' Meeting Load: A Hampton Road Case-Study

Abstract Preparation for a disaster is not something that can be done by a single organization thus there is a need for coordination between them. Meetings and joint exercises are one means of coordination used by the emergency management community. Meetings and exercises take time, including transportation of personnel and arrangements, and time is money. With limited budgets, emergency managers need to make hard decisions about how their time is allocated. This paper describes a cost model for meeting analysis and discusses a case study that looks at the holistic time spent on meetings and exercises, by personnel, for the Hampton Roads Region of Virginia. A novel way is used to display this expenditure, e.g., it is shown in terms of monetary cost instead of temporal cost. This analysis highlighted some unexpected results, i.e., the small number of personnel involved in multiple working group meetings and high level of travel costs between the HR and the state capital, Richmond. This cost model approach may provide emergency managers with better mechanisms to show their meetings costs to senior leadership.