In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems – systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence – and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

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.

In every decision problem there are things we know and things we do not know. Risk analysis science uses the best available evidence to assess what we know while it is carefully intentional in the way it addresses the importance of the things we do not know in the evaluation of decision choices and decision outcomes. The field of risk analysis science continues to expand and grow and the second edition of Principles of Risk Analysis: Decision Making Under Uncertainty responds to this evolution with several significant changes. The language has been updated and expanded throughout the text and the book features several new areas of expansion including five new chapters. The book’s simple and straightforward style—based on the author’s decades of experience as a risk analyst, trainer, and educator—strips away the mysterious aura that often accompanies risk analysis. Features: Details the tasks of risk management, risk assessment, and risk communication in a straightforward, conceptual manner Provides sufficient detail to empower professionals in any discipline to become risk practitioners Expands the risk management emphasis with a new chapter to serve private industry and a growing public sector interest in the growing practice of enterprise risk management Describes dozens of quantitative and qualitative risk assessment tools in a new chapter Practical guidance and ideas for using risk science to improve decisions and their outcomes is found in a new chapter on decision making under uncertainty Practical methods for helping risk professionals to tell their risk story are the focus of a new chapter Features an expanded set of examples of the risk process that demonstrate the growing applications of risk analysis As before, this book continues to appeal to professionals who want to learn and apply risk science in their own professions as well as students preparing for professional careers. This book remains a discipline free guide to the principles of risk analysis that is accessible to all interested practitioners. Files used in the creation of this book and additional exercises as well as a free student version of Palisade Corporation’s Decision Tools Suite software are available with the purchase of this book. A less detailed introduction to the risk analysis science tasks of risk management, risk assessment, and risk communication is found in Primer of Risk Analysis: Decision Making Under Uncertainty, Second Edition, ISBN: 978-1-138-31228-9.

Primer on Risk Analysis: Decision Making Under Uncertainty, Second Edition lays out the tasks of risk analysis in a straightforward, conceptual manner, tackling the question, "What is risk analysis?" Distilling the common principles of many risk dialects into serviceable definitions, it provides a foundation for the practice of risk management and decision making under uncertainty for professionals from all disciplines. New in this edition is an expanded risk management emphasis that includes an overview chapter on enterprise risk management and a chapter on decision making under uncertainty designed to help decision makers use the results of risk analysis in practical ways to improve decisions and their outcomes. This book will empower you to enter the world of risk management in your own domain of expertise by providing you with practical, insightful, useful and adaptable knowledge of risk analysis science including risk management, risk assessment, and risk communication. Features: Answers the fundamental question, "What is Risk Analysis?" Presents the tasks of risk management, risk assessment, and risk communication in a straightforward, conceptual manner Responds to the continuing evolution of risk science and addresses the language of risk as it continues to evolve Expands the risk management emphasis with a new chapter to serve private industry and a growing public sector interest in the growing practice of enterprise risk management Includes a new chapter on decision making under uncertainty provides practical guidance and ideas for using risk science to improve decisions and their outcomes Features an expanded set of examples of the risk process that demonstrate the growing applications of risk analysis This book is suitable for executives, professionals and students who seek a fundamental understanding of risk management, risk assessment, and risk communication. A more detailed examination of this topic, suitable for practitioners from any discipline as well as students and professionals who aspire to become experts in the practice of risk analysis science, is found in Principles of Risk Analysis: Decision Making Under Uncertainty, Second Edition, ISBN: 978-1-138-47820-6.

Explores methods for the representation and treatment of uncertainty in risk assessment In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications. While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences. Uncertainty in Risk Assessment: Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts. Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory. Offers guidance on when to use probability and when to use an alternative representation of uncertainty. Presents and discusses methods for the representation and characterization of uncertainty in risk assessment. Uses examples to clearly illustrate ideas and concepts.

This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides : a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction ; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techniques for uncertainty representation ; a description of the different steps involved in a Probabilistic Risk Assessement (PRA) or Quantitative Risk Assessment (QRA), and an analysis of the types of uncertainty that can affect each of these steps ; annexes giving an overview of a number of tools used during probabilistic risk assessment, including the HAZID technique, fault trees and event tree analysis.

Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ? Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. It goes beyond the “black-box” view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making. Modelling Under Risk and Uncertainty: Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems. Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events. Implements decision theory choices differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty through a consistent multi-disciplinary set of statistical estimation, physical modelling, robust computation and risk analysis. Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition. Illustrated with one favourite pedagogical example crossing natural risk, engineering and economics, developed throughout the book to facilitate the reading and understanding. Supports Master/PhD-level course as well as advanced tutorials for professional training Analysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.