A Theory Of Uncertainty

This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. For this new edition the entire text has been totally rewritten. The chapters on chance theory and uncertainty theory are completely new. Mathematicians, researchers, engineers, designers, and students will find this work a stimulating and useful reference.

Three-time recipient of the AJN Book of the Year Award! Praise for the third edition: “This is an outstanding edition of this book. It has great relevance for learning about, developing, and using middle range theories. It is very user friendly, yet scholarly." Score: 90, 4 Stars -Doody's Medical Reviews The fourth edition of this invaluable publication on middle range theory in nursing reflects the most current theoretical advances in the field. With two additional chapters, new content incorporates exemplars that bridge middle range theory to advanced nursing practice and research. Additional content for DNP and PhD programs includes two new theories: Bureaucratic Caring and Self-Care of Chronic Illness. This user-friendly text stresses how theory informs practice and research in the everyday world of nursing. Divided into four sections, content sets the stage for understanding middle range theory by elaborating on disciplinary perspectives, an organizing framework, and evaluation of the theory. Middle Range Theory for Nursing, Fourth Edition presents a broad spectrum of 13 middle range theories. Each theory is broken down into its purpose, development, and conceptual underpinnings, and includes a model demonstrating the relationships among the concepts, and the use of the theory in research and practice. In addition, concept building for research through the lens of middle range theory is presented as a rigorous 10-phase process that moves from a practice story to a conceptual foundation. Exemplars are presented clarifying both the concept building process and the use of conceptual structures in research design. This new edition remains an essential text for advanced practice, theory, and research courses. New to the Fourth Edition: Reflects new theoretical advances Two completely new chapters New content for DNP and PhD programs Two new theories: Bureaucratic Caring and Self-Care of Chronic Illness Two articles from Advances in Nursing Science documenting a historical meta-perspective on middle range theory development Key Features: Provides a strong contextual foundation for understanding middle range theory Introduces the Ladder of Abstraction to clarify the range of nursing’s theoretical foundation Presents 13 middle range theories with philosophical, conceptual, and empirical dimensions of each theory Includes Appendix summarizing middle range theories from 1988 to 2016

Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, countable subadditivity, and product measure axioms. Uncertainty is any concept that satisfies the axioms of uncertainty theory. Thus uncertainty is neither randomness nor fuzziness. It is also known from some surveys that a lot of phenomena do behave like uncertainty. How do we model uncertainty? How do we use uncertainty theory? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory, including uncertain programming, uncertain risk analysis, uncertain reliability analysis, uncertain process, uncertain calculus, uncertain differential equation, uncertain logic, uncertain entailment, and uncertain inference. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.

This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions - first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

This book provides tools for making decisions in an environment of uncertainty. In Chapter 1 the author explains the most important aspects of the concept of relation. From this start arise the other three concepts that cover practically all processes from which decisions stem. These three concepts are: attribution from which the concept of assignment arises; and grouping, which includes the concept of an original function. The techniques presented, as well as the models and algorithms developed, constitute an invaluable aid for those who must make decisions. Audience: Researchers and graduate students interested in mathematics applied to economics and management.

A timeless classic of economic theory that remains fascinating and pertinent today, this is Frank Knight's famous explanation of why perfect competition cannot eliminate profits, the important differences between "risk" and "uncertainty," and the vital role of the entrepreneur in profitmaking. Based on Knight's PhD dissertation, this 1921 work, balancing theory with fact to come to stunning insights, is a distinct pleasure to read. FRANK H. KNIGHT (1885-1972) is considered by some the greatest American scholar of economics of the 20th century. An economics professor at the University of Chicago from 1927 until 1955, he was one of the founders of the Chicago school of economics, which influenced Milton Friedman and George Stigler.

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.