Wonderful Solutions and Habitual Domains for Challenging Problems in Changeable Spaces [electronic resource] : From Theoretical Framework to Applications / by Moussa Larbani, Po-Lung Yu.

За: Інтелектуальна відповідальність: Вид матеріалу: Текст Публікація: Singapore : Springer Singapore : Imprint: Springer, 2017Видання: 1st ed. 2017Опис: XXVII, 275 p. 17 illus. online resourceТип вмісту:
  • text
Тип засобу:
  • computer
Тип носія:
  • online resource
ISBN:
  • 9789811019814
Тематика(и): Додаткові фізичні формати: Printed edition:: Немає назви; Printed edition:: Немає назви; Printed edition:: Немає назвиДесяткова класифікація Дьюї:
  • 658.40301 23
Класифікація Бібліотеки Конгресу:
  • HD30.23
Електронне місцезнаходження та доступ:
Вміст:
Chapter 1: Challenging Decision Problems and Decision Models -- Chapter 2: Decision Processes and Decision Making in Changeable Spaces -- Chapter 3: Habitual Domains, Human Behaviour Mechanism and Wonderful Solutions for DMCS Problem Analysis -- Chapter 4: Expansion of Habitual Domains and DMCS -- Chapter 5: Competence Set Analysis, Decision Blinds and Decision-Making -- Chapter 6: Decision-Making in Changeable Spaces (DMCS): A New Paradigm -- Chapter 7: Solving Real-World DMCS Problems - Part 1: Management and Economics Problems -- Chapter 8. Solving Real World DMCS Problems - Part 2: Social, Geopolitical and Discovery Problems -- Chapter 9: Innovation Dynamics as a DMCS Problem -- Chapter 10: Conclusion and Further Research.
У: Springer eBooksЗведення: 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.
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Chapter 1: Challenging Decision Problems and Decision Models -- Chapter 2: Decision Processes and Decision Making in Changeable Spaces -- Chapter 3: Habitual Domains, Human Behaviour Mechanism and Wonderful Solutions for DMCS Problem Analysis -- Chapter 4: Expansion of Habitual Domains and DMCS -- Chapter 5: Competence Set Analysis, Decision Blinds and Decision-Making -- Chapter 6: Decision-Making in Changeable Spaces (DMCS): A New Paradigm -- Chapter 7: Solving Real-World DMCS Problems - Part 1: Management and Economics Problems -- Chapter 8. Solving Real World DMCS Problems - Part 2: Social, Geopolitical and Discovery Problems -- Chapter 9: Innovation Dynamics as a DMCS Problem -- Chapter 10: Conclusion and Further Research.

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.

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