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(More customer reviews)People make decisions under uncertainties everyday, consciously or unconsciously. Have you ever run into the situation that you browse dozens of web pages to make you feel certain to buy a certain product, but turned out to found out that the information could never be adequate? Why can you see so many computer failures? Have you ever thought that the computer you are using was designed partially by irrational guessing because of inadequate design input? In above cases uncertainties cost people time and money, and in many cases, such as transportation and defense, they cost human lives. Facing uncertainties, when people can no longer collect any more information to make them feel certain, classical theory fails to work as it is expected. Usually people have two options: ignore uncertainties or make a guess of the uncertain information. However, thousands of incidences have indicated that both options can lead to catastrophic results. This book provided a third option, uncertainty modeling and analysis, which has been proved efficient and useful by various applications in many fields ranging from politics to technology.
I have worked for many years in designing high reliable notebook computers in an international company. Uncertainties have caused many persisting problems to my research and develop work. One simple case is the free drop of a notebook computer. No matter how hard I tried to control the drop, there was no useful pattern of shocks which were generated by the drops. Moreover, I cannot drop hundred of these kind of expensive products to get adequate data, thus it seemed almost impossible to make rational decisions in treating drops. I am lucky that I got the chance to attend the author Professor B. M. Ayyub's class and find that this book is quite useful to deal with my problem. Professor Ayyub is a leading authority in the areas of risk analysis, uncertainty modeling, decision analysis, and systems engineering in the world. Another author, George J. Klir is a distinguished professor of systems science at Binghamton University, State University of New York. Their experience and success in academy and industry guaranteed the quality of this book.
At the beginning of the book, the author presented different philosophies and related topics about uncertainty. No technique tools are provided, but the presented information is quite enlightening for engineering and scientific work. The reader is also encouraged to cultivate a habit to analyze problems in a systematic manner, which is quite important but is often ignored. Examples given for this purpose in the book may look overly concise, but because of the difficulty to provide so much information from different schools of philosophy in one chapter, the author has to cut off some information. The first chapter can serve as an independent material for reading. Even if you don't have time to finish the whole book, do not miss the first chapter.
When the reader has the basic understanding of system and uncertainty, the author begins to help the reader to recollect the classical theory of probability, and then establishes a passage from the classical theory to the uncertainty theory. It is worth noticing that, on one hand, the languages, techniques and examples chosen are simple enough for most readers. Even if you have trouble to remember the classical probability theory of your undergraduate study, you won't feel very hard to catch up with the book. On the other hand, this simplification doesn't hurt the information that should be conveyed to the reader. When the passage is established, the reader can find out that she or he can benefit in two aspects: for one thing, the reader can understand some important principles of uncertainty theory more easily based on her or his previous understanding of the counterpart classical theory principles. For another, many methods in dealing with uncertainties can be converted into problems in classical theory, which has plentiful of matured tools. In the following chapters, the reader learned useful skills like synthesizing uncertainty and information, measuring uncertainty, and finally learned how to model uncertainty and analyze it for specific engineering and scientific problems.
Although this book is aimed at engineering and sciences, professionals and students in other areas like marketing and management can also benefit from this book. For example, in computer industry, product managers usually finds it difficult to make a proper product specification to address a new or emerging market without some of the critical information. This book can help them to analyze the uncertainty and to be more able to make the right decision. For the people who want to dive deeper into this area, this book provides a valuable list of reference.
Every book causes some kind of inconvenience for its readers. In this book, the exercise problems at the end of each chapter are well designed, but there is no answer to the question. The book in general is quite well organized, but there is a couple of terms are not inconsistently used. However, I think they won't cause too much trouble for most readers.
In sum, this book provides effective medicine to treat uncertainty, the pain point for most engineering and scientific problems. People who have studied undergraduate probability theory won't have any trouble in reading it. Except textbook, this book can also serve as a valuable manual and handbook for data organizing and decision making in research.
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Engineers and scientists often need to solve complex problems with incomplete information resources, necessitating a proper treatment of uncertainty and a reliance on expert opinions. Uncertainty Modeling and Analysis in Engineering and the Sciences prepares current and future analysts and practitioners to understand the fundamentals of knowledge and ignorance, how to model and analyze uncertainty, and how to select appropriate analytical tools for particular problems.This volume covers primary components of ignorance and their impact on practice and decision making. It provides an overview of the current state of uncertainty modeling and analysis, and reviews emerging theories while emphasizing practical applications in science and engineering.The book introduces fundamental concepts of classical, fuzzy, and rough sets, probability, Bayesian methods, interval analysis, fuzzy arithmetic, interval probabilities, evidence theory, open-world models, sequences, and possibility theory. The authors present these methods to meet the needs of practitioners in many fields, emphasizing the practical use, limitations, advantages, and disadvantages of the methods.
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