Showing posts with label risk analysis. Show all posts
Showing posts with label risk analysis. Show all posts

Decision Analysis for Healthcare Managers Review

Decision Analysis for Healthcare Managers
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This is an interesting book that covers a variety of tools helpful to decision makers in the health industry. While these tools have been individually presented in other papers and books, I think this is probably the first book that has it all together. It first covers the very basics of what decision analysis is, as well as the fundamentals of probability. Then there are full chapters on decision trees, modeling group decisions, root-cause analysis, ... (see the table of contents). Within each chapter, there are real-world driven examples from the health industry with explanations of the detailed process in conducting the analysis (e.g. with sample interviews with the decision makers to get the parameters). I think it's a very good reference if you want to learn a decision analysis tool in detail with some health care examples or as a text book on the subject. It doesn't appear to be written to give a brief overview of the field just to satisfy one's curiosity (though the first chapter could help with that). It's more targeted at those who want to learn all or selected concepts and tools in detail.

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The first part of the book explains the various analytical tools that simplify and accelerate decision making. Learn about tools that help you determine causes, evaluate choices, and forecast future events. For occasions when a group, rather than an individual, has to make a decision, you will also learn what tools can help you create group consensus. The second half of the book shows you how to apply analytical tools to different healthcare situations, including comparing clinician performance, determining the causes for medical errors, analyzing the costs of programs, and determining the market for new services. Many practical examples walk you step-by-step through common decision-making scenarios.

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Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research & Management Science) Review

Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research and Management Science)
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This is an excellent, approachable read for any risk manager; understanding its examples requires only elementary probability, statistics and calculus, though the foundations are much deeper. The author uses direct language, and does not hesitate to declare a fashionable risk analysis technique "worse than useless." The author shows how not to do risk analysis, using simple but devastating examples to illustrate the weaknesses of prioritized investments, subject matter expert opinion, risk matrices and qualitative risk assessments, and the independence assumption. Then, case studies present constructive examples of good practice. Refreshingly, this text clearly distinguishes between threats from Mother Nature, and those posed by an intelligent adversary. There is unevenness, because this is an edited ensemble of papers originally published in a variety of technical journals; however, this is also a strength, because the appeal and scholarship underlying biological, engineering, and social science examples is broad. This is not a how-to guide, and won't help fill in a blank page risk analysis; however, this is an excellent source for the skeptical consumer of contemporary risk management advice and products, and hopefully will have some influence with policy makers who are the source of simplistic and dangerous guidance.

Gerald G. Brown
Distinguished Professor of Operations Research
Naval Postgraduate School
National Academy of Engineering
INFORMS Fellow


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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.

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The Failure of Risk Management: Why It's Broken and How to Fix It Review

The Failure of Risk Management: Why It's Broken and How to Fix It
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How do we know if our risk management methods are working? Would we notice if they were not working? What are the consequences if they are not working? These are the three basic questions that Douglas Hubbard asks in his book The Failure of Risk Management.
In this book Mr. Hubbard lays out the basics of risk management and explains why many risk management methods are worse than useless. He also provides some ideas and first steps to fix the problem.
Here's a brief walk though 'The Failure of Risk Management':
Part I introduces the history of risk management and the problems with modern risk management methods. Independent events, for instance, are often times not independent at all. This common-mode failure is unaccounted for by many managers, yet can be devastating in an emergency.
Part II of the book goes in depth with some of the problems and failures of risk management, and to me was the most interesting part of the book. Chapter 4 is called The "Four Horseman" of Risk Management, and describes the differences between what the author considers the four main classes of risk managers. The four classes are actuaries, "war quants," economists, and management consultants. Each group has distinctly different methods and areas of expertise, as well as different levels of validation.
Chapter 5 is about how risk should be defined, and why different people may actually be talking about different things when they discuss volatility and risk. Chapter 6 breaks down why humans are not good at subjective methods (which lays the ground work for later chapters introducing quantitative methods). There are a few "calibration" tests available for you to see how overconfident you are in your decision making. These are pretty interesting, and even after reading about overconfidence I still did poorly on them.
Chapters 7, 8, and 9 talk about problems with subjective scoring methods, problems with describing one-off events, and the problems with some quantitative models. The author talks about "black swans," as described by Nassim Nicholas Taleb, and how they relate to modeling. Many times people believe that events can't be modeled, but the author believes this is not so.
The last section of the book, Part III, gives some ideas on how to fix risk management. Adding empiricism is a big start, as well as calibration of subjective human inputs. Many companies build and use models, but then they don't actually bother to see how well the things have performed in the past. I will leave the rest of the solutions for you to read in the book.
Recommendation:
First off, the author says this book is geared towards all types of risk management, and all types of industries, and I think this is true. The author uses a wide variety of examples from airplane engine failures to volcano eruptions. But I still feel like this book is more geared towards enterprise risk management, and less towards the already quant heavy fields such as actuarial science or credit risk management. But it was an interesting read nonetheless.
It seems like in the past 20 years there have been several so-called "once-in-a-lifetime events," such as the floods of Hurricane Katrina or any of the financial crisis, including 1987, 1998, 2000, or 2008. I wish I had the money to buy this book for every person who ever said "no one saw that coming."
I think this is a great book for anyone who deals with the potential for risk, loss, or damage - no matter if it is financial, personal, or physical. When the stakes are high we should be careful relying on a risk matrix developed by a management consultant, and Douglas Hubbard will tell you why. If you work in risk management, or if you have influence on the operational strategy of some organization, then this book is a must read.


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An essential guide to the calibrated risk analysis approach
The Failure of Risk Management takes a close look at misused and misapplied basic analysis methods and shows how some of the most popular "risk management" methods are no better than astrology! Using examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and more, Hubbard reveals critical flaws in risk management methods–and shows how all of these problems can be fixed. The solutions involve combinations of scientifically proven and frequently used methods from nuclear power, exploratory oil, and other areas of business and government. Finally, Hubbard explains how new forms of collaboration across all industries and government can improve risk management in every field.
Douglas W. Hubbard (Glen Ellyn, IL) is the inventor of Applied Information Economics (AIE) and the author of Wiley's How to Measure Anything: Finding the Value of Intangibles in Business (978-0-470-11012-6), the #1 bestseller in business math on Amazon. He has applied innovative risk assessment and risk management methods in government and corporations since 1994.
"Doug Hubbard, a recognized expert among experts in the field of risk management, covers the entire spectrum of risk management in this invaluable guide. There are specific value-added take aways in each chapter that are sure to enrich all readers including IT, business management, students, and academics alike"—Peter Julian, former chief-information officer of the New York Metro Transit Authority. President of Alliance Group consulting
"In his trademark style, Doug asks the tough questions on risk management. A must-read not only for analysts, but also for the executive who is making critical business decisions."—Jim Franklin, VP Enterprise Performance Management and General Manager, Crystal Ball Global Business Unit, Oracle Corporation.

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Offshore Risk Assessment: Principles, Modelling and Applications of QRA Studies (Springer Series in Reliability Engineering) Review

Offshore Risk Assessment: Principles, Modelling and Applications of QRA Studies (Springer Series in Reliability Engineering)
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This book has 15 chapters, everything from basic definitions, such as PLL, frequency of impairment etc, to chapters where explosion risk modelling are described quite in detail. On an overall basis, I am sure every pro- can learn something from this book. However, it is not a guideline, outlining a stepwise QRA-approach. It is rather a collection of chapters (sometimes with varying quality) where different aspects of risk analysis are discussed. Chapter 10 (Collision Risk Modelling) gives detailed information about both the historical ship collision incidents and a model about how to calculate the collision energy & consequences. Obviously, the section about historical incidents is a bit outdated and include a few misleading information. Appendix A gives an overview of the softwares in the market. Some of the softwares in the list are not supported anymore. Furthermore, RiskSpectrum is a PSA tool, pretty much tailored for nuc- business. To conclude: Mr. Vinnem and his team put a lot of effort into this book. I think everybody can find something they may like in the book.

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Offshore Risk Assessment was the first book to deal with quantified risk assessment (QRA) as applied specifically to offshore installations and operations. This book is a major revision of the first edition. It has been informed by a major R&D programme on offshore risk assessment in Norway (2002-2006). Not only does this book describe the state-of-the-art of QRA, it also identifies weaknesses and areas that need development.

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Risk Assessment in Geotechnical Engineering Review

Risk Assessment in Geotechnical Engineering
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Two years ago when I started my masters in structural engineering, I had to learn about random field theory. The books that I found, considered the "must reads" for random field theory, might as well have been in Chinese (I don't understand Chinese - just for the record). It was extremely painful and frustrating trying to understand the completely not clear or easy to understand concepts - as they were presented in those books. Upon searching the internet, I found some papers written by Gordon Fenton. His description of random field theory and his work in general, was like an oasis in the desert. This is one very rare person who can explain complicated concepts so that they are easy to understand and apply. So when I found out he was publishing a book, I thought, this is a must have. And I haven't been disappointed. The first section on probability theory is without a doubt, the best explanation of the theory I have seen, with simple examples to help me concrete my understanding of the theory being presented. Therefore, this is the first textbook that I would recommend for people trying to understand the concepts of probability theory. Similarly, all other sections in the book are equally easy to understand and can make even me look intelligent - as he enables me to really understand the concepts being presented, because of his fantastic ability to communicate ' I like in particular, how he explains the practical reasons for why the theory can be simplified or assumptions can be made. Most boffin writers of textbooks don't explain "the obvious" which is not so obvious to the novice person reading their books. Sometimes I think that they don't realise that not everybody has done the mathematics degree required to understand some of the intricacies, which if not clearly explained, can stump the reader for days until they find out why they have assumed this or that. That's where Gordon Fenton is a real a gem. He doesn't leave you scratching your head, scrambling for other textbooks to fill in the gaps before you can continue with his explanations. So I highly recommend this book to anybody trying to understand probability theory, random field theory, estimation, reliability, Monte Carlo simulation and all other topics he covers in his book.

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Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level (The Wiley Finance Series) Review

Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level (The Wiley Finance Series)
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Michael Rees succeeds to a large extent in his endeavor of writing a text that addresses the financial modeling process instead of Excel functionality, financial theory, or mathematical models. To his credit, Rees has put together a large number of useful modeling examples in the CD-ROM that is sold with the text. Rees' book assumes that readers have at least an intermediate knowledge of both statistical and financial concepts.
After reviewing select Excel functions and tools relevant to financial modeling, Rees gives his audience of modelers many practical tips about how to design, structure, and build models that are relevant, accurate, and easily understandable. Whoever has experience with models will probably agree with Rees when he writes that the majority of models built are in practice of mediocre quality. Someone other than the author of the model will often experience several challenges in dealing with the model at hand, i.e., too much time spent on understanding the model, complexity of the auditing and validating processes, hard to share with others, over-reliance on the original modeler to maintain or use it, lack of clarity of objectives, and presence of errors and implicit assumptions.
Rees then goes into the modeling of financial statements that is often required in the world of corporate finance for forecasting profit and cash, assessing financing requirements, analyzing credit risk and valuation, etc. This chapter is a little gem. It contains many practical tips. Once again, readers will be reminded that there is not always 100% agreement on the definition of some financial concepts.
Rees then uses Palisade Corporation's add-ins @RISK and PrecisionTree for many modeling examples in the two chapters that he dedicates to risk modeling and real option modeling, respectively. Having some understanding of both statistical and financial concepts is particularly important here to benefit from reading both chapters. Probably, many readers with an advanced knowledge of Excel 2007 will regret that the above-mentioned functionality that Palisade Corporation offers has not yet been systematically integrated into at least Microsoft Office Professional.
Finally, Rees discusses the use of Visual Basic for Applications (VBA) in a range of practical financial modeling situations. Rees points out that many otherwise competent modelers never learn VBA. For this reason, Rees makes the assumption that his audience is not very familiar with VBA. Rees shows how macros, i.e., subroutines and user-defined functions, can be used in a variety of modeling contexts.
In conclusion, Rees has made a valuable contribution to the field of financial modeling. The CD-ROM that is sold with the text plays a key role in achieving this objective.


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Risk Modeling, Assessment, and Management (Wiley Series in Systems Engineering and Management) Review

Risk Modeling, Assessment, and Management (Wiley Series in Systems Engineering and Management)
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The author spends a great deal of time describing his models and methods (referred to by highly forgettable acronyms) as if they were standards of practice. The writing is not easy to read and the organization of the material and poor indexing do not facilitate use as a reference. A lack of problems and exercises (as well as the use of non-standard terminology) makes the book hard to use as a classroom text.
There are much better book available that cover this material.

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Examines timely multidisciplinary applications, problems, and case histories in risk modeling, assessment, and management
Risk Modeling, Assessment, and Management, Third Edition describes the state of the art of risk analysis, a rapidly growing field with important applications in engineering, science, manufacturing, business, homeland security, management, and public policy. Unlike any other text on the subject, this definitive work applies the art and science of risk analysis to current and emergent engineering and socioeconomic problems. It clearly demonstrates how to quantify risk and construct probabilities for real-world decision-making problems, including a host of institutional, organizational, and political issues.
Avoiding higher mathematics whenever possible, this important new edition presents basic concepts as well as advanced material. It incorporates numerous examples and case studies to illustrate the analytical methods under discussion and features restructured and updated chapters, as well as:

A new chapter applying systems-driven and risk-based analysis to a variety of Homeland Security issues

An accompanying FTP site—developed with Professor Joost Santos—that offers 150 example problems with an Instructor's Solution Manual and case studies from a variety of journals

Case studies on the 9/11 attack and Hurricane Katrina

An adaptive multiplayer Hierarchical Holographic Modeling (HHM) game added to Chapter Three

This is an indispensable resource for academic, industry, and government professionals in such diverse areas as homeland and cyber security, healthcare, the environment, physical infrastructure systems, engineering, business, and more. It is also a valuable textbook for both undergraduate and graduate students in systems engineering and systems management courses with a focus on our uncertain world.


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Optimization Modeling with Spreadsheets Review

Optimization Modeling with Spreadsheets
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Great book! It gives you exactly what you need to know to actually put together a working model on your own. It has enough theory so you understand what you're doing, and a lot of practical application material on how to actually do it. It was my third "modeling" book, but the first one to bridge the gap between theory and application. I attribute this primarily on the author's choice of focusing on showing the models in Excel. The other books I've picked up have tried to maintain a neutrality on what you use to build your model, and they focus on the abstract methods, which are important, but leave you lacking the actual details on how to get it done! And let's face it: Chances are you're going to build your model in Excel and use the Frontline solvers anyway. That being said, I now feel confident enough to take my models out of Excel and plant them in other solvers. Once you build a few, the concepts stick.

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Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains Review

Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains
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Some sections are very good others are really bad. Each chapter has a different author who was selected as an expert for that topic. It feels like the overall book was rushed to print without really scrubbing each chapter and tying the whole book together.
Some sections were mainly plugs for the authors research project. Some other section are very focused on using a particular tool. And really... How does a discussion on GUIs fit in a chapter on human behavior modeling?
This book could be so much better but the editors need to put their red pens to work.

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An insightful presentation of the key concepts, paradigms, and applications of modeling and simulation
Modeling and simulation has become an integral part of research and development across many fields of study, having evolved from a tool to a discipline in less than two decades. Modeling and Simulation Fundamentals offers a comprehensive and authoritative treatment of the topic and includes definitions, paradigms, and applications to equip readers with the skills needed to work successfully as developers and users of modeling and simulation.
Featuring contributions written by leading experts in the field, the book's fluid presentation builds from topic to topic and provides the foundation and theoretical underpinnings of modeling and simulation. First, an introduction to the topic is presented, including related terminology, examples of model development, and various domains of modeling and simulation. Subsequent chapters develop the necessary mathematical background needed to understand modeling and simulation topics, model types, and the importance of visualization. In addition, Monte Carlo simulation, continuous simulation, and discrete event simulation are thoroughly discussed, all of which are significant to a complete understanding of modeling and simulation. The book also features chapters that outline sophisticated methodologies, verification and validation, and the importance of interoperability. A related FTP site features color representations of the book's numerous figures.
Modeling and Simulation Fundamentals encompasses a comprehensive study of the discipline and is an excellent book for modeling and simulation courses at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of computational statistics, engineering, and computer science who use statistical modeling techniques.

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