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.

Managing uncertainty has become a new business imperative. Technological discontinuities, regulatory upheavals, geopolitical shocks, abrupt shifts in consumer tastes or behavior, and many other factors have emerged or intensified in recent years and together conspire to undermine even the most carefully constructed business strategies. Managing Uncertainty: Strategies for Surviving and Thriving in Turbulent Times addresses these new challenges, assessing the sources of business turbulence, how to classify uncertainty, and the different ways in which uncertainty can be embraced to allow greater innovation and growth. Drawing on examples from around the world, the book presents the most recent ideas on what it means to manage uncertainty, from practitioners, academics, and consultants. Addresses the challenges of managing uncertainty in business Presents a step-by-step guide to managing business uncertainty Draws examples from major international companies, including Intel, Procter & Gamble, Siemens, Boeing, Quinetiq, Philips, China Telecom, Ford, Apple, Shell, Glaxo SmithKline and many more Written for business leaders and managers looking for new ways to ensure that their businesses continue to thrive in a world of increasing complexity, Managing Uncertainty presents new and innovative ideas about reducing risk by understanding difficult-to-predict shifts.

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 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.

This book offers a framework for making decisions under risk and uncertainty. Synthesizing research from economics, finance, decision theory, management, and other fields, the book provides a set of tools and a way of thinking that determines the relative merits of different strategies. It takes as its premise that we make better decisions if we use the whole toolkit of economics and related fields to inform our decision making. The text explores the distinction between risk and uncertainty and covers standard models of decision making under risk as well as more recent work on decision making.

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.

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.

The reality of everyday organizational life is that it is filled with uncertainty, contradictions and paradoxes. Yet leaders and managers are expected to act as though they can predict the future and bring about the impossible: that they can transform themselves and their colleagues, design different cultures, choose the values for their organization, be innovative, control conflict and have inspiring visions. Whilst managers will have had lots of experiences of being in charge, they probably realise that they are not always in control. So how might we frame a much more realistic account of what’s possible for managers to achieve? Many managers are implicitly aware of their messy reality, but they rarely spend much time reflecting on what it is that they are actually doing. Drawing on insights from the complexity sciences, process sociology and pragmatic philosophy, Chris Mowles engages directly with some principal contradictions of organizational life concerning innovation, culture change, conflict and leadership. Mowles argues that if managers proceed from the expectation that organizational life as inherently uncertain, and interactions between people are complex and often paradoxical, they start noticing different things and create possibilities for acting in different ways. Managing in Uncertainty will be of interest to practitioners, advanced students and researchers looking at management and organizational studies from a critical perspective.

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,

Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. Authored by a leading European network of experts representing a cross section of industries, Uncertainty in Industrial Practice aims to provide a reference for the dissemination of uncertainty treatment in any type of industry. It is concerned with the quantification of uncertainties in the presence of data, model(s) and knowledge about the system, and offers a technical contribution to decision-making processes whilst acknowledging industrial constraints. The approach presented can be applied to a range of different business contexts, from research or early design through to certification or in-service processes. The authors aim to foster optimal trade-offs between literature-referenced methodologies and the simplified approaches often inevitable in practice, owing to data, time or budget limitations of technical decision-makers. Uncertainty in Industrial Practice: Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework. Presents methods for organizing and treating uncertainties in a generic and prioritized perspective. Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints. Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods. Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries. This book provides a guide to dealing with quantitative uncertainty in engineering and modelling and is aimed at practitioners, including risk-industry regulators and academics wishing to develop industry-realistic methodologies.