Systems Research II: Essays in Honor of Yasuhiko Takahara on Systems Management Theory and Practice (Translational Systems Sciences, 27)

Preface
Contents
Part I Social/Organizational Theory and Application
	1 Complex Systems and Postmodernism: A New Perspective for Society in the Twenty-First Century
		1.1 From Order Theory to Chaos Theory of the World
			1.1.1 A Copernican Revolution
			1.1.2 Edge of Chaos and Self-Organization
		1.2 Science of Complex Systems: Convergence of Various Chaos Theories
		1.3 Postmodernism or “Edge of Chaos” in Civilization
			1.3.1 Difference and Rhizome
			1.3.2 Rhizome as a Principle of Social Formation
			1.3.3 Lack of the Transparency of the Representation
			1.3.4 Dedifferentiation of the Functionalist Society
		1.4 Self-Organization from the Edge of Chaos
		References
	2 A Retrospective on the University –Industry Innovation Nexus in Japan: Empirical Assessment of Coauthorship in the Light of New Data
		2.1 Introduction
		2.2 Why the University–Industry Innovation Nexus?
			2.2.1 Global Context
			2.2.2 Personal Context
		2.3 University–Industry Coauthorship Linkage in Japan, 1981–1996
			2.3.1 Research Context
			2.3.2 Conventional Wisdom of University–Industry Disconnect in Japan
			2.3.3 Questioning the Conventional Wisdom of Disconnect
			2.3.4 Measuring the University–Industry Linkage
			2.3.5 Main Findings of the 1981–1996 Dataset
			2.3.6 Skeptical Reaction
			2.3.7 Unpursued Implications of Earlier Research
		2.4 Post-1996 University–Industry Coauthorship Linkage in Japan
			2.4.1 Research Study of Interest, 1981–2004
			2.4.2 Main Findings of the 1981–2004 Dataset
			2.4.3 Implications of the New Study on Our Earlier Study
		2.5 Concluding and Looking Forward
		References
	3 Consideration of Organization Model Based on Dynamic Equilibrium Theory
		3.1 Introduction
		3.2 Concept of Dynamic Equilibrium
		3.3 Organizational Model of Dynamic Equilibrium
			3.3.1 People
			3.3.2 Organization
			3.3.3 Business Process
		3.4 Dynamism of Dynamic Equilibrium
		3.5 Characteristics of Dynamism of Dynamic Equilibrium
		3.6 Strategic Dynamic Equilibrium
		3.7 Conclusion
		References
	4 A Process for a Conceptual Design and Its Simulation Toward New Business Model Creation
		4.1 Introduction
		4.2 Conceptual Design Process for New Business Models
			4.2.1 SWOT Analysis
			4.2.2 Formulating a Policy
			4.2.3 Applying AF
			4.2.4 Identifying Interactions Between Multiple BMPs
		4.3 Verification Process for New Business Models
			4.3.1 Description of the Current Business Model
			4.3.2 Reflecting the Conceptual Design
			4.3.3 Describe the New Business Model
				4.3.3.1 SD Model when Applying Self-Service Pattern
				4.3.3.2 SD Model When Applying Add-on Pattern
			4.3.4 Reflecting the Interaction between BMPs
				4.3.4.1 When an Interaction Acts on an Existing Flow
				4.3.4.2 When an Interaction Creates a New Flow between Stocks
			4.3.5 Running the Simulation
			4.3.6 Running SD Simulation with Monte Carlo Simulation
		4.4 Future Issues
			4.4.1 Conversion from Conceptual Design Results on BMC to SD Model
			4.4.2 Representation of the Interaction between BMPs in the SD Model
			4.4.3 Introduction of Blocks and Layers for Building SD Models and Providing Views
			4.4.4 Unify Templates and Extend Processes to Other BMPs
		4.5 Related Research
		4.6 Summary
		References
	5 Virtual Organization, Organizational Intelligence, and Imperfect Information Processing
		5.1 Imperfect Information and Organizational Processing
			5.1.1 System Recognition and Meta System
			5.1.2 Genesis, Oracle, and Prediction
			5.1.3 Imperfect Information Processing
			5.1.4 Transforming Imperfect Information
			5.1.5 Invariant Imperfect Information
		5.2 Middle Level Autonomous Unit in Hierarchical System
			5.2.1 Middle Level Autonomous Unit
			5.2.2 Arising MLAUs
			5.2.3 Simulating Transformation of Organization
			5.2.4 Proposition of MLAU and Global Optimality
		5.3 Organizational Intelligence
			5.3.1 What Is Organizational Intelligence?
			5.3.2 Virtual Organization and Organizational Intelligence
			5.3.3 Composite Model and Organizational Intelligence
		5.4 Geometry of Organizational Structure
			5.4.1 Transformation Between HS and DCAS
			5.4.2 Growth of Technology and Transformation
			5.4.3 Optimal, Efficient, and Advantage Process
			5.4.4 Computer System
			5.4.5 Political System
			5.4.6 Production System
			5.4.7 Power and Generating Organizational Structure
			5.4.8 City and Integration
		5.5 Conclusion
		References
	6 Composite: A Model of Virtual Organization
		6.1 Introduction
			6.1.1 System Recognition
			6.1.2 Geometry and Structure
			6.1.3 Hierarchy and Stratification
			6.1.4 Complex System and Chaos
			6.1.5 Hierarchy and Complexity
			6.1.6 Vertical Section of Hierarchical Structure and Comprehensive Recognition
			6.1.7 After Complexity and Chaos
		6.2 Composite
			6.2.1 Problems
			6.2.2 Composite: A Model of Virtual Organization
			6.2.3 Frame of Composite
			6.2.4 Local Rule Set
			6.2.5 Decentralized Autonomous Unit and Scope
			6.2.6 Local Scope and Highly Global Scope
			6.2.7 Composing Process and Stratification
			6.2.8 Recursive Operation and Composing
			6.2.9 Higher Stratification and Its Number of Levels
			6.2.10 Behavior of Composite
			6.2.11 Optimal Structure in Stratification
		6.3 A Model of Complexity
			6.3.1 Geometry of Structure and Complexity
			6.3.2 Hierarchical Structure Breaking
			6.3.3 Consideration of Complexity
			6.3.4 Composite and Complexity
			6.3.5 Generation from Complex and Chaos
			6.3.6 Paradox of Complexity
		6.4 A Model of Prediction
			6.4.1 Imperfect Information and Prediction
			6.4.2 Prediction and Bifurcation
			6.4.3 Bifurcation and Its Characteristics
			6.4.4 Bifurcation and Composite
			6.4.5 Bifurcation on Top of Composite
			6.4.6 Bifurcation and Complexity
			6.4.7 Uncertainty in Composite
			6.4.8 Complexity and Self-Organization
			6.4.9 Artificial Intelligence and Imperfect Information
		6.5 Conclusion and Developments
		References
Part II Systems Management
	7 What Should Be Added to Science for Solving Wicked Problems?
		7.1 Introduction
			7.1.1 Issue Extraction
			7.1.2 Function Identification
			7.1.3 Construction Design
			7.1.4 Artificial System Development
			7.1.5 Provision to the Market or Society
		7.2 What Are Wicked Problems?
		7.3 Can Science Be Directly Applicable to Solve Wicked Problems?
			7.3.1 Science
			7.3.2 Limitation When We Apply Science for Wicked Problems
		7.4 General Systems Theory and Emergent Property
		7.5 Cybernetics
		7.6 Structural Similarity
		7.7 Business Dynamics
		7.8 Soft Systems Methodology
		7.9 Design Thinking and Design Management
		7.10 Conclusions
		References
	8 Methodology for Refining Concept Through Refutation in Theory of Organizational Strategy
		8.1 Introduction
		8.2 Common Methodology of Natural and Social Sciences
		8.3 Logical Structure of Development of Theory of Strategy
			8.3.1 Universal Proposition and Specific Proposition
			8.3.2 Role of Business Cases in Theory of Strategy as Social Science
		8.4 Finding Possible Characteristics for Necessary and Sufficient Conditions
		8.5 Conclusion
		References
	9 PVaR: A New Risk Measure for Financial Investments
		9.1 Risk Measures for Financial Investments
		9.2 Notion and Formulation of Period Value at Risk
		9.3 Calculation of PVaR
		9.4 Estimation of PVaR Using the Historical Simulation Method
		9.5 Estimation of PVaR Using the Monte Carlo Simulation Method
		9.6 PVaR Estimation Experiments with MC Simulation Method
			9.6.1 Estimation of PVaR with Monte Carlo Simulation
			9.6.2 Least Number of Simulations for Getting a Qualified Estimation of PVaR
			9.6.3 Comparison of Market Risk Measured in PVaR with that in VaR
		9.7 Conclusions and Remarks
		References
	10 An Agent-Based Approach to Stability of Complete, Directed, and Signed Social Networks with Loops
		10.1 Introduction
		10.2 Model
		10.3 Self-attitudes
		10.4 Symmetry of Attitudes
		10.5 Stability of Social Networks
		10.6 Conclusions
		References
	11 A Model of Consensus and Consensus Building Within the Framework of Committees with Permissible Ranges of Decision Makers
		11.1 Introduction
		11.2 Preliminaries peleg1984,yamazaki-etal2000a
			11.2.1 Committees and Core peleg1984
			11.2.2 Committees with Permissible Ranges yamazaki-etal2000a
		11.3 Consensus and Consensus Building
		11.4 Consensus and Core
		11.5 Consensus and Nash Equilibrium
		11.6 Existence of Consensus
		11.7 Conclusions
		Appendix: Core and Efficiency
		References
	12 A Game Theory Investigation of Contract Between IT Vendor and User in Problems of Information System
		12.1 Introduction
		12.2 Background
			12.2.1 IT Risk from Human Resources
			12.2.2 IT Outsourcing and Compensation for Damages
			12.2.3 Game Theory
		12.3 Modeling of Outsourcing Contract Based on Game Theory
			12.3.1 Payoffs of a User Company
			12.3.2 Payoffs of an IT Vendor
			12.3.3 Claims for Damage from a User Company to an IT Vendor
			12.3.4 Incentives from a User Company to an IT Vendor
			12.3.5 Payoff in a Case Without a Claim for Damage and No Incentive
		12.4 Introduce a Claim for Damage
			12.4.1 Contract for IT Vendor to Pay Full Damage
			12.4.2 Contract for Both Companies to Pay the Damage
		12.5 Effect of Incentives
		12.6 Repeated Game
		12.7 Conclusion
		Appendix: 3 Games (Non-Implement Game, Prisoner\'s Dilemma, and Implement Game)
		Reference
	13 Systemic Approach to Reliability and Safety Management Incorporating Uncertainty
		13.1 Introducing a Systems Perspective to Reliability and Safety Theory
		13.2 Emergent Failure Generation
			13.2.1 Reliability Engineering Theory from a Systems Perspective
			13.2.2 Emergent Failures
				13.2.2.1 [Pattern A] Failures Caused by Non-contact Interactions
				13.2.2.2 [Pattern B] Failures Caused by Component Combination Incompatibilities
				13.2.2.3 [Pattern C] Faulty Contact Failure
		13.3 Gray Zone Model
			13.3.1 What Is a Gray Zone?
			13.3.2 Safety Assurance Design and Danger Avoidance Design
			13.3.3 Toward Safety Acquisition Considering the Gray Zone
			13.3.4 Threshold-Dependent Cases
		13.4 Automated Human–Machine Cooperative Systems
			13.4.1 Non-Homogeneous Safety Monitoring System
			13.4.2 Human–Machine Cooperative Monitoring System
			13.4.3 Mathematical Approach to Optimal Monitoring Strategy
				13.4.3.1 Domain Assigning Probability
				13.4.3.2 Combination Rule
				13.4.3.3 Assumption
				13.4.3.4 Optimal Monitoring System
		13.5 Toward Establishing a Systemic Safety Management
		References