This book introduces a new paradigm called `Optimization in Changeable Spaces' (OCS) as a useful tool for decision making and problem solving. It illustrates how OCS incorporates, searches, and constructively restructures the parameters, tangible and intangible, involved in the process of decision making. The book elaborates on OCS problems that can be modeled and solved effectively by using the concepts of competence set analysis, Habitual Domain (HD) and the mental operators called the 7-8-9 principles of deep knowledge of HD. In addition, new concepts of covering and discovering processes are proposed and formulated as mathematical tools to solve OCS problems. The book also includes reformulations of a number of illustrative real-life challenging problems that cannot be solved by traditional optimization techniques into OCS problems, and details how they can be addressed. Beyond that, it also includes perspectives related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery and knowledge extraction. This book will be of interest to managers of businesses and institutions, policy makers, and educators and students of decision making and behavior in DBA and/or MBA.
Publisher: Springer Verlag, Singapore
Number of pages: 275
Weight: 5797 g
Dimensions: 235 x 155 x 18 mm
Edition: 1st ed. 2017
"The book serves as a corrective to purely quantitative approaches to decision making and problem solving, and it will interest both those studying decision making in the abstract and those, such as business leaders and engineers, who make decisions every day. The book will enable both groups to understand better the complexities of decision making and to expand their problem-solving skill sets." (Computing Reviews, May, 2017)