Models Of Decision Making
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EBOOK: Analytical Models for Decision-Making
Author: Colin Sanderson
language: en
Publisher: McGraw-Hill Education (UK)
Release Date: 2006-03-16
Health care systems are complex and, as a result, it is often unclear what the effects of changes in policy or service provision might be. At the same time, resources for health care tend to be in short supply, which means that public health practitioners have to make difficult decisions. This book describes the quantitative and qualitative methods that can help decision-makers to structure and clarify difficult problems and to explore the implications of pursuing different options. The accompanying CD ROM provides the opportunity to try out some of the proposed solutions. The book examines: Models and decision-making in health care Methods for clarifying complex decisions Models for service planning and resource allocation Modelling for evaluating changes in systems Series Editors: Rosalind Plowman and Nicki Thorogood.
Decision-Making Models
Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems, optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision making mechanisms realized under uncertainty. - Introduces mathematics of intelligent systems which provides the usage of mathematical rigor such as precise definitions, theorems, results, and proofs - Provides extended and new comprehensive methods which can be used efficiently in a fuzzy environment as well as optimization problems and related fields - Covers applications and elaborates on the usage of the developed methodology in various fields of industry such as software technologies, biomedicine, image processing, and communications
Multiperson Decision Making Models Using Fuzzy Sets and Possibility Theory
Author: J. Kacprzyk
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Decision making is certainly a very crucial component of many human activities. It is, therefore, not surprising that models of decisions play a very important role not only in decision theory but also in areas such as operations Research, Management science, social Psychology etc . . The basic model of a decision in classical normative decision theory has very little in common with real decision making: It portrays a decision as a clear-cut act of choice, performed by one individual decision maker and in which states of nature, possible actions, results and preferences are well and crisply defined. The only compo nent in which uncertainty is permitted is the occurence of the different states of nature, for which probabilistic descriptions are allowed. These probabilities are generally assumed to be known numerically, i. e. as single probabili ties or as probability distribution functions. Extensions of this basic model can primarily be conceived in three directions: 1. Rather than a single decision maker there are several decision makers involved. This has lead to the areas of game theory, team theory and group decision theory. 2. The preference or utility function is not single valued but rather vector valued. This extension is considered in multiattribute utility theory and in multicritieria analysis. 3.