Abstract
To facilitate a smarter world, an innovative systems approach that can flexibly accommodate various modes of decision-making with different spatiotemporal granularity, which is a characteristic of social systems composed of diverse stakeholders, is necessary by organically and closely integrating the cyber world and the real world. This chapter presents a new systems approach to design seamless integration among multiple social subsystems with hierarchical characteristics that constitute a social system as a promising methodology to achieve this integration. Societal simulation modeling and simulation methodology are proposed as solutions. However, existing techniques become limited when the multiscale nature of social systems must be considered. Therefore, adaptation of multiscale modeling and simulation approach for social systems is performed in this chapter. The proposed approach is evaluated using a case study of electric power system. Results of computational experiments reveal the effectiveness of multiscale social modeling and simulation for designing social systems without omitting their multiscale nature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abar S, Theodoropoulos G, Lemarinier P, O’Hare G (2017) Agent-based modelling and simulation tools: a review of the state-of-art software. Comput Sci Rev 24:13–33
Ayton G, Noid W, Voth G (2007) Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol 17:192–198
Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci U S A 99:7280
A. Borshchev 2013 Multi-method modeling, Proceedings of the 2013 Winter Simulation Conference. pp. 4089–4100.
Brailsford S, Eldabi T, Kunc M, Mustafee N, Osorio A (2019) Hybrid simulation modeling in operational research: a state-of-the-art-review. Eur J Operational Res 278:721–737
Brookings (n.d.). https://www.brookings.edu/series/connected-society-internet-of-things-innovation/. Accessed May 2022
Japan Cabinet Office (n.d.). https://www8.cao.go.jp/cstp/english/society5_0/index.html. Accessed May 2022
Chopard B, Borgdorff J, Hoekstra AG (2014) A framework for multiscale modelling. Philos Trans R Soc A Math Phys Eng Sci 372(2021):20130378
CIA (n.d.). https://www.cia.gov/library/publications/the-world-factbook/fields/print\_2259.html. Accessed November 2019.
T Eldabi, M Balaban, S Brailsford, N Mustafee 2016 Hybrid simulation: historical lessons, present challenges and futures, proceedings of the 2016 Winter Simulation Conference. 1388–1403.
Electric Power Annual (n.d.). https://www.eia.gov/electricity/annual/. Accessed May 2022
Falcone J, Chopard B, Hoekstra A (2010) MML: towards a multiscale modeling language. Procedia Computer Science 1(1):819–826
Fish J (2009) Multiscale methods: bridging the scales in science and engineering. Oxford University Press, Oxford, UK
Forrester J (1961) Industrial dynamics. MIT Press
Gordon G (1978) The development of the general purpose simulation system, History of programming languages conference. 13(8):183–198
Heine B, Meyer M, Strangfeld O (2005) Stylised facts and the contribution of simulation to the economic analysis of budgeting, journal of artificial societies and social. Simulation 8(4)
Hitachi (n.d.). https://www.hitachi.com/rev/archive/2020/r2020_01/01b05/index.html. Accessed May 2022
M. Horstemeyer 2009 Multiscale modeling: a review. In: Practical Aspects of Computational Chemistry, Springer Science, Heidelberg, Germany. pp. 87–135.
Kaihara T, Kita H, Takahashi S (eds) (2021) Innovative systems approach for designing smarter world. Springer
Kansai Electric Power Inc., Annual report 2019, 2019
Maier MW (1998) Architecting principles for system of systems. Syst Eng 1(4):267–284
Multiscale ABSS Method for Social Policy Making (n.d.). https://www.jst.go.jp/mirai/en/uploads/saitaku2020/JPMJMI20B3_summary_en.pdf. Accessed May 2022
Nikhanbayev N, Kaihara T, Fujii N, Kokuryo D (2019) Multiscale modeling of social systems: scale bridging via decision making, vol 567. Springer, IFIPAICT, Austin, pp 617–624
Society 5.0 (n.d.). https://www.keidanren.or.jp/en/policy/2018/095_booklet.pdf. Accessed May 2022
U.S. Energy Information Administration 2019 Annual Energy Outlook 2019.
Weinan E, Li X, Ren W, Vanden-Eijnden E (2007) Heterogeneous multiscale methods: a review. Commun Comput Phys 2:367–450
Acknowledgments
This chapter is based on the research activity conducted at the “Research and Study Group for the Actual Development of New Systems Approach for the Smarter World” in the Society of Instrument and Control Engineers. The authors would like to thank all the research group members for our fruitful discussions and comments, both from a theoretical perspective and from practical viewpoints in terms of systems approaches. The authors also thank Ms. Sakamoto for her assistance with the organization of the figures and tables.
This work is supported by JST-Mirai Program Grant Number JPMJMI20B3, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kaihara, T., Nursultan, N. (2023). Toward Realization of Innovative Systems Approach for Societal Design: Multiscale Social Modeling and Simulation (MSMS) Methodology. In: Kaihara, T., Kita, H., Takahashi, S., Funabashi, M. (eds) Innovative Systems Approach for Facilitating Smarter World. Design Science and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-19-7776-3_1
Download citation
DOI: https://doi.org/10.1007/978-981-19-7776-3_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7775-6
Online ISBN: 978-981-19-7776-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)