Innovative Systems Approach for Facilitating Smarter World

2023
ISBN: 978-981-19-7775-6
Verlag: Springer Nature Singapore

Buch, Englisch, 221 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 560 g

Reihe: Design Science and Innovation

This book introduces state-of-the-art concepts and methodologies on innovative systems approach that enables the grand design and implementation about Smarter World. This book also describes the shared view that the various heterogeneous social systems that make up Smarter World should be viewed as systems at an abstract level, and develop new developments in SoS and spiral systems through the cycle of analysis, synthesis and abduction. Several new concepts that integrate data-driven mechanism into traditional model-driven methodologies in systems approach are explained with practical applications. As such, it offers a valuable resource for systems engineers, system integrators, and researchers in related engineering fields, as well as government policymakers.

Zielgruppe

Research

Autoren/Hrsg.

Kaihara, Toshiya

Funabashi, Motohisa

Kita, Hajime

Takahashi, Shingo

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Konstruktionslehre, Bauelemente, CAD
• Sozialwissenschaften Soziologie | Soziale Arbeit Spezielle Soziologie Wissenssoziologie, Wissenschaftssoziologie, Techniksoziologie
• Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik

Inhaltsverzeichnis

1) Toward the realization of innovative systems approach for social problem-solving In order to realize smarter world, an innovative systems approach that can flexibly handle various decisions in different time and space, which is characteristic of social systems composed of various stakeholders, is essential by organically and highly integrating the cyber world and the real world. In this chapter, we outline a new systems approach to realize seamless integration amongst multiple social subsystems with hierarchical characteristics that constitute a social system as promising methodology to achieve this.

2) Modelling and Optimization under Uncertainty and Their Integrated Approach -A Spiral-up Systems Approach for Risk managementUnder uncertain situation that the data are unknowable, modelling and optimization are considered. Concretely, by introducing min-max and/or robustness criteria, on which the worst-case scenario are assumed for risk management, learning problems to construct system models with uncertain variables and also optimization problems with their system models as equality constraints are formulated as well as their solutions. In addition to these suggestions, based on concept of a spiral-up systems approach, an approach integrated modelling and optimization under uncertainty is proposed.
3) Issues of System Cooperation from a Viewpoint of System Structure This article discusses system cooperation from a viewpoint of system structure as a key issue on SOS. First, the author points out issues on value and economy of systems. Then cooperation of systems is discussed with 6 typical system structures. Among such structures, decentralized autonomous systems and system integration are discussed more in detail as their promising applications to construct novel social systems.
4) Boundary and Relationality Perspective Systems Approach -Towards Its DevelopmentIn order to construct a new systems approach that can handle situations and issues in recent systems, the importance of taking boundaries and relationships as a perspective has been pointed out. In this chapter, the author discusses what it means to think about systems from the perspectives of boundaries and relationalities, and what this will bring about as a new systems approach, and prospect a path toward the construction of a new systems approach with these perspectives.
5) Relationality-driven System Design for System of Systems in Local Communities In designing, operating, and managing a System of Systems (SoS), human factors cannot be disregarded. Based on the idea that the hints for achieving an SoS are naturally embedded and concealed in people’s daily lives, on the contrary, we should set human activities and lives as the origin for system design. In this article, starting from the reality of people’s activities and daily lives in a local community, we investigate a possibility to create a mechanism through which people are naturally self-motivated to be involved in the process of generating, operating, and managing an SoS.
6) Black-Box Optimization and Its Applications Data-driven optimization, which performs optimization with acquiring data online from a simulator or sensor, is called Black-Box Optimization (BBO). With the development of simulation technology and measurement technology, Black-Box Optimization has been attracting attention as a practical optimization method for large-scale and complex real systems. In this chapter, from the viewpoint of robustness and adaptability of optimization methods, while we will give an overview of typical approaches to Black-Box Optimization, and summarize not only research trends such as robustness and adaptability of Metaheuristics, but also approaches to deal with constraints.
7) Estimation of Objective Functions: Modelling of Problems and Understanding of Human Decision-Making Processes Human decision-making is highly influential and of great importance in systems that involve humans and their activities. Human decision-making is a process of optimization where certain objective functions are maximized (or minimized). The objective functions are, however, not necessarily known or defined explicitly. This chapter describes some techniques that estimate the objective functions or parts of them used in human decision-making processes in the contexts of interactive evolutionary computation, multi-objective optimization and (inverse) reinforcement learning. The estimated results form models of human-decision making and human behaviours, and will be useful for assisting humandecision making, imitating human expert skills, and designing or improving the systems involving human activities.
8) A co-evolutionary systems design based on integration of machine learning and optimization A framework on co-evolutionary decision-making is proposed for estimating appropriate objective functions and constraints of decision-makers from big data for automatically generating optimization models. A feedback loop is constructed for machine learning and optimization to use the optimization results for machine learning to complement each other's performance for real-time optimization and online additional learning. A case study on the application of the machine learning and inverse optimization method for estimating weighting factors of the objective function of the production scheduling problem is presented.
9) Toward System Thinking Practice through Cases, Games, and Agent-Simulation This chapter describes a new integrated approach toward system thinking practice for implementing a complex system of systems in a smarter world. For this purpose, we focus on conventional methods with cases and games in a business school education. The method will ground SoS thinking practice using cases and games for/from agent-simulation inspired from persona marketing techniques.
10) Causal reasoning in socio-economics systems In this paper, from the viewpoint of causal inference, we discuss propensity score matching as data-based statistical causal inference and ABM as model-based deductive causal inference. The propensity score is a very useful method to estimate the effect of similar measures from a hypothetically randomized set of data, even when experiments are difficult. For completely new measures, agent-based deductive causal inference is effective because it is model-based and can deductively predict the future to a certain extent. The importance of causal inference approaches from both sides of the data and the model is expected to increase in the future. 11) Co-creating Modeling Process as Adaptive Decision Making Process This chapter introduces the concept of co-creative modelling as an adaptive decision making process that realizes dynamically the cycle of structuring and restructuring models to provide a co-creating process for social problem solving by problem holders in real fields.
12) Mutual Growth of Human and System in Smarter World This chapter predicts what the adaptable human-machine systems should be in the Smarter World inspired by the phenomenon of mutual growth between organisms.
13) Towards SoS evolution management –approaches and social significanceIn the era of SoS (System of Systems), it is very important to manage not only individual system development but also evolution as SoS. In the sociotechnical system, it is expected that the methodology for managing the evolution of SoS will be embodied, starting from the concept of transition management, which was conceived for social innovation in the environmental field. In this paper, we show how this methodology is desired in the real world and propose the direction of the solution by the computational approach.
14) Power systems progressing with systems approach We examine the use of new Systems Approaches to solve the problems of the current electric power infrastructure in Japan. Furthermore, we describe the objectives and outline of typical power markets, including new markets that are expected to open in the future.
15) Power System Network Technology -System of Systems Case StudyThis chapter describes the current status and future prospects of "electric power network systems" related to power transmission / transformation and distribution, among the huge power systems.
16) System on Systems on railways – Interconnection with railway companies - This chapter describes the history and the current status of "interconnection with railway companies".
17) Current and Future Trends on Smart Home Technology –Including SoS PerspectiveThis chapter introduces the latest trends in commercialization and research on smart home technology through domestic and overseas cases. Furthermore, we will look into the future from the perspective of the System of Systems.

Über Autor(innen)

Toshiya KAIHARA is a Professor of Graduate School of System Informatics, and Director of Value Creation Smart Production Research Centre at Kobe University, Kobe, Japan. He received the B.E. and M.E. degrees from Kyoto University, Kyoto, Japan, and the Ph.D. and DIC from Imperial College London, London, UK. His research interests include systems optimization and simulation, and their application into production, service, and social systems. He is author of more than 450 publications. He is an editor and author of book: Innovative Systems Approach for Designing Smarter World (2020) of Springer. He is a member of JSME(Fellow), IEEJ(Fellow), EAJ, ISCIE, SICE, JSPE, ORSJ, SSJ, CIRP(Fellow), IFAC, IFIP, IEEE, and others.

Hajime KITA is a Professor at Institute for Liberal Arts and Sciences, Kyoto University in Japan. He received his PhD degree from Kyoto University. His research interests are evolutionary computation, social simulation and education of informatics in university. He is author and editor of books: Agent-Based Simulation: From Modelling Methodologies to Real-World Applications (2005), Control of Traffic Systems in Buildings (2006), Agent-Based Approaches in Economic and Social Complex Systems V (2009), Realistic Simulation of Financial Markets (2016) of Springer.

Shingo TAKAHASHI is Professor of the Department of Industrial and Management Systems Engineering and Director of Institute for Social Simulation at Waseda University. He holds MS and PhD in Systems Science from Tokyo Institute of Technology. His current research interests include: modeling and simulation of social systems as complex adaptive systems, especially focusing on agent-based social simulation and soft systems thinking as practice, aimed at applying to soft, i.e. ill-structured, problem situations involving various people with plural world views.

Dr. Motohisa FUNABASHI is a System Scientist. He graduated from Graduate School of Engineering, Kyoto University in 1969, and joined Hitachi, Ltd. working for R&D on systems control (1969-2010). Also he served as a Visiting Professor, Graduate School of Mathematical Sciences, the University of Tokyo (1996-1999), a Visiting Professor, Graduate School of Informatics, Kyoto University (2003-2008), an Auditor of the National Institute for Environmental Studies (2007-2011), a Senior Professor of Japan Advanced Institute of Science and Technology (2012-2017), and a Program Officer of Low Carbon Technology Development and Demonstration Program of the Ministry of the Environment (2018-2020). His current research interests include computational systems modeling and architecting. He is a member of SICE (Honorary Member and Fellow), IEEJ (Fellow), IEEE, and ACM, and others.