Skip to main content

Agent-Based Modeling and Simulation Validation by Scenario Analysis

  • Conference paper
  • First Online:
Agent-Based Approaches in Economic and Social Complex Systems VII

Part of the book series: Agent-Based Social Systems ((ABSS,volume 10))

Abstract

In this chapter, we discuss how scenario analysis can contribute to the validation of agent-based modeling and simulation (ABMS). We describe the basic architecture and characteristics of ABMS. We also introduce the concept and method of scenario analysis and explain that validation by users (stakeholders) is critical for the acceptance of ABMS practitioners’ proposals. Next, we discuss a validation problem relative to the arbitrary representation of ABMS results. For this problem, we propose a framework of ABMS validation that emphasizes the stakeholders’ learning through a participatory scenario-analysis phase with no arbitrary ABMS results. Furthermore, we recognize the need for an information system that supports the participatory scenario-analysis phase by the stakeholders in real time. Finally, we conclude that scenario analysis solves the validation problem of arbitrarily presenting ABMS results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://cs.gmu.edu/~eclab/projects/mason/

  2. 2.

    http://www.soars.jp/

  3. 3.

    http://repast.sourceforge.net/

References

  1. North MJ, Macal CM (2007) Managing business complexity. Oxford University Press, New York

    Book  Google Scholar 

  2. Epstein JM (2009) Modelling to contain pandemics. Nature 460:687

    Article  Google Scholar 

  3. Goto Y, Takahashi S (2010) Landscape analysis of possible outcomes. In: Chen SH, Terano T, Yamamoto R (eds) Agent-based approaches in economic and social complex systems VI. Springer Series on ABSS, vol 8. Springer, Tokyo, pp 87–98

    Google Scholar 

  4. Deguchi H (2009) Dawn of agent-based social systems sciences. In: Deguchi H, Kijima K (eds) Manifesto: agent-based social systems sciences. Keiso-Shobo, Tokyo [in Japanese]

    Google Scholar 

  5. Ohori K, Takahashi S (2012) Market design for standardization problems with agent-based social simulation. J Evol Econ 22:49–77. doi:10.1007/s00191-010-0196-y

    Article  Google Scholar 

  6. Richiardi M, Leombruni R, Saam N, Sonnessa M (2006) A common protocol for agent-based social simulation. J Artif Soc S 9, p 15. Accessed 19 January 2013. http://jasss.soc.surrey.ac.uk/9/1/15.html

  7. Sargent RG (2000) Verification, validation, and accreditation of simulation models. In: Joines JA, Barton RR, Kang K, Fishwick PA (eds) Proceedings of the 2000 Winter Simulation Conference. Society for Computer Simulation International, San Diego, CA, pp 50–59

    Chapter  Google Scholar 

  8. Gilbert N (2007) Agent-based models. Sage, Los Angeles

    Google Scholar 

  9. Axtell R, Axelrod R, Epstein JM, Cohen MD (1996) Aligning simulation models: a case study and results. Comput Math Org Theory 1:123–141

    Article  Google Scholar 

  10. Hales D, Rouchier J, Edmonds B (2003) Model-to-model analysis. J Artif Soc S 6, p 5. Accessed 19 January 2013. http://jasss.soc.surrey.ac.uk/6/4/5.html

  11. Yoon M, Lee K (2009) Agent-based and history-friendly models for explaining industrial evolution. Evol Inst Econ Rev 6:45–70

    Google Scholar 

  12. Goto Y, Takahashi S, Senoue Y (2009) Analysis of performance measurement system for knowledge sharing under intraorganizational competition. J Jpn Soc Manag Info 18:15–49 [in Japanese]

    Google Scholar 

  13. Louie MA, Carley KM (2008) Balancing the criticisms: validating multi-agent models of social systems. Simul Model Pract Theory 16:242–256. doi:10.1016/j.simpat.2007.11.011

    Article  Google Scholar 

  14. Barreteau O (2003) The joint use of role-playing games and models regarding negotiation processes: characterization of associations. J Artif Soc S 6, p 3. Accessed 19 January 2013. http://jasss.soc.surrey.ac.uk/6/2/3.html

  15. Ramanath AM, Gilbert N (2004) The design of participatory agent-based social simulations. J Artif Soc S 6, p 1. Accessed 19 January 2013. http://jasss.soc.surrey.ac.uk/7/4/1.html

  16. Espejo R, Schuhmann W, Schwaninger M, Bilello U (1996) Organizational transformation and learning: a cybernetic approach to management. Wiley, Chichester

    Google Scholar 

  17. Ohori K, Kobayashi N, Obata A, Takahashi A, Takahashi S (2012) Decision support for management of agents’ knowledge and skills with job rotation in service-oriented organization. In: Proceedings of the 45th Hawaii International Conference on System Sciences, pp 1492–1501. doi:10.1109/HICSS.2012.195

    Google Scholar 

  18. Deguchi H, Saito T, Ichikawa M, Tanuma H (2011) Simulated tabletop exercise for risk management: anti bio-terrorism multi scenario simulated tabletop exercise. Dev Bus Simul Exp Learn 38:1–21

    Google Scholar 

  19. Argyris C, Schön DA (1996) Organizational learning II: theory, method, and practice. Addison-Wesley, Reading, MA

    Google Scholar 

  20. Heath B, Hill R, Ciarallo F (2009) A survey of agent-based modeling practices (January 1998 to July 2008). J Artif Soc S 12, p 9. Accessed 19 January 2013. http://jasss.soc.surrey.ac.uk/12/4/9.html

  21. Barnaud C, Promburom T, Trebuil G, Bousquet F (2007) An evolving simulation/gaming process to facilitate adaptive watershed management in northern mountainous Thailand. Simul Gaming 38:398–420. doi:10.1177/1046878107300670

    Article  Google Scholar 

Download references

Acknowledgments

This chapter is modified and extended from the earlier version presented at the conference (AESCS 2012). We appreciate the participants and two anonymous reviewers for helpful comments. This work was supported in part by a Grant-in-Aid for Scientific Research 21310097 and 22730312 of Japan Society for the Promotion of Science.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yusuke Goto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Japan

About this paper

Cite this paper

Goto, Y., Takahashi, S. (2013). Agent-Based Modeling and Simulation Validation by Scenario Analysis. In: Murata, T., Terano, T., Takahashi, S. (eds) Agent-Based Approaches in Economic and Social Complex Systems VII. Agent-Based Social Systems, vol 10. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54279-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-54279-7_1

  • Published:

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-54278-0

  • Online ISBN: 978-4-431-54279-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics