Risk Modeling, Assessment, and Management

YACOV Y. HAIMES, PhD, is the Lawrence R. Quarles Professor at the School of Engineering and Applied Science, University of Virginia, USA, and is a member of the Systems and Information Engineering faculty and the Civil and Environmental Engineering faculty. He is the Founding Director (1987) of the university-wide Center for Risk Management of Engineering Systems. On the faculty of Case Western Reserve University, USA, for 17 years, he was the Chair of the Systems Engineering Department, and Director of the Center for Large-Scale Systems and Policy Analysis.

• Preface to the Fourth Edition ixThe Companion Website xvAcknowledgments xviiPart I Fundamentals of Risk Modeling, Assessment, and Management 11 The Art and Science of Systems and Risk Analysis 31.1 Introduction 31.2 Systems Engineering 41.3 Risk Assessment and Management 141.4 Concept Road Map 261.5 Epilogue 35References 352 The Role of Modeling in the Definition and Quantification of the Risk Function 412.1 Introduction 412.2 The Risk Assessment and Management Process: Historical Perspectives 432.3 Information, Intelligence, and Models 452.4 The Building Blocks of Mathematical Models 472.5 On the Complex Definition of Risk, Vulnerability, and Resilience: a Systems‐Based Approach 512.6 On the Definition of Vulnerabilities in Measuring Risks to Systems 562.7 On the Definition of Resilience in Measuring Risk to Systems 572.8 On the Complex Quantification of Risk to Systems 60References 653 Identifying Risk through Hierarchical Holographic Modeling and its Derivatives 693.1 Hierarchical Aspects 693.2 Hierarchical Overlapping Coordination 703.3 HHM 733.4 HHM and the Theory of Scenario Structuring 763.5 Adaptive Multiplayer HHM Game 793.6 Water Resources System 803.7 Sustainable Development 833.8 HHM in a System Acquisition Project 863.9 Software Acquisition 903.10 Hardening the Water Supply Infrastructure 943.11 Risk Assessment and Management for Support of Operations other than War 983.12 Automated Highway System 1033.13 Food‐Poisoning Scenarios 108References 1134 Modeling and Decision Analysis 1154.1 Introduction 1154.2 Decision Rules Under Uncertainty 1164.3 Decision Trees 1184.4 Decision Matrix 1224.5 The Fractile Method 1244.6 Triangular Distribution 1274.7 Influence Diagrams 1284.8 Population Dynamic Models 1324.9 PSM 1394.10 Example Problems 144References 1525 Multiobjective Trade‐off Analysis 1555.1 Introduction 1555.2 Examples of Multiple Environmental Objectives 1575.3 The Surrogate Worth Trade‐off Method 1595.4 Characterizing a Proper Noninferior Solution 1665.5 The SWT Method and the Utility Function Approach 1685.6 Example Problems 1725.7 Summary 177References 1786 Defining Uncertainty and Sensitivity Analysis 1796.1 Introduction 1796.2 Sensitivity, Responsivity, Stability, and Irreversibility 1806.3 Uncertainties Due to Errors in Modeling 1826.4 Characterization of Modeling Errors 1836.5 Uncertainty Taxonomy 1856.6 The USIM 1966.7 Formulation of the Multiobjective Optimization Problem 1996.8 A Robust Algorithm of the USIM 2046.9 Integration of the USIM with Parameter Optimization at the Design Stage 2076.10 Conclusions 209References 2097 Risk Filtering, Ranking, and Management 2117.1 Introduction 2117.2 Past Efforts in Risk Filtering and Ranking 2127.3 RFRM: A Methodological Framework 2137.4 Case Study: An OOTW 2207.5 Summary 224References 224Part II Advances in Risk Modeling, Assessment, and Management 2278 Risk of Extreme Events and the Fallacy of the Expected Value 2298.1 Introduction 2298.2 Risk of Extreme Events 2308.3 The Fallacy of the Expected Value 2328.4 The PMRM 2338.5 General Formulation of the PMRM 2368.6 Summary of the Pmrm 2388.7 Illustrative Example 2398.8 Analysis of Dam Failure and Extreme Flood through the PMRM 2408.9 Example Problems 2438.10 Summary 257References 2579 Multiobjective Decision‐tree Analysis 2599.1 Introduction 2599.2 Methodological Approach 2619.3 Differences between SODT and MODT 2799.4 Summary 2819.5 Example Problems 282References 29310 Multiobjective Risk Impact Analysis Method 29510.1 Introduction 29510.2 Impact Analysis 29610.3 The Multiobjective, Multistage Impact Analysis Method: An Overview 29710.4 Combining the PMRM and the MMIAM 29810.5 Relating Multiobjective Decision Trees to the MRIAM 30410.6 Example Problems 31310.7 Epilogue 325References 32611 Statistics of Extremes: Extension of the PMRM 32911.1 A Review of the Partitioned Multiobjective Risk Method 32911.2 Statistics of Extremes 33311.3 Incorporating the Statistics of Extremes into the PMRM 33811.4 Sensitivity Analysis of the Approximation of f4(·) 34411.5 Generalized Quantification of Risk of Extreme Events 35011.6 Summary 35611.7 Example Problems 357References 36812 Systems‐Based Guiding Principles for Risk Modeling, Planning, Assessment, Management, and Communication 37112.1 Introduction 37112.2 The Journey: The Guiding Principles in the Broader Context of the Emerging Next Generation Developed by the Federal Aviation Administration  372References 38713 Fault Trees 38913.1 Introduction 38913.2 Basic Fault-Tree Analysis 39113.3 Reliability and Fault-Tree Analysis 39213.4 Minimal Cut Sets 39713.5 The DARE Using Fault Trees 40013.6 Extreme Events in Fault Tree Analysis 40313.7 An Example Problem Based on a Case Study 40513.8 Failure Mode and Effects Analysis and Failure Mode, Effects, and Criticality Analysis 40913.9 Event Trees 41113.10 Example Problems 414References 42014 Multiobjective Statistical Method 42314.1 Introduction 42314.2 Mathematical Formulation of the Interior Drainage Problem 42414.3 Formulation of the Optimization Problem 42414.4 The MSM: Step-by-Step 42514.5 The SWT Method 42714.6 Multiple Objectives 42814.7 Applying the MSM 42914.8 Example Problems 432References 43815 Principles and Guidelines for Project Risk Management 43915.1 Introduction 43915.2 Definitions and Principles of Project Risk Management 44015.3 Project Risk Management Methods 44315.4 Aircraft Development Example 45015.5 Quantitative Risk Assessment and Management of Software Acquisition 45415.6 Critical Factors That Affect Software Nontechnical Risk 45815.7 Basis for Variances in Cost Estimation 46015.8 Discrete Dynamic Modeling 46115.9 Summary 469References 46916 Modeling Complex Systems of Systems with Phantom System Models 47316.1 Introduction 47316.2 What Have We Learned from Other Contributors? 47416.3 The Centrality of the States of the System in Modeling and in Risk Analysis 47616.4 The Centrality of Time in Modeling Multidimensional Risk, Uncertainty, and Benefits 47716.5 Extension of HHM to PSM 47816.6 PSM and Meta-modeling 48016.7 PSM Laboratory 48616.8 Summary 488References 48917 Adaptive Two‐Player Hierarchical Holographic Modeling Game for Counterterrorism Intelligence Analysis 49317.1 Introduction 49317.2 Bayes’ Theorem 49417.3 Modeling the Multiple Perspectives of Complex Systems 49517.4 Adaptive Two‐Player Hhm Game: Terrorist Networks versus Homeland Protection 49917.5 The Building Blocks of Mathematical Models and the Centrality of State Variables in Intelligence Analysis 50217.6 Hierarchical Adaptive Two‐Player HHM Game 50417.7 Collaborative Computing Support for Adaptive Two‐Player HHM Games 50517.8 Summary 507References 50818 Inoperability Input–Output Model and Its Derivatives for Interdependent Infrastructure Sectors 51118.1 Overview 51118.2 Background: The Original Leontief Input–Output Model 51218.3 Inoperability Input–Output Model 51318.4 Regimes of Recovery 51618.5 Supporting Databases for IIM Analysis 51718.6 National and Regional Databases for IIM Analysis 51818.7 RIMS II 52218.8 Development of the IIM and its Extensions 52318.9 The Dynamic IIM 52718.10 Practical Uses of the IIM 53018.11 Uncertainty IIM 53318.12 Example Problems 53618.13 Summary 539References 54019 Case Studies 54319.1 A Risk‐Based Input–Output Methodology for Measuring the Effects of the August 2003 Northeast Blackout  54319.2 Systemic Valuation of Strategic Preparedness Through Applying the IIM with Lessons Learned from Hurricane Katrina 55819.3 Ex Post Analysis Using the IIM of the September 11, 2001, Attack on the United States 56919.4 Risk Modeling, Assessment, and Management of Lahar Flow Threat 57519.5 The Statistics of Extreme Events and 6‐Sigma Capability 58719.6 Sequential Pareto‐Optimal Decisions Made During Emergent Complex Systems of Systems: An Application to the Faa Nextgen 593References 612Appendix: Optimization Techniques 617A.1 Introduction to Modeling and Optimization 617A.2 Bayesian Analysis and the Prediction of Chemical Carcinogenicity 655A.3 The Farmer’s Dilemma: Linear Model and Duality 657A.4 Standard Normal Probability Table 664References 665Author Index 667Subject Index 673