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E-grāmata: Wonderful Solutions and Habitual Domains for Challenging Problems in Changeable Spaces: From Theoretical Framework to Applications

  • Formāts: EPUB+DRM
  • Izdošanas datums: 24-Aug-2016
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811019814
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  • Formāts: EPUB+DRM
  • Izdošanas datums: 24-Aug-2016
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811019814

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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.

Recenzijas

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)

1 Challenging Decision Problems and Decision Models
1(24)
1.1 Introduction
1(1)
1.2 Existing Decision-Making Models and Their Limitations
2(9)
1.2.1 Traditional Decision Models
2(4)
1.2.2 The Cognitive Decision Models
6(1)
1.2.3 Some Problems Unsolvable by Existing Decision Models
6(5)
1.3 An Informal Introduction to Decision-Making in Changeable Spaces
11(2)
1.4 Conclusion
13(12)
Appendix 1 A Snapshot on Decision-Making Models
14(6)
Appendix 2 Discussion of Schelling's Game of Chicken Model of Problem 1.8 (Cuban Missile Crisis)
20(2)
References
22(3)
2 Decision Processes and Decision-Making in Changeable Spaces
25(26)
2.1 Introduction
25(5)
2.1.1 A Brief Introduction to the Ten Decision Parameters
26(4)
2.2 Classification of Decision Problems
30(2)
2.3 Decision Elements and Decision Environmental Facets
32(12)
2.3.1 Decision Elements
32(6)
2.3.2 Decision Environmental Facets
38(6)
2.4 Decision-Making in Changeable Spaces Problems
44(5)
2.5 Conclusion
49(2)
References
50(1)
3 Habitual Domains, Human Behaviour Mechanism and Wonderful Solutions for DMCS Problem Analysis
51(38)
3.1 Introduction
51(1)
3.2 Habitual Domains
52(2)
3.3 The Eight HD Hypotheses H1---H8 in Behaviour Dynamics
54(12)
3.3.1 Circuit Pattern Hypothesis, H1
54(1)
3.3.2 Unlimited Capacity Hypothesis, H2
55(1)
3.3.3 Efficient Restructuring Hypothesis, H3
55(1)
3.3.4 Analogy/Association Hypothesis, H4
56(1)
3.3.5 Goal Setting and State Evaluation Hypothesis, H5
57(3)
3.3.6 Charge Structure and Attention Allocation Hypothesis, H6
60(3)
3.3.7 Discharge Hypothesis, H7
63(2)
3.3.8 Information Input Hypothesis, H8
65(1)
3.4 Human Behaviour Mechanism and Decision-Making
66(5)
3.4.1 Stability of Habitual Domains
69(2)
3.5 HD Model of DMCS Problems and Acceptable and Wonderful Solutions
71(10)
3.5.1 Acceptable Solutions and Wonderful Solutions
74(4)
3.5.2 Comparing DMCS Habitual Domain Model with Existing Decision Models
78(3)
3.6 Behavioural Tendencies
81(6)
3.7 Conclusion
87(2)
References
88(1)
4 Expansion of Habitual Domains and DMCS
89(34)
4.1 Introduction
89(2)
4.2 Degrees of HD Expansion
91(4)
4.2.1 Zero-Degree Expansion
91(1)
4.2.2 First-Degree Expansion
92(1)
4.2.3 Second-Degree Expansion
93(2)
4.3 The 7-8-9 Principles of Deep Knowledge for HD Expansion
95(17)
4.3.1 Seven Empowerment Operators
96(5)
4.3.2 Eight Basic Methods for Expanding HD
101(4)
4.3.3 Nine Principles for Deep Knowledge
105(7)
4.4 Procedure for Solving DMCS Problems
112(8)
4.5 Conclusion
120(3)
References
120(3)
5 Competence Set Analysis, Decision Blinds and Decision-Making----
123(24)
5.1 Introduction
123(3)
5.2 Cores of Habitual Domains
126(2)
5.3 Learning Process
128(2)
5.3.1 Implanting
128(1)
5.3.2 Nurturing
129(1)
5.3.3 Habituating
130(1)
5.4 Competence Sets and Classes of Decision Problems
130(6)
5.4.1 Routine Problem
131(1)
5.4.2 Mixed Routine Problems
132(1)
5.4.3 Fuzzy Problems
132(2)
5.4.4 Challenging Problems
134(2)
5.5 Confidence, Risk Taking and Ignorance
136(3)
5.6 Effective Decision-Making
139(1)
5.7 Decision Blinds and Decision Traps
140(1)
5.8 Covering and Discovering Problems
141(3)
5.9 Support in Decision-Making Process
144(1)
5.10 Conclusion
145(2)
References
145(2)
6 Decision-Making in Changeable Spaces (DMCS): A New Paradigm
147(36)
6.1 Introduction
147(1)
6.2 Optimisation in Changeable Spaces
148(4)
6.3 Covering Problem
152(3)
6.3.1 Feasibility and Covering Time and/or Cost
154(1)
6.4 Discovering
155(3)
6.5 Necessary and Sufficient Conditions for Covering
158(3)
6.5.1 Cardinality Approach to Covering
158(3)
6.6 General Procedures for Solving Covering and Discovering Problems
161(13)
6.6.1 General Covering Problem Procedure
164(7)
6.6.2 Covering Feasibility Procedure
171(1)
6.6.3 Covering Time Procedure
172(1)
6.6.4 Discovering Procedure
173(1)
6.7 Application
174(6)
6.8 A Comparison Between OCS Models and Existing Decision Models
180(1)
6.9 Conclusion
181(2)
References
182(1)
7 Solving Real-World DMCS Problems, Part 1: Management and Economics Problems
183(26)
7.1 Introduction
183(1)
7.2 Management Applications
184(17)
7.2.1 From Business Crisis to Prosperity by Matsushita
185(6)
7.2.2 The 1984 Olympic Games, Converting Potential Big Loss to Big Gains
191(4)
7.2.3 From Tough Competition to Supply Chain Integration by Synnex
195(4)
7.2.4 Converting Revenge Sentiment to Full Cooperation
199(2)
7.3 Economics Application
201(7)
7.4 Conclusion
208(1)
References
208(1)
8 Solving Real-World DMCS Problems, Part 2: Social, Geopolitical, and Discovery Problems
209(32)
8.1 Introduction
209(1)
8.2 Silence Game Between Husband and Wife
210(4)
8.3 Clearing a Violent Demonstration Peacefully
214(3)
8.4 The Farmer and the Hunter
217(4)
8.5 A Winning Strategy Without Implementation
221(3)
8.6 Cuban Missile Crisis (1962)
224(11)
8.7 Multilanguage Script Keyboard
235(3)
8.8 Conclusion
238(3)
References
239(2)
9 Innovation Dynamics as a DMCS Problem
241(16)
9.1 Introduction
241(1)
9.2 Innovation from Habitual Domain Perspective
242(1)
9.3 An Anatomy of Innovation Dynamics
243(6)
9.3.1 Competence Set Expansion and Transformation (see Fig. 9.1(i) and (C))
244(1)
9.3.2 Providing Product/Service to Release the Pain and Frustration of Targeted Customers (see Fig. 9.1(ii)--(iii) and (B))
245(1)
9.3.3 Creating Charge and Releasing Charge (see Fig. 9.1(iv))
246(1)
9.3.4 Creating Value (see Fig. 9.1(v) and (A))
247(1)
9.3.5 Value Distribution and Reinvestment (see Fig. 9.1(vi) and (D)--(E))
247(1)
9.3.6 Clockwise and Counterclockwise Versions of Innovation Dynamics
248(1)
9.4 DMCS Problems in Innovation Dynamics
249(5)
9.4.1 OCS and Innovation Dynamics
251(3)
9.5 Conclusion
254(3)
References
255(2)
10 Conclusion and Further Research
257(14)
10.1 Introduction
257(1)
10.2 DMCS and OCS in Management and Game Situations
258(1)
10.3 DMCS and OCS in Artificial Intelligence
259(2)
10.3.1 HD-Agent in Artificial Economics and e-Economy
260(1)
10.4 Scientific Discovery
261(1)
10.5 Knowledge Extraction
262(1)
10.6 Competence Set-Related Research Problems
262(9)
References
264(7)
Index 271
Moussa Larbani is a professor at the International Islamic University Malaysia (IIUM). He earned his bachelors degree in mathematics, majoring in operations research from USTHB University, Algiers, Algeria in 1985, and his Ph.D. in ordinary differential equations and optimal control from Odessa State University, Ukraine in 1991. He has been Associate Professor at UMMTO University, Tizi-Ouzou, Algeria from 1991-2001. He then joined IIUM University in Kuala Lumpur, Malaysia until 2006. He spent one year at Kainan University in Taiwan and re-joined IIUM University in Kuala Lumpur, Malaysia in 2007. He has been a Professor at IIUM University since 2010. His current research interests include decision making, Habitual Domain theory, fuzzy game theory, supply chain management and multiple criteria problems involving uncertainty. Dr. Larbani has made a significant contribution to the theory of second order games and the theory of decision making in changeable spaces with Prof. P.L. Yu. Moreover, he has published more than 50 articles over multiple objective programming, fuzzy games, differential equations, supply chain management, data mining and finance.

Po-Lung Yu, Distinguished Professor (Emeritus) of University of Kansas (KU), Kansas, and Distinguished Professor for Life of National Chiao-Tung University (NCTU), Taiwan, was raised in Taiwan, further educated and trained in USA. He earned BAInternational Trade (1963) from National Taiwan University, and Ph.D.Operations Research and Industrial Engineering (1969) from the Johns Hopkins University. From 1977 to 2004, Dr. Yu held an endowed Chair as the Carl A. Scupin Distinguished Professor of the University of Kansas. He taught at NCTU from 1999 to 2011. Previously he taught at the University of Rochester (1969-73) and the University of Texas at Austin (1973-77). He won awards for outstanding research and for teaching. Dr. Yu, the initiator of Habitual Domains theory, competence set analysis and second order games, has published, in English and Chinese, 22 books and over 180 professional articles over multiple criteria decision making, mathematical programming, differential games and optimal control theory, and various application problems including investment models, efficient market, marketing, automobile safety and energy policy, corporate acquisition and merger analysis, aside from what he initiated. Dr. Yu is recognized internationally as a remarkable thinker, scholar, teacher and advisor. He has given many keynote addresses around the world, academically and publicly. His audiences of Habitual Domains and related topics, sometimes exceeded thousands of people, include professors, students, corporate executives, ministers, military generals, monks, nuns, house wives, jail prisoners, etc.