Risk Modeling, Assessment, and Management

Presents systems-based theory, methodology, and applications in risk modeling, assessment, and management This book examines risk analysis, focusing on quantifying risk and constructing probabilities for real-world decision-making, including engineering, design, technology, institutions, organizations, and policy. The author presents fundamental concepts (hierarchical holographic modeling; state space; decision analysis; multi-objective trade-off analysis) as well as advanced material (extreme events and the partitioned multi-objective risk method; multi-objective decision trees; multi-objective risk impact analysis method; guiding principles in risk analysis); avoids higher mathematics whenever possible; and reinforces the material with examples and case studies. The book will be used in systems engineering, enterprise risk management, engineering management, industrial engineering, civil engineering, and operations research. The fourth edition of Risk Modeling, Assessment, and Management features: Expanded chapters on systems-based guiding principles for risk modeling, planning, assessment, management, and communication; modeling interdependent and interconnected complex systems of systems with phantom system models; and hierarchical holographic modeling An expanded appendix including a Bayesian analysis for the prediction of chemical carcinogenicity, and the Farmer’s Dilemma formulated and solved using a deterministic linear model Updated case studies including a new case study on sequential Pareto-optimal decisions for emergent complex systems of systems A new companion website with over 200 solved exercises that feature risk analysis theories, methodologies, and application Risk Modeling, Assessment, and Management, Fourth Edition, is written for both undergraduate and graduate students in systems engineering and systems management courses. The text also serves as a resource for academic, industry, and government professionals in the fields of homeland and cyber security, healthcare, physical infrastructure systems, engineering, business, and more.

Table of Contents:
Preface to the Fourth Edition ix The Companion Website xv Acknowledgments xvii Part I Fundamentals of Risk Modeling, Assessment, and Management 1 1 The Art and Science of Systems and Risk Analysis 3 1.1 Introduction 3 1.2 Systems Engineering 4 1.3 Risk Assessment and Management 14 1.4 Concept Road Map 26 1.5 Epilogue 35 References 35 2 The Role of Modeling in the Definition and Quantification of the Risk Function 41 2.1 Introduction 41 2.2 The Risk Assessment and Management Process: Historical Perspectives 43 2.3 Information, Intelligence, and Models 45 2.4 The Building Blocks of Mathematical Models 47 2.5 On the Complex Definition of Risk, Vulnerability, and Resilience: a Systems‐Based Approach 51 2.6 On the Definition of Vulnerabilities in Measuring Risks to Systems 56 2.7 On the Definition of Resilience in Measuring Risk to Systems 57 2.8 On the Complex Quantification of Risk to Systems 60 References 65 3 Identifying Risk through Hierarchical Holographic Modeling and its Derivatives 69 3.1 Hierarchical Aspects 69 3.2 Hierarchical Overlapping Coordination 70 3.3 HHM 73 3.4 HHM and the Theory of Scenario Structuring 76 3.5 Adaptive Multiplayer HHM Game 79 3.6 Water Resources System 80 3.7 Sustainable Development 83 3.8 HHM in a System Acquisition Project 86 3.9 Software Acquisition 90 3.10 Hardening the Water Supply Infrastructure 94 3.11 Risk Assessment and Management for Support of Operations other than War 98 3.12 Automated Highway System 103 3.13 Food‐Poisoning Scenarios 108 References 113 4 Modeling and Decision Analysis 115 4.1 Introduction 115 4.2 Decision Rules Under Uncertainty 116 4.3 Decision Trees 118 4.4 Decision Matrix 122 4.5 The Fractile Method 124 4.6 Triangular Distribution 127 4.7 Influence Diagrams 128 4.8 Population Dynamic Models 132 4.9 PSM 139 4.10 Example Problems 144 References 152 5 Multiobjective Trade‐off Analysis 155 5.1 Introduction 155 5.2 Examples of Multiple Environmental Objectives 157 5.3 The Surrogate Worth Trade‐off Method 159 5.4 Characterizing a Proper Noninferior Solution 166 5.5 The SWT Method and the Utility Function Approach 168 5.6 Example Problems 172 5.7 Summary 177 References 178 6 Defining Uncertainty and Sensitivity Analysis 179 6.1 Introduction 179 6.2 Sensitivity, Responsivity, Stability, and Irreversibility 180 6.3 Uncertainties Due to Errors in Modeling 182 6.4 Characterization of Modeling Errors 183 6.5 Uncertainty Taxonomy 185 6.6 The USIM 196 6.7 Formulation of the Multiobjective Optimization Problem 199 6.8 A Robust Algorithm of the USIM 204 6.9 Integration of the USIM with Parameter Optimization at the Design Stage 207 6.10 Conclusions 209 References 209 7 Risk Filtering, Ranking, and Management 211 7.1 Introduction 211 7.2 Past Efforts in Risk Filtering and Ranking 212 7.3 RFRM: A Methodological Framework 213 7.4 Case Study: An OOTW 220 7.5 Summary 224 References 224 Part II Advances in Risk Modeling, Assessment, and Management 227 8 Risk of Extreme Events and the Fallacy of the Expected Value 229 8.1 Introduction 229 8.2 Risk of Extreme Events 230 8.3 The Fallacy of the Expected Value 232 8.4 The PMRM 233 8.5 General Formulation of the PMRM 236 8.6 Summary of the Pmrm 238 8.7 Illustrative Example 239 8.8 Analysis of Dam Failure and Extreme Flood through the PMRM 240 8.9 Example Problems 243 8.10 Summary 257 References 257 9 Multiobjective Decision‐tree Analysis 259 9.1 Introduction 259 9.2 Methodological Approach 261 9.3 Differences between SODT and MODT 279 9.4 Summary 281 9.5 Example Problems 282 References 293 10 Multiobjective Risk Impact Analysis Method 295 10.1 Introduction 295 10.2 Impact Analysis 296 10.3 The Multiobjective, Multistage Impact Analysis Method: An Overview 297 10.4 Combining the PMRM and the MMIAM 298 10.5 Relating Multiobjective Decision Trees to the MRIAM 304 10.6 Example Problems 313 10.7 Epilogue 325 References 326 11 Statistics of Extremes: Extension of the PMRM 329 11.1 A Review of the Partitioned Multiobjective Risk Method 329 11.2 Statistics of Extremes 333 11.3 Incorporating the Statistics of Extremes into the PMRM 338 11.4 Sensitivity Analysis of the Approximation of f4(·) 344 11.5 Generalized Quantification of Risk of Extreme Events 350 11.6 Summary 356 11.7 Example Problems 357 References 368 12 Systems‐Based Guiding Principles for Risk Modeling, Planning, Assessment, Management, and Communication 371 12.1 Introduction 371 12.2 The Journey: The Guiding Principles in the Broader Context of the Emerging Next Generation Developed by the Federal Aviation Administration  372 References 387 13 Fault Trees 389 13.1 Introduction 389 13.2 Basic Fault-Tree Analysis 391 13.3 Reliability and Fault-Tree Analysis 392 13.4 Minimal Cut Sets 397 13.5 The DARE Using Fault Trees 400 13.6 Extreme Events in Fault Tree Analysis 403 13.7 An Example Problem Based on a Case Study 405 13.8 Failure Mode and Effects Analysis and Failure Mode, Effects, and Criticality Analysis 409 13.9 Event Trees 411 13.10 Example Problems 414 References 420 14 Multiobjective Statistical Method 423 14.1 Introduction 423 14.2 Mathematical Formulation of the Interior Drainage Problem 424 14.3 Formulation of the Optimization Problem 424 14.4 The MSM: Step-by-Step 425 14.5 The SWT Method 427 14.6 Multiple Objectives 428 14.7 Applying the MSM 429 14.8 Example Problems 432 References 438 15 Principles and Guidelines for Project Risk Management 439 15.1 Introduction 439 15.2 Definitions and Principles of Project Risk Management 440 15.3 Project Risk Management Methods 443 15.4 Aircraft Development Example 450 15.5 Quantitative Risk Assessment and Management of Software Acquisition 454 15.6 Critical Factors That Affect Software Nontechnical Risk 458 15.7 Basis for Variances in Cost Estimation 460 15.8 Discrete Dynamic Modeling 461 15.9 Summary 469 References 469 16 Modeling Complex Systems of Systems with Phantom System Models 473 16.1 Introduction 473 16.2 What Have We Learned from Other Contributors? 474 16.3 The Centrality of the States of the System in Modeling and in Risk Analysis 476 16.4 The Centrality of Time in Modeling Multidimensional Risk, Uncertainty, and Benefits 477 16.5 Extension of HHM to PSM 478 16.6 PSM and Meta-modeling 480 16.7 PSM Laboratory 486 16.8 Summary 488 References 489 17 Adaptive Two‐Player Hierarchical Holographic Modeling Game for Counterterrorism Intelligence Analysis 493 17.1 Introduction 493 17.2 Bayes’ Theorem 494 17.3 Modeling the Multiple Perspectives of Complex Systems 495 17.4 Adaptive Two‐Player Hhm Game: Terrorist Networks versus Homeland Protection 499 17.5 The Building Blocks of Mathematical Models and the Centrality of State Variables in Intelligence Analysis 502 17.6 Hierarchical Adaptive Two‐Player HHM Game 504 17.7 Collaborative Computing Support for Adaptive Two‐Player HHM Games 505 17.8 Summary 507 References 508 18 Inoperability Input–Output Model and Its Derivatives for Interdependent Infrastructure Sectors 511 18.1 Overview 511 18.2 Background: The Original Leontief Input–Output Model 512 18.3 Inoperability Input–Output Model 513 18.4 Regimes of Recovery 516 18.5 Supporting Databases for IIM Analysis 517 18.6 National and Regional Databases for IIM Analysis 518 18.7 RIMS II 522 18.8 Development of the IIM and its Extensions 523 18.9 The Dynamic IIM 527 18.10 Practical Uses of the IIM 530 18.11 Uncertainty IIM 533 18.12 Example Problems 536 18.13 Summary 539 References 540 19 Case Studies 543 19.1 A Risk‐Based Input–Output Methodology for Measuring the Effects of the August 2003 Northeast Blackout  543 19.2 Systemic Valuation of Strategic Preparedness Through Applying the IIM with Lessons Learned from Hurricane Katrina 558 19.3 Ex Post Analysis Using the IIM of the September 11, 2001, Attack on the United States 569 19.4 Risk Modeling, Assessment, and Management of Lahar Flow Threat 575 19.5 The Statistics of Extreme Events and 6‐Sigma Capability 587 19.6 Sequential Pareto‐Optimal Decisions Made During Emergent Complex Systems of Systems: An Application to the Faa Nextgen 593 References 612 Appendix: Optimization Techniques 617 A.1 Introduction to Modeling and Optimization 617 A.2 Bayesian Analysis and the Prediction of Chemical Carcinogenicity 655 A.3 The Farmer’s Dilemma: Linear Model and Duality 657 A.4 Standard Normal Probability Table 664 References 665 Author Index 667 Subject Index 673