Innovative Systems Approach for Facilitating Smarter World

Preface
Contents
1: Toward Realization of Innovative Systems Approach for Societal Design: Multiscale Social Modeling and Simulation (MSMS) Methodology
	1	 Introduction
	2	 Multiscale Modeling and Simulation Methods for Social Systems Design
		2.1	 Social Systems Modeling and Simulation
		2.2	 Related Studies
	3	 Multiscale Social Modeling and Simulation (MSMS) Terminology
		3.1	 Terminology
		3.2	 Scale Separation
		3.3	 Scale Connection
	4	 MSMS Application into Social Systems for the Electric Power Industry
		4.1	 High-Level Decision-Maker: The Government
		4.2	 Mid-Level Decision-Maker: Power Company
		4.3	 Low-Level Decision-Maker: Electricity Consumers—Residents
		4.4	 Scale Connection and Implementation of the Target Model
	5	 MSMS Validation with Computational Experiments
		5.1	 Experiment Setup
			5.1.1	 Residents
			5.1.2	 Timeline Setup
			5.1.3	 Power Companies
			5.1.4	 Government
		5.2	 Multiscale Simulation of Smartmeter Installation
			5.2.1	 Objective of Experiment
			5.2.2	 Experimental Outline
			5.2.3	 Results of the Computational Experiment
		5.3	 Comparative Experiment: Model Restricted at Macro-Scale Against Multiscale Model
			5.3.1	 Objective of Experiment
			5.3.2	 Experimental Outline
			5.3.3	 Results of Computational Experiment
	6	 Conclusions
	References
2: Modelling-Driven Optimization Problems with Uncertainty Tolerance and Their Solution Strategies: A Risk-Management Perspective in the Circulating and Spiral-up Systems Approach
	1	 Introduction
	2	 Formulation of Modelling-Driven Optimization Problems
	3	 Modelling-Driven Optimization with Uncertainty Tolerance
		3.1	 Formulation of Modelling-Driven Optimization with Uncertainty Tolerance
		3.2	 Constraint-Relaxation Method for Modelling-Driven Optimization Problems with Uncertainty Tolerance
		3.3	 Computational Techniques for Constraint-Relaxation Methods
	4	 Conclusions
	References
3: Issues of System Cooperation from a Viewpoint of System Structure
	1	 Introduction
	2	 Value and Economy of Systems
		2.1	 System Size and Economy
		2.2	 Symbiotic Man-Machine Systems and Value Creation
	3	 Cooperation of Two Similar Systems
	4	 Structure of Cooperating Systems
		4.1	 Platforms
		4.2	 Layered Systems
		4.3	 Serial Systems
		4.4	 Autonomous Decentralized Systems
		4.5	 System Integration
	5	 Systems Close to and Far from Us
		5.1	 Distance to Systems
		5.2	 Importance of Human Resources, Knowledge, and Learning
	6	 User-Centric System Construction
		6.1	 Users as a Field of Value Creation
		6.2	 Proposal of Home System Integration
		6.3	 System Integration and Standardization, from a Case of Educational Systems
	7	 Conclusion
	References
4: Boundary and Relationality Perspective Systems Approach: Towards Its Development
	1	 Introduction
	2	 Challenges Related to Boundary and Relationality for Systems Approaches
	3	 Various Kinds of Boundaries and Relationalities
	4	 Possible Systems Approaches
		4.1	 Relationality Oriented Systems Approach
		4.2	 Systems Approach Integrating Heterogeneous Models and Co-evolving with Real Systems
	5	 Conclusion
	References
5: Relationality Design Emphasizing Clinical Aspects of System of Systems in Local Community
	1	 Introduction
	2	 Relationality-Driven System Design of Community for SoS in Local Community
		2.1	 Research Perspective
		2.2	 Gift & Circulation Model (G&CM) as Mechanism for Generation, Circulation, and Communization of Relationality Assets (RAs)
		2.3	 Applications Provided in Field Experiment
	3	 Relationality Design with Emphasis on Clinical Aspects
		3.1	 Relationality Assets Adapted for an Individual’s Clinical Aspects
		3.2	 Introduction of Proactive Functions for Leadership Commission in Community
		3.3	 Diverse Development of Gift & Circulation Model to Incorporate Clinical Aspects
	4	 Conclusion
	References
6: Black-Box Optimization and Its Applications
	1	 Introduction
	2	 Black-Box Optimization
	3	 Robustness and Adaptability for Black-Box Optimization
		3.1	 Robustness and Adaptability of Metaheuristics
		3.2	 Example Metaheuristics with Robustness and Adaptability
			3.2.1	 Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
			3.2.2	 Adaptive Particle Swarm Optimization with Rotational Invariance
	4	 Constrained Black-Box Optimization
		4.1	 Classes of Constraints in BBO
		4.2	 Classical Method for Constraint Handling
		4.3	 Methods for Constraint Handling in BBO
			4.3.1	 Constraints Handling Techniques
			4.3.2	 Penalty-Based CHT
			4.3.3	 Separatist-Based CHT
			4.3.4	 Multi-Objective-Based CHT
			4.3.5	 General Comment
	5	 Application of BBO
	6	 Conclusion
	References
7: Estimation of Objective Functions: Modeling of Problems and Understanding of Decision-Making Processes Towards the Spiral-up Systems Approach
	1	 Introduction
	2	 Formulation of Optimization/Decision-Making
	3	 Estimation of Objective Functions
		3.1	 Estimation of Weight Coefficients on Multiple Objective Functions
		3.2	 Estimation in Interactive Evolutionary Computation
			3.2.1	 Estimation of the Contribution of Decision Variables to the Objective Function
			3.2.2	 Estimation of the Interaction Between Decision Variables
		3.3	 Inverse Optimization and Inverse Reinforcement Learning
	4	 Contributions to the Spiral-up Systems Approach
	5	 Conclusions
	References
8: Co-evolutionary Decision-Making Modeling Via Integration of Machine Learning and Optimization
	1	 Introduction
	2	 Supply Chain Dynamic Model Configuration Platform
		2.1	 Construction of Integrated Information and Mathematical Model for Enterprise Collaboration in Supply Chains
		2.2	 Real-Time Optimization of Equilibrium Solutions
		2.3	 Game-Theoretic Model for Mass Customization
		2.4	 Analysis of Supply Chain Structure by Game Theoretic Approach
	3	 Co-evolutionary System Design of Machine Learning and Optimization Based on Dynamic Model
		3.1	 Co-evolutionary Decision-Making Modeling Through the Integration of Machine Learning and Optimization
	4	 Identification of Objective Function from Data for Scheduling Problems
		4.1	 Machine Learning Model for Identification of Objective Function from Input and Output Data
		4.2	 Definition of Production Scheduling Problem
	5	 Estimating Weighting Factors from Data for Scheduling Problems
		5.1	 Experimental Results for Identification of the Objective Function
	6	 Estimation of Weighting Factors in the Multi-Objective Scheduling Problems
		6.1	 Estimating Weighting Factors Using Machine Learning
		6.2	 Estimating the Weighting Factor by the Inverse Optimization Model
		6.3	 Inverse Optimization Using Surrogate Model
		6.4	 Numerical Example for Weighting Factor Estimation Problem
	7	 Conclusion
	References
9: Agent Modeling, Gaming Simulation, and Their Formal Description
	1	 Introduction
	2	 Our Previous Work with Gaming Simulation
		2.1	 Case-Based Gaming Approach to Study Decision-Making Processes
		2.2	 Log-Based Approach to Analyze Participants of Gaming Simulation
	3	 Managerial Decision-Making Description Model
	4	 System-Experience Boundaries Map
	5	 Example: Asset Formation and Withdrawal Simulation (Kikuchi et al. 2022)
		5.1	 Overview of the Agent-Simulation Model
		5.2	 Classification of Individual Questionnaires and Simulation of Asset Formation
		5.3	 Formal Description of the Simulation Using SEBM
	6	 Concluding Remarks
	References
10: Causal and Deductive Reasoning in Socio-Economic Systems
	1	 Introduction
	2	 Data-Based Causal Inference in Statistics
		2.1	 An Inductive Causal Inference Case Based on Propensity Score Matching
		2.2	 Matching
		2.3	 External Support Measure Effect for Startups
	3	 Model-Based Deductive Causal Inference
		3.1	 Deductive Causal Inference for Passing on Knowledge
		3.2	 Deductive Causal Inference of City Dynamics
	4	 Conclusion
	References
11: Co-Creative Modeling as Adaptive Decision-Making Process
	1	 Introduction
	2	 Adaptive Approach
	3	 Decision-Making Process Model
		3.1	 Rational Decision-Making Model
		3.2	 Decision-Making Support by Social Simulation
	4	 Participatory Modeling
		4.1	 Virtual Grounding
		4.2	 Companion Modeling
		4.3	 Generative Experimentation
		4.4	 Tabletop Exercises/Participatory Simulation
	5	 Sharing Situation with Modeling-Group Model Building as a Typical Case
	6	 Co-Creative Modeling as Adaptive Decision-Making Process
	7	 Concluding Remark
	References
12: Mutual Growth of Human and System in Smarter World
	1	 Introduction
	2	 Adaptive Learning of Artificial Systems
	3	 Growing Together
	4	 Direction of Growth
	5	 Growing Systems in Anti-fragile Manners
	6	 Conclusions
	References
13: Towards SoS Evolution Management for Developing Smarter Cities: Social Significance and Approaches
	1	 Introduction
	2	 SoS Evolution Management in Urban Development
		2.1	 City Development Scenario
		2.2	 Significance from Systems Science and Technology
	3	 Fundamental Technologies for the Evolution of Social Service Systems
		3.1	 Transition Management
		3.2	 Systems Engineering
		3.3	 Enterprise Information Systems
		3.4	 Required Research Approach
	4	 Proposed Scheme for Exploration of Socio-Technical SoS Evolution
		4.1	 Present Efforts on SoS Evolution Planning
		4.2	 Design of Socio-Technical SoS Evolution with Concept of the Transition Management
	5	 Concluding Summary
	References
14: Power System Progressing with Systems Approach
	1	 Introduction
	2	 Application of the New Systems Approach in Electric Power Infrastructure
		2.1	 Challenges for Electric Power Infrastructure
		2.2	 Application of the New Systems Approach
	3	 Changes in the Power System
		3.1	 Dissemination and Issues of Renewable Energy
		3.2	 From Centralized Systems to Distributed Systems
		3.3	 Electricity Exchange Market
		3.4	 Auction Method for the Reserve Market
	4	 Conclusions
	References
15: Power Network System Technology: A Focus on SoS
	1	 Introduction
	2	 Systems in Power Networks
		2.1	 OT System
		2.2	 IT System
		2.3	 The System Structure of Power Networks
	3	 Standardization Oriented for Interoperability
		3.1	 IEC 61850
		3.2	 IEC 61970 CIM
		3.3	 Concept Enabling Cross-Manufacturer Interoperability
	4	 Example of Systems of Systems in Power Network Systems
		4.1	 Visualization of the Area of Power Outages Using Web Schematics
		4.2	 Asset Management Utilizing Sensors and Other Devices
	5	 Prospects of Leveraging Information from OT to IT
	6	 Conclusion
	References
16: System of Systems in Railway
	1	 Railway as a Backbone in Human Life
	2	 System of Systems in a Certain Railway Line
	3	 Through Service
		3.1	 The Basic Mode of Through Service
		3.2	 Quantity and Destination of Through Service
		3.3	 Integrating Hardware
		3.4	 Integrating Software
		3.5	 Influence of Through Service
		3.6	 Through Service in Other Countries
	4	 Revision of Timetables
		4.1	 How to Revise Timetable
		4.2	 From Plan to Implementation
		4.3	 Restoring the Timetable
	5	 Maas and Automatic Operation
		5.1	 MaaS: Mobility as a Service
		5.2	 Automatic Operation
	6	 Conclusion
	References
17: Current and Future Trends on Smart Home Technology: Including SoS Perspective
	1	 Introduction
	2	 Current Status of Commercialization of Smart Home Technology
		2.1	 Overseas Commercialization Cases
		2.2	 Commercialization Cases in Japan
	3	 Current Research Trends Related to Smart Home
		3.1	 Research Trends in Overseas
		3.2	 Research Trends in Japan
	4	 Future Outlook from a SoS Perspective
	5	 Conclusions
	References