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Uncertainty can take many forms, can be represented in many ways, and can have important implications in decision-making and policy development. This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management. The focus of this book is on the kinds and implications of uncertainty in environmental modeling and management, with practical guidelines and examples for successful modeling and risk analysis in the face of uncertain conditions and incomplete information. Provided is a clear classification of uncertainty; methods for measuring, modeling, and communicating uncertainty; practical guidelines for capturing and representing expert knowledge and judgment; explanations of the role of uncertainty in decision-making; a guideline to avoiding logical fallacies when dealing with uncertainty; and several example cases of real-world ecological modeling and risk analysis to illustrate the concepts and approaches. Case topics provide examples of structured decision-making, statistical modeling, and related topics. A summary provides practical next steps that the reader can take in analyzing and interpreting uncertainty in real-world situations. Also provided is a glossary and a suite of references.
As I write, the financial systems of the world are collapsing with still no clear indication of what the consequences will be and which measures should be taken to avoid such a crisis in the future. There seems to be agreement though, that the financial instruments introduced in the past few decades entailed far too much complexity and uncertainty and that there was too little regulatory control over the use of these instruments. Management of uncertainty with the aim of achieving self-control is the core concern of this book. It was not written with a focus on financial systems, but many concepts developed in this book are applicable to this field as well. The - neric principles of reducing, maintaining or increasing uncertainties in view of the different contingencies an organization is faced with, the fundamental issue of how much control is possible and who should be in control, and the question of how much and what kind of regulation is necessary with the overall aim of finding an appropriate balance between system stability and flexibility are at the centre of heated debates on the future of finance.
Dealing effectively with uncertainty requires today's project manager to be familiar with a broad spectrum of strategies, encompassing both 'hard' and 'soft' methods. This theme of unified thinking (i.e. the need to selectively draw upon a wide range of strategies in any given situation) will differentiate the book from its contemporaries. By picking up where traditional risk management techniques begin to fail, it brings together leading-edge thinking from a variety of disciplines and shows how these techniques can be used to conquer uncertainty in projects. The ability to make good decisions when faced with uncertainty is the real challenge. It is a universal truth that a decision is only as good as the information it is based on. But good information is often hard to come by, and all projects are vulnerable to the unknown and the unknowable. Thus, uncertainty becomes the sworn enemy of the project manager. Wherever we try to analyse, quantify, plan and act, uncertainty lies in wait to surprise us with its ambiguity and unpredictability. It lurks in every stage of the project lifecycle: in the planning (how long will this really take?), the initiation (this isn't the situation I expected!), the execution (who could have foreseen that happening?), and even the completion of a project (where are the expected benefits?). But managing uncertainty is a lot more than just applying risk management techniques. It requires a deep appreciation of how uncertainty arises and, by recognising its different guises, the appropriate strategies can be formulated. If we can learn how to reduce uncertainty, we can make better management decisions and increase the chances of the project succeeding. This book addresses five key questions: ¢ Why is there uncertainty in projects? ¢ How do you spot the symptoms of uncertainty, preferably at an early stage? ¢ What can be done to avoid uncertainty? ¢ What strategies can be used to deal with project uncertainty? ¢ How can both the individual and the organisation learn to cope more effectively in the future? The reader is assumed to be a either a project management professional, or a senior manager looking for ways to improve project management strategy within their organisation. As such, a foundation in project management basics is assumed, although not essential. The book then builds on this by exposing new ideas and concepts, and shows how these can be harnessed to tackle uncertainty in its many guises.
This book explains the notational system NUSAP (Numeral, Unit, Spread, Assessment, Pedigree) and applies it to several examples from the environmental sciences. The authors are now making further extensions of NUSAP, including an algorithm for the propagation of quality-grades through models used in risk and safety studies. They are also developing the concept of `Post-normal Science', in which quality assurance of information requires the participation of `extended peer-communities' lying outside the traditional expertise.
In this provocative book, Paul Glimcher argues that economic theory may provide an alternative to the classical Cartesian model of the brain and behavior. Glimcher argues that Cartesian dualism operates from the false premise that the reflex is able to describe behavior in the real world that animals inhabit. A mathematically rich cognitive theory, he claims, could solve the most difficult problems that any environment could present, eliminating the need for dualism by eliminating the need for a reflex theory. Such a mathematically rigorous description of the neural processes that connect sensation and action, he explains, will have its roots in microeconomic theory. Economic theory allows physiologists to define both the optimal course of action that an animal might select and a mathematical route by which that optimal solution can be derived. Glimcher outlines what an economics-based cognitive model might look like and how one would begin to test it empirically. Along the way, he presents a fascinating history of neuroscience. He also discusses related questions about determinism, free will, and the stochastic nature of complex behavior.
This book provides a new point of view on the subject of the management of uncertainty. It covers a wide variety of both theoretical and practical issues involving the analysis and management of uncertainty in the fields of finance, management and marketing. Audience: Researchers and professionals from operations research, management science and economics.
This investigative analysis studies why key European countries responded differently to the Chernobyl nuclear disaster, and what can be learned from it. The author details why the accident was defined differently in various countries, why actions were or were not taken, and what was learned about the management of nuclear risk. Furthermore, Liberatore studies the short-term and long-term responses and consequences of Chernobyl not only in specific countries, but within the European Union as a whole. Liberatore also provides a policy communication model to illustrate the interaction among the key personnel in such incidents: the scientists, the politicians, the interest groups, and the mass media. The author's focus upon uncertainty managementis a compelling account for all who seek to understand and improve the practical management of transboundary risks.
Fuzzy Logic for the Management of Uncertainty covers many important topics, including:" "Developments in mathematics that have paved the road for fuzzy logic;" "Deep, and of a broad perspective, exposition of virtually all approaches used in contemporary science for the representation and handling of imperfect (uncertain, imprecise, vague, ambiguous, etc.) information;" "Coverage of practically all relevant and promising directions and approaches in fuzzy logic research including LT--fuzzy logic, model theoretic approaches, intuitionistic fuzzy logic, nonmonotonic fuzzy logic, modifier fuzzy logic;" "VLSI fuzzy logic-based chips that have triggered the implementation of fuzzy logic in so many fields of science and technology;" "A broad coverage of fuzzy logic in approximate reasoning, including basic issues related to the role of fuzzy logic for approximate reasoning, analyses of various definitions of fuzzy implication that is a crucial element in fuzzy logic-based reasoning schemes,
As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view points of information systems experts on the needs that challenge the uncer tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.
Decision Making Under Uncertainty in Electricity Markets provides models and procedures to be used by electricity market agents to make informed decisions under uncertainty. These procedures rely on well established stochastic programming models, which make them efficient and robust. Particularly, these techniques allow electricity producers to derive offering strategies for the pool and contracting decisions in the futures market. Retailers use these techniques to derive selling prices to clients and energy procurement strategies through the pool, the futures market and bilateral contracting. Using the proposed models, consumers can derive the best energy procurement strategies using the available trading floors. The market operator can use the techniques proposed in this book to clear simultaneously energy and reserve markets promoting efficiency and equity. The techniques described in this book are of interest for professionals working on energy markets, and for graduate students in power engineering, applied mathematics, applied economics, and operations research.