Wonderful Solutions and Habitual Domains for Challenging Problems in Changeable Spaces

Beschreibung

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.
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.
Introduces a new decision theory, Optimization in Changeable Spaces Illustrates how to expand competences, handle environmental, psychological and behavioral aspects and their dynamics when solving challenging decision problems Includes perspectives related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery and knowledge extraction Relevant to academics, managers and policy makers Includes supplementary material: sn.pub/extras

Autor*in

Moussa Larbani

Themen in »Wonderful Solutions and Habitual Domains for Challenging Problems in Changeable Spaces«

7-8-9 Principles of deep knowledge for HD expansion Competence Set Analysis Competence Set Analysis Covering and discovering processes in decision making Decision Blinds in Competence Set Analysis Decision Making Problems in Changeable Spaces Decision Traps Decision making and optimization in changeable spaces Discovering in Competence Set Analysis and Habitual Domain Expansion of Habitual Domains Habitual Domain Innovation Dynamics decision making and problem solving optimization in changeable spaces

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“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)

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