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Improving Homeland Security Decisions

Book Description

What are the risks of terrorism and what are their consequences and economic impacts? Are we safer from terrorism today than before 9/11? Does the government spend our homeland security funds well? These questions motivated a twelve-year research program of the National Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California, funded by the Department of Homeland Security. This book showcases some of the most important results of this research and offers key insights on how to address the most important security problems of our time. Written for homeland security researchers and practitioners, this book covers a wide range of methodologies and real-world examples of how to reduce terrorism risks, increase the efficient use of homeland security resources, and thereby make better decisions overall.

Table of Contents

  1. Cover
  2. Half title
  3. Title page
  4. Imprints page
  5. Contents
  6. Contributors
  7. 1 Improving Homeland Security Decisions
    1. 1.1 Introduction
  8. 2 Probabilistic Risk Analysis and Terrorism Risk
    1. 2.1 Introduction
      1. 2.1.1 2006 Bioterrorism Risk Assessment Background
      2. 2.1.2 Intelligent Adversary Analysis
    2. 2.2 Probabilities Are Useful to Quantify the Risk of Terrorist Attacks
    3. 2.3 Tools for Terrorism Risk Analysis
      1. 2.3.1 Logic Trees
        1. 2.3.1.1 Probability, Event, and Decision Trees
        2. 2.3.1.2 Fault, Attack, and Success Tree
      2. 2.3.2 Influence Diagrams
      3. 2.3.3 Causal Loop Diagrams and Systems Dynamic Models
      4. 2.3.4 Bayesian Network Analysis
      5. 2.3.5 Game Theoretic Models
    4. 2.4 Conclusion
    5. Acknowledgments
    6. Notes
    7. References
  9. 3 Integrating Stakeholder Values into Strategic Planning through Comparative Risk Analysis
    1. 3.1 Overview of Five Steps on Process
    2. 3.2 Categorizing the Risks
    3. 3.3 Selecting Attributes
    4. 3.4 Presenting Risks
    5. 3.5 Unique Aspects of Assessing Comparative Risk in the Homeland Security Domain
    6. 3.6 Conducting Risk Rankings
      1. 3.6.1 Recruiting Participants
      2. 3.6.2 Carrying Out Sessions
    7. 3.7 Interpreting Results
    8. 3.8 Extensions
    9. References
  10. 4 Validating Terrorism Risk Assessment Models – Lessons Learned from 11 Models
    1. 4.1 Introduction
    2. 4.2 Introducing a TRAM Validation Conceptual Framework
    3. 4.3 Perspective I: Capturing the Risk-Generating Process
    4. 4.4 Perspective II: Model Quality
    5. 4.5 Perspective III: Engaging With the Risk Management Decision-Making Process
      1. Example 1: Putting uncertainty into terms relevant to the risk management decision-making process (DMP)
      2. Example 2: Considering how the DMP will address risk aspects left out of a TRAM
    6. 4.6 Perspective IV: Engaging with the Terrorist-Defender Game
    7. 4.7 An Example Application of the 13 Tests
    8. 4.8 Combining Perspectives III and IV
    9. 4.9 Summary of Main Points
    10. 4.10 Paths Forward
    11. References
  11. 5 Coping with Uncertainty in Adversarial Risk Analysis
    1. 5.1 What Types of Uncertainties Can Be Important in Terrorism Risk Analysis?
      1. 5.1.1 Terrorists’ Uncertainty about the Consequences of Various Possible Acts
      2. 5.1.2 Defender Uncertainty about Terrorists
      3. 5.1.3 Uncertainty about One’s Own Values (Present and Future)
    2. 5.2 Strategies for Dealing with Statistical Uncertainty and “Deep Uncertainty”
      1. 5.2.1 Are We Being Attacked Yet? Computational Methods for Detecting Changes and Quantifying Uncertainty
      2. 5.2.2 Predicting and Quantifying Uncertainties about Adversarial Risks Using BN, ID, or Nested Decision-Tree Models
      3. 5.2.3 Predicting Adversarial Risks with Unknown Probabilities and Scarce Data
        1. Imprecise Probabilities, Second-Order Probabilities, and Bounding Approaches
        2. Robust Inference and Optimization
        3. Adaptive Learning, Bayesian Model Averaging (BMA), Model Ensembles, and “Plural Analysis”
    3. 5.3 How Has Uncertainty Analysis Been Used in Adversarial Risk Analysis?
    4. 5.4 Making Prudent Decisions Despite Realistic Uncertainties
    5. References
  12. 6 A Risk and Economic Analysis of Dirty Bomb Attacks on the Ports of Los Angeles and Long Beach
    1. 6.1 Introduction
      1. 6.1.1 The Dirty Bomb Threat
    2. 6.2 Sources of Radioactive Material
      1. 6.2.1 Nuclear Reactor and Waste Facilities
      2. 6.2.2 Medical, Research, and Industrial Facilities
      3. 6.2.3 Foreign Sources of Radioactive Material
    3. 6.3 Scenarios and Probabilities
      1. 6.3.1 Type of Bomb Constructed
      2. 6.3.2 Delivery Modes
      3. 6.3.3 Detonation Site
      4. 6.3.4 Pruning Scenarios and Assessing Relative Likelihoods
      5. 6.3.5 Probabilities of Success
    4. 6.4 Consequences
      1. 6.4.1 Blast Effects and Acute Radiation
      2. 6.4.2 Health Effects Due to Airborne Releases
      3. 6.4.3 Economic Consequences
    5. 6.5 Countermeasures
    6. 6.6 Conclusions
    7. Acknowledgments
    8. References
  13. 7 Regional Transportation and Supply Chain Modeling for Large-Scale Emergencies
    1. 7.1 Introduction
    2. 7.2 Inventory Management Problem
      1. 7.2.1 Inventory Model
      2. 7.2.2 Example
    3. 7.3 Storage Problem
      1. 7.3.1 Facility Location Model
      2. 7.3.2 Illustrative Example
    4. 7.4 Distribution Problem
      1. 7.4.1 Vehicle Routing Model
      2. 7.4.2 Example
    5. 7.5 Conclusion
    6. Acknowledgment
    7. References
  14. 8 Economic Consequences of Terrorism and Natural Disasters: The Computable General Equilibrium Approach
    1. 8.1 Introduction
    2. 8.2 An Introduction to Basic Design of a CGE Model
      1. 8.2.1 The Starting Point: The Input-Output Database
      2. 8.2.2 Moving Off the Starting Point
      3. 8.2.3 How Does CGE Modeling Relate to Regression-Based Econometric?
    3. 8.3 Extensions of CGE to Incorporate Resilience and Behavioral Linkages
      1. 8.3.1 Resilience
      2. 8.3.2 Behavioral Linkages
    4. 8.4 USAGE and USAGE-TERM
      1. 8.4.1 USAGE
      2. 8.4.2 USAGE-TERM
    5. 8.5 An Illustrative Application of CGE Modeling: The Economic Effects of a One-Kiloton Nuclear Detonation in Downtown Los Angeles
      1. 8.5.1 Setting up USAGE-TERM and Specifying the Shocks
        1. 1. Impact Area, Immediate Deaths, Affected Surviving Population
        2. 2. Shutdown Period
        3. 3. Evacuations and Labor Supply
        4. 4. Later Deaths, Morbidity, and Labor Supply
        5. 5. Medical Expenditures
        6. 6. Radiation Clean-Up and Decontamination Cost
        7. 7. Treatment of Capital
        8. 8. Aversion Behavior and the Return of the Evacuated Population
        9. 9. Financing: The Public-Sector Deficit and Foreign Debt
      2. 8.5.2 Results
    6. 8.6 Concluding Remarks
    7. Notes
    8. References
  15. 9 Economic Resilience to Terrorism and Natural Hazards
    1. 9.1 Introduction
    2. 9.2 Defining Resilience across Disciplines
      1. 9.2.1 Ecological Origins
      2. 9.2.2 Individual Resilience
      3. 9.2.3 Community Resilience
      4. 9.2.4 Engineering-Based Definitions
      5. 9.2.5 Organizational Behavior
      6. 9.2.6 Planning
    3. 9.3 Defining Economic Resilience
      1. 9.3.1 Basic Attributes
      2. 9.3.2 Time-Path of Resilience
    4. 9.4 An Operational Metric
    5. 9.5 Measurement of Economic Resilience
    6. 9.6 Resilience Indices
    7. 9.7 Conclusion
    8. References
  16. 10 The Temporal Regional Economic Impacts of a Hurricane Disaster on Oil Refinery Operations: A FlexNIEMO Approach
    1. 10.1 Introduction
    2. 10.2 IO Operation and NIEMO
    3. 10.3 Hurricanes in the Gulf of Mexico
    4. 10.4 The Model
    5. 10.5 FlexNIEMO Results
    6. 10.6 Conclusions
    7. References
  17. 11 Risk-Informed Benefit-Cost Analysis
    1. 11.1 Introduction
    2. 11.2 Risk Principles and Enterprise Risk Management in the U.S. Government
    3. 11.3 The Department of Homeland Security Context
    4. 11.4 Risk Management Models and Metrics
    5. 11.5 Meta Model Choice
    6. 11.6 Benefit-Cost Analysis and the Implementation of Risk Concepts
    7. 11.7 Risk and BCA
    8. 11.8 Meta-choice and Specification within Benefit-Cost Analysis
    9. 11.9 Metrics
      1. 11.9.1 Decision Criteria Metrics
      2. 11.9.2 Preferences and Behavioral Response Metrics
    10. 11.10 Variability and Randomness
    11. 11.11 Conclusion
    12. Notes
    13. References
  18. 12 Enhancing Post-disaster Economic Resilience: Public-Private Partnership for Insuring Terrorism
    1. 12.1 Introduction
    2. 12.2 Challenges in Insuring Terrorism
      1. 12.2.1 Dynamic Uncertainty and Time Scale
      2. 12.2.2 Potential for Catastrophic Losses
      3. 12.2.3 Interdependencies
    3. 12.3 Decision Processes of Insurers Regarding Terrorism Insurance
      1. 12.3.1 Impact of Availability Bias on Insurer Behavior
    4. 12.3.2 Role of Ambiguity on Insurer Behavior
    5. 12.4 The Terrorism Risk Insurance Act (TRIA)
      1. 12.4.1 Structure of the New TRIA Partnership
      2. 12.4.2 Estimating Losses from Terrorist Attack Scenarios
    6. 12.5 Conclusion
    7. Notes
    8. References
  19. 13 Economic Impacts of Changes in Wait Times at U.S. Ports of Entry
    1. 13.1 Introduction
    2. 13.2 Methodological Overview
      1. 13.2.1 Microeconomic Analysis
      2. 13.2.2 Macroeconomic Analysis
    3. 13.3 Data
    4. 13.4 Overview of Results
    5. 13.5 Microeconomic Analysis of Changes in Wait Times
      1. 13.5.1 Passenger Vehicle Lanes at Land Border Crossings: Impact on Wait Time of Adding an Officer
      2. 13.5.2 Passenger Vehicle Lanes at Land Border Crossings: Value of Time Saved for Existing Traffic
        1. Passenger Vehicle Lanes at Land Border Crossings: New Cross-Border Trips Induced by Lower Wait Time
        2. Commercial Vehicles at Land Border Crossings: Change in Wait Time and Cross-Border Trips
        3. International Air Arrivals at Airports: Change in Wait Time and Cross-Border Trips
        4. International Air Arrivals at Airports: Value of Time Saved for Existing Traffic
        5. International Air Arrivals at Airports: New International Air Trips Resulting from Change in Wait Time
    6. 13.6 Impacts of Reduced Wait Times on Truck Transportation Costs
      1. 13.6.1 Volumes of Truck Traffic at the Border
      2. 13.6.2 Wait Times for Trucks at the Border
      3. 13.6.3 Truck Travel Distances
      4. 13.6.4 Truck Operating Costs
      5. 13.6.5 Changes in Truck Transportation Costs
      6. 13.6.6 Truck Opportunity Cost of Time Savings
    7. 13.7 National Competitiveness and Macroeconomic Impacts of Changes in Freight Transportation Costs
      1. 13.7.1 Trade and Transport in GTAP
      2. 13.7.2 Results and Comparisons
    8. 13.8 Regional and National Macroeconomic Impacts of Changes in Tourism and Business Travel
      1. 13.8.1 Input-Output Analysis
      2. 13.8.2 Further CGE Analysis
      3. 13.8.3 Methodology and Data Sources for Estimating Changes in Travel Expenditures
        1. Expenditure Changes of Land Travelers
        2. Expenditure Changes of Air Travelers
      4. 13.8.4 Simulation Results
        1. Impact Results for Wait Time Changes at Passenger Land POEs
        2. Impact Results for Wait Time Changes at Airline Passenger POEs
    9. 13.9 Study Limitations
    10. 13.10 Conclusions and Possibilities for Future Research
    11. Notes
    12. References
  20. 14 Organizational Decision Processes
    1. 14.1 Introduction
    2. 14.2 Decision Quality and Organizational Decision Quality
      1. 14.2.1 Decisions versus Outcomes
      2. 14.2.2 Decision Quality
      3. 14.2.3 The Decision-Driven Organization
      4. 14.2.4 Organizational Decision Quality
    3. 14.3 Enabling Organizational Decision Quality
      1. 14.3.1 Appropriate Decision Processes
      2. 14.3.2 Education and Training
      3. 14.3.3 Alignment with Organizational Processes
      4. 14.3.4 Learning and Continuous Improvement
    4. 14.4 Raiffa-Howard Award for Organizational Decision Quality
    5. 14.5 Commonwealth of Virginia Decision Process for Homeland Security Grant Allocation, 2009–Present
    6. 14.6 Conclusions
    7. Note
    8. Bibliography
  21. 15 A Value Model for Evaluating Homeland Security Decisions
    1. 15.1 Introduction
    2. 15.2 Construction of a Value Model
      1. 15.2.1 The Concept of a Value Model
      2. 15.2.2 Identifying Objectives
      3. 15.2.3 Specifying Metrics to Measure Objectives
      4. 15.2.4 Combining Achievement on Different Objectives
      5. 15.2.5 Value Trade-offs
    3. 15.3 Construction of a DHS Value Model
      1. 15.3.1 Identifying and Organizing DHS Objectives
      2. 15.3.2 Selecting Metrics to Measure DHS Objectives
      3. 15.3.3 Combining Impacts of Different DHS Objectives
      4. 15.3.4 Assessing Value Trade-offs
    4. 15.4 Uses of a Homeland Security Value Model
      1. 15.4.1 Evaluating Terrorism Risks
      2. 15.4.2 Evaluating the Benefits of Countermeasures
      3. 15.4.3 Developing a Severity Index for Terrorist Consequences
      4. 15.4.4 Improving the Quality of DHS Decision Processes
    5. 15.5 Conclusions
    6. References
  22. 16 Identifying, Structuring, and Comparing the Objectives of Al Qaeda and ISIL
    1. 16.1 It Is Important to Understand Terrorist Objectives
    2. 16.2 Identifying, Structuring, and Comparing Objectives
      1. 16.2.1 Identifying Objectives
      2. 16.2.2 Structuring Objectives
      3. 16.2.3 Comparing Objectives Hierarchies
    3. 16.3 Case Study: Comparing Objectives of Two Terrorist Groups
      1. 16.3.1 Determining the Objectives of Al Qaeda and ISIL
      2. 16.3.2 Comparing the Objectives of Al Qaeda and ISIL
        1. Objectives Related to Religion
        2. Objectives Related to Territory and Power
        3. Objectives Related to Establishing a Caliphate
        4. Objectives Related to Killing Enemies
    4. 16.4 Conclusion
      1. 16.4.1 Methodology
      2. 16.4.2 Main Differences and Similarities between Al Qaeda’s and ISIL’s Objectives
      3. 16.4.3 Different Counterterrorism Strategies
    5. References
  23. 17 Multi-objective Decision Making: Expected Utility vs. Some Widely Used (and Flawed) Methods
    1. 17.1 Introduction
    2. 17.2 The Majority-Vote Choice Criterion over the Individual Attributes
      1. 17.2.1 Numerical Example: The Majority-Vote Method over Attributes
      2. 17.2.2 Results of the Market Survey
        1. Group 1: The Family Sedan Folk
        2. Group 2: The Big City Folk
        3. Group 3: The Sports Folk
    3. 17.3 The Min-Max Regret Criterion
      1. 17.3.1 Numeric Example: Rank Reversal with Min-Max Regret
    4. 17.4 The “Weight and Rate” Choice Criterion
      1. 17.4.1 Example: A Typical (Erroneous) Application of Weight and Rate in Systems Engineering
    5. 17.5 Multiattribute Value and Multiattribute Utility Functions
      1. 17.5.1 The Rationale for Expected Utility Maximization
      2. 17.5.2 Expected Utility Formulations for Decisions with Multiple Attributes
      3. 17.5.3 Deterministic Multiattribute Decision Problems (Value Functions)
      4. 17.5.4 Constructing Multiattribute Utility Functions Using Value Functions
        1. Example: On Fates Comparable to Death
      5. 17.5.5 Basic Expansion Theorems for Multiattribute Utility Functions
        1. Basic Definitions
        2. Expansion of a Utility Function around a Single Attribute
        3. Expansion of a Utility Function around Multiple Attributes
        4. Simplifying the Assessments with Utility Independence Assertions
        5. When Utility Independence Conditions Do Not Hold
    6. 17.6 Conclusion
    7. References
  24. 18 Achieving Multiple Objectives with Limited Resources: Using Utility Theory and Control Theory
    1. 18.1 Introduction
    2. 18.2 Multiattribute Utility Copulas
    3. 18.3 Case Study: Maximizing the Utility of Multiple Objectives with UAS
      1. 18.3.1 Choosing the Attributes and Individual Goal Functions for the UAS Control Problem
        1. Objective Function for Surveillance
        2. Objective Function for Collision Avoidance
        3. Objective Function for Proximity
    4. 18.4 Control Strategies Based on the Archimedean Utility Copula
    5. 18.5 Experiments
    6. 18.6 Conclusion
    7. Bibliography
  25. 19 Defender-Attacker Decision Tree Analysis to Combat Terrorism
    1. 19.1 Introduction
    2. 19.2 Introduction to Defender-Attacker Decision Trees
    3. 19.3 The MANPADS Threat
    4. 19.4 Decision Tree
    5. 19.5 Analysis of the Decision Tree
    6. 19.6 Summary and Conclusion
    7. Acknowledgments
    8. Notes
    9. References
  26. 20 Decision Making for Bioterror Preparedness: Examples from Smallpox Vaccination Policy
    1. 20.1 Introduction
    2. 20.2 Vaccination Policy in the Large and in the Small(pox)
    3. 20.3 Natural Outbreak or Bioterror Attack? Ring versus Mass Vaccination
    4. 20.4 Build the Button Now: Vaccinating the Vaccinators
    5. 20.5 Why Wait? Pre- versus Post-attack Vaccination
    6. 20.6 Conclusions
    7. Acknowledgments
    8. References
  27. 21 Stackelberg Security Games (SSG) Basics and Application Overview
    1. 21.1 Introduction
    2. 21.2 Stackelberg Security Games (SSG) Basics
    3. 21.3 Categorizing Security Games
      1. 21.3.1 Infrastructure Security Games
      2. 21.3.2 Green Security Games
      3. 21.3.3 Opportunistic Crime Security Games
    4. 21.4 Deployed and Emerging Security Applications
      1. 21.4.1 Infrastructure Security Game Applications
        1. ARMOR for Los Angeles International Airport
        2. IRIS for U.S. Federal Air Marshals Service
        3. PROTECT for U.S. Coast Guard
        4. GUARDS for U.S. Transportation Security Agency
        5. TRUSTS for Urban Security in Transit Systems
        6. Protecting Public Events
      2. 21.4.2 Green Security Game Applications
      3. 21.4.3 Opportunistic Crime Security Game Applications
    5. 21.5 Applications of Security Games beyond Security
    6. 21.6 Major Research Issues
    7. Note
    8. References
  28. 22 Basic Solution Concepts and Algorithms for Stackelberg Security Games
    1. 22.1 Introduction
    2. 22.2 Basic Security Games
      1. 22.2.1 Stackelberg Security Games: Formal Definition
      2. 22.2.2 Solving Stackelberg Security Games
      3. 22.2.3 Compact Stackelberg Security Games
      4. 22.2.4 Solving Compact SSG
        1. Problem Analysis
        2. LTRA Algorithm
    3. 22.3 Security Games with Uncertainty
      1. 22.3.1 Bayesian Stackelberg Security Games
      2. 22.3.2 Interval Security Games
        1. ISG Model
        2. Analysis of ISG
        3. ISG Algorithm
    4. 22.4 Security Games with Scheduling Constraints
      1. 22.4.1 Generalized Model with Scheduling Constraints: SPARS
        1. ASPEN Column Generation
        2. Improving Branch and Bounds
      2. 22.4.2 Compact Representations with Scheduling Constraints
    5. 22.5 Conclusion
    6. Notes
    7. References
  29. 23 Mixed-Integer Optimization Methods for Solving Stackelberg Security Games
    1. 23.1 Introduction
    2. 23.2 Mixed-Integer Programming Formulations of SSG
      1. 23.2.1 Exploiting the Payoff Structure of Security Applications
      2. 23.2.2 Models
      3. 23.2.3 Computational Results
    3. 23.3 Methods for Large-Scale Linear Programming
      1. 23.3.1 Column generation
      2. 23.3.2 Cut Generation
    4. 23.4 Heuristic Methods to Solve Large SSGs
      1. 23.4.1 Fixed Patrols against Attackers on a Path
      2. 23.4.2 Patrolling Streets on a Graph
    5. 23.5 Conclusions
    6. References
  30. 24 Methods for Addressing the Unpredictable Real-World Element in Security
    1. 24.1 Introduction
    2. 24.2 Real-World Effects on the Standard Assumptions of Game Theory
      1. 24.2.1 Rationality Assumption
      2. 24.2.2 Information Assumption
      3. 24.2.3 Execution and Observation Assumption
    3. 24.3 Robust Approaches to Address Adversary Decision Making
      1. 24.3.1 ε-optimal Robust Response
      2. 24.3.2 Robustness in Stackelberg Security Games
      3. 24.3.3 Robustness to Execution and Observation Uncertainty
    4. 24.4 Behavioral Approaches to Address Adversarial Decision Making
      1. 24.4.1 Cognitive Biases
        1. Conservatism Bias
        2. Bayesian Updating
      2. 24.4.2 Modeling the Adversary Decision-Making Process
        1. Attacker’s Expected Value
        2. Attacker’s Expected Utility Accounting for Risk Attitude
        3. Lens Model
        4. Lens Model accounting for Risk Attitude (lens-α)
        5. Multiattribute Utility (MAU) Model
    5. 24.5 Addressing Multiple Decision Makers
      1. 24.5.1 Bayesian Stackelberg Games
      2. 24.5.2 Robust Bayesian Stackelberg Games
    6. 24.6 Simulations
      1. 24.6.1 General Population as an Approximation for Threats
      2. 24.6.2 Experimental Setup
      3. 24.6.3 Results
        1. Behavioral Modeling Evaluation
        2. Modeling versus Robust-Based Algorithms
    7. 24.7 Conclusions and Real-World Applications
      1. 24.7.1 PROTECT for U.S. Coast Guard
      2. 24.7.2 PAWS for Wildlife Security
      3. 24.7.3 Fish Protection for the U.S. Coast Guard
    8. References
  31. 25 Learning to Play Stackelberg Security Games
    1. 25.1 Single Unknown Attacker
      1. 25.1.1 Optimization with Membership Queries
      2. 25.1.2 Using Membership Queries to Learn SSGs
    2. 25.2 Single Attacker Drawn from a Distribution
      1. 25.2.1 Monte Carlo Tree Search
      2. 25.2.2 Applying MCTS to Repeated SSGs
    3. 25.3 Sequence of Attackers
      1. 25.3.1 Background on Regret Minimization
      2. 25.3.2 Applying Regret Minimization Techniques to Repeated SSGs
    4. 25.4 Further Reading
    5. Acknowledgments
    6. Note
    7. References
  32. 26 Evaluating Deployed Decision Support Systems for Security: Challenges, Analysis, and Approaches
    1. 26.1 Introduction
    2. 26.2 Descriptions of Systems
    3. 26.3 Formulating the Problem
      1. 26.3.1 Abstracting the Real-World Problem
      2. 26.3.2 Solution Concepts and Computational Considerations
        1. Potential Solution Concepts
        2. Algorithmic Goals
      3. 26.3.3 Implementing the Solution
    4. 26.4 Evaluation Case Studies
      1. 26.4.1 Comparison with Previous Best Practices
      2. 26.4.2 Mathematical Sensitivity Analysis
      3. 26.4.3 Human Trials
      4. 26.4.4 Quantitative Data
      5. 26.4.5 Adversarial Perspective Team
      6. 26.4.6 Qualitative Expert Evaluations
      7. 26.4.7 Real-World Evaluations
        1. Fare Evasion Experiment
        2. Counterterrorism Experiment
    5. 26.5 Goals for Security Decision Support Systems
      1. 26.5.1 Security per Dollar
      2. 26.5.2 Threat Deterrence
    6. 26.6 Summary of Evaluation Types
      1. 26.6.1 Model-Based/Algorithmic
      2. 26.6.2 Cost-Benefit Analysis
      3. 26.6.3 Relative Benefit
      4. 26.6.4 Human Behavioral Experiments
      5. 26.6.5 Operational Record
      6. 26.6.6 High-Level Evaluations
    7. 26.7 Related Work
    8. 26.8 Conclusions
    9. Acknowledgments
    10. Notes
    11. References
  33. 27 Homeland Security Resource Allocation Games: Considering Partially Strategic Attackers and Equity
    1. 27.1 Introduction
    2. 27.2 Literature Review
      1. 27.2.1 Strategic and Non-strategic Threats
      2. 27.2.2 Equity Considerations in Risk Analysis
    3. 27.3 Hybrid Defensive Resource Allocation in the Face of a Partially Strategic Attacker
      1. 27.3.1 Model Formulation and Data Sources
      2. 27.3.2 Numerical Illustrations
      3. 27.3.3 Robustness Analyses
        1. Two False Beliefs and Definitions of Robustness
        2. Sensitivity Analyses
    4. 27.4 Cost of Equity in Defensive Resource Allocation in the Face of a Strategic Attacker
      1. 27.4.1 Model Formulation
      2. 27.4.2 Data Sources
      3. 27.4.3 Sensitivity Analyses
        1. Equity Type
        2. Cost-effectiveness of Defense
        3. Total Defense Budget
    5. 27.5 Conclusion
    6. References
  34. 28 Decision Analysis by Proxy for Adaptive Adversaries
    1. 28.1 Terrorism Context
    2. 28.2 Proxy Multiple Objectives Value Modeling
      1. 28.2.1 Methodology
    3. 28.3 Decision Maker and Context
    4. 28.4 Objectives Hierarchy
    5. 28.5 Attack Alternatives
    6. 28.6 Decision Tree Probability Estimates
    7. 28.7 Attribute Definition and Measurement
    8. 28.8 Risk Attitudes across Attributes
    9. 28.9 Value Trade-offs across Attributes
    10. 28.10 Results
      1. 28.10.1 Attack Alternative Ranking
      2. 28.10.2 Utilities by Proxy
    11. 28.11 Conclusions
    12. 28.12 Model Challenges
    13. 28.13 Model Applications
    14. References
  35. 29 Asymmetric Prescriptive/Descriptive Game Theory for Counterterrorism
    1. 29.1 Introduction
    2. 29.2 Asymmetrically Prescriptive/Descriptive Applications in Counterterrorism
    3. 29.3 Prospect Theory
    4. 29.4 Existing Work on PT in Game Theory
    5. 29.5 Impacts of Modeling the Adversary with PT Preferences
    6. 29.6 Conclusions and Further Research
    7. Acknowledgments
    8. References
  36. 30 Near-Misses and Decision Making Under Uncertainty in the Context of Cybersecurity
    1. 30.1 Introduction
    2. 30.2 Background – Warning Systems and Near-Miss Events
      1. 30.2.1 Resilient Near-Misses
      2. 30.2.2 Vulnerable Near-Misses
    3. 30.3 Study 1
      1. 30.3.1 Materials and Methods
      2. 30.3.2 Results
      3. 30.3.3 Discussion
    4. 30.4 Study 2
      1. 30.4.1 Materials and Methods
      2. 30.4.2 Results
      3. 30.4.3 Discussion
    5. 30.5 General Discussion
    6. 30.6 Conclusions
    7. References
  37. Index