Skip to main content
SpringerLink
Log in

Finding a Structure: Evaluating Different Modelling Languages Regarding Their Suitability of Designing Agent-Based Models

  • Conference paper
  • First Online:
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Body, Motion and Behavior (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12777))

Included in the following conference series:

  • 1153 Accesses

Abstract

Several approaches to standardize the creation of agent-based models exist, but there is no perfect way to do it yet. In this study we analyze, whether two modelling languages (i*Star, UML) can help in designing agent-based models. We identified requirements for building agent-based models and analyzed to what extent the requirements can be met by applying modeling languages. We reflect whether the application of modeling languages can profitably facilitate the creation of agent-based models. We found that modeling languages can meet some requirements for creating agent-based models. Finally, modeling languages offer an added value to the creation of agent-based models, but their application also requires more time than creating a model without their application. However, when creating agent-based models, a considerable amount of time should be spent to decide what the model would depict. Our approach can be helpful in the future for creation of agent-based models.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    Examples of an agent-based simulation with homogeneous are the well-known forest fire and schelling [33] model.

  2. 2.

    Examples for topology include a 2-dimensional grid, network topology or geographic information system topology.

References

  1. Grimm, V., et al.: “Supplementary file s1 to: Grimm, V., et al. (2020) ‘the odd protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism”. J. Artif. Soc. Soc. Simul. 23(2) (2020)

    Google Scholar 

  2. Amouroux, E., Gaudou, B., Desvaux, S., Drogoul, A.: ODD: a promising but incomplete formalism for individual-based model specification. In: 2010 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Hanoi, pp. 1–4 (2010)

    Google Scholar 

  3. Martínez, C.P.A., et al.: A comparative analisys of i*-based agent-oriented modeling languages. In: Proceedings of the SEKE 2005, the 17th International Conference on Software Engineering & Knowledge Engineering: Technical Program, 14–16 July 2005, Taipei, Taiwan, Republic of China, pp. 43–50 (2005)

    Google Scholar 

  4. Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 99(Suppl 3), 7280–7287 (2002). https://doi.org/10.1073/pnas.082080899

    Article  Google Scholar 

  5. Bruch, E., Atwell, J.: Agent-based models in empirical social research. Sociol. Methods Res. 44(2), 186–221 (2015). https://doi.org/10.1177/0049124113506405. PMID: 25983351

    Article  MathSciNet  Google Scholar 

  6. Byrne, D.S.: Complexity Theory and the Social Sciences: An Introduction. Business and the World Economy. Routledge, London (1998). ISBN 9780415162968. https://books.google.de/books?id=NaVSZXVdc-0C

    Google Scholar 

  7. Castle, C.J., Crooks, A.T.: Principles and concepts of agent-based modelling for developing geospatial simulations (2006)

    Google Scholar 

  8. Conte, R., et al.: Manifesto of computational social science. Eur. Phys. J. Special Topics 2014(1), 325–346 (2012). http://wrap.warwick.ac.uk/67839/

    Article  Google Scholar 

  9. Curtis, B., Kellner, M.I., Over, J.: Process modeling. Commun. ACM 35(9), 75–90 (1992)

    Article  Google Scholar 

  10. Engels, G., Heckel, R., Sauer, S.: UML—a universal modeling language? In: Nielsen, M., Simpson, D. (eds.) ICATPN 2000. LNCS, vol. 1825, pp. 24–38. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44988-4_3

    Chapter  Google Scholar 

  11. Epstein, J.M.: Agent-based computational models and generative social science. Complexity 4(5), 41–60 (1999). https://doi.org/10.1002/(SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F. ISSN 1076–2787

    Article  MathSciNet  Google Scholar 

  12. Epstein, J.M.: Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity). Princeton University Press, Princeton (2007)

    Google Scholar 

  13. Fanelli, D.: Opinion: Is science really facing a reproducibility crisis, and do we need it to? Proc. Natl. Acad. Sci. USA 11, 2628–2631 (2018)

    Article  Google Scholar 

  14. Felderer, M., Herrmann, A.: Comprehensibility of system models during test design: a controlled experiment comparing UML activity diagrams and state machines. Softw. Qual. J. 27(1), 125–147 (2019)

    Article  Google Scholar 

  15. Franklin, S., Graesser, A.: Is it an agent, or just a program?: a taxonomy for autonomous agents. In: Müller, J.P., Wooldridge, M.J., Jennings, N.R. (eds.) ATAL 1996. LNCS, vol. 1193, pp. 21–35. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0013570

    Chapter  Google Scholar 

  16. Grimm, V., et al.: A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198(1–2), 115–126 (2006). https://doi.org/10.1016/j.ecolmodel.2006.04.023. ISSN 0304–3800

    Article  Google Scholar 

  17. Grimm, V., et al.: The odd protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism. J. Artif. Soc. Soc. Simul. 23(2), 7 (2020). https://doi.org/10.18564/jasss.4259. ISSN 1460–7425. http://jasss.soc.surrey.ac.uk/23/2/7.html

    Article  Google Scholar 

  18. Grimm, V., Berger, U., DeAngelis, D.L., Gary Polhill, J., Giske, J., Railsback, S.F.: The odd protocol: a review and first update. Ecol. Model. 221(23), 2760–2768 (2010). https://doi.org/10.1016/j.ecolmodel.2010.08.019. ISSN 0304–3800. https://www.sciencedirect.com/science/article/pii/S030438001000414X

    Article  Google Scholar 

  19. Hahn., C., A domain specific modeling language for multiagent systems. In: Proceedings of the 7th International Joint Conference on A“Utonomous Agents and Multiagent Systems, vol. 1, pp. 233–240. Citeseer (2008)

    Google Scholar 

  20. Helbing, D., Balietti, S.: How to do agent-based simulations in the future: from modeling social mechanisms to emergent phenomena and interactive systems design why develop and use agent-based models? Int. J. Res. Market., 1–55 (2011). https://doi.org/10.1007/978-3-642-24004-1. http://www.santafe.edu/media/workingpapers/11-06-024.pdf

  21. Humphrey, W.S., Kellner, M.I.: Software process modeling: principles of entity process models. In: Proceedings of the 11th International Conference on Software Engineering, pp. 331–342 (1989)

    Google Scholar 

  22. Kavakli, E.: Goal-oriented requirements engineering: a unifying framework. Requirements Eng. 6(4), 237–251 (2002)

    Article  Google Scholar 

  23. Krogstie, J., Using a semiotic framework to evaluate UML for the development of models of high quality. In: Unified Modeling Language: Systems Analysis, Design and Development Issues, pp. 89–106. IGI Global (2001)

    Google Scholar 

  24. Macal, C.M., North, M.J.: Tutorial on agent-based modeling and simulation. In: Proceedings of the 2005 Winter Simulation Conference, pp. 2–15 (2005)

    Google Scholar 

  25. Matulevicius, R.: Improving the syntax and semantics of goal modelling languages. In: iStar, pp. 75–78 (2008)

    Google Scholar 

  26. Matulevičius, R., Heymans, P.: Comparing goal modelling languages: an experiment. In: Sawyer, P., Paech, B., Heymans, P. (eds.) REFSQ 2007. LNCS, vol. 4542, pp. 18–32. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73031-6_2

    Chapter  Google Scholar 

  27. Monks, T., Currie, C.S., Onggo, B.S., Robinson, S., Kunc, M., Taylor, S.J.E.: Strengthening the reporting of empirical simulation studies: introducing the stress guidelines. J. Simul. 1(13), 55–67 (2019)

    Article  Google Scholar 

  28. Peng, C., Guiot, J., Wu, H., Jiang, H., Luo, Y.: Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach”. Ecol. Lett. 14(5), 522–536 (2011)

    Article  Google Scholar 

  29. Polhill, J.G., Parker, D.C., Brown, D., Grimm, V.: Using the ODD protocol for describing three agent-based social simulation models of land-use change. J. Artif. Soc. Soc. Simul. 11(2), 1–3 (2008)

    Google Scholar 

  30. Railsback, S.F.: Concepts from complex adaptive systems as a framework for individual-basedmodelling. Ecol. Model. 139(1), 47–62 (2001)

    Article  Google Scholar 

  31. Rand, W., Rust, R.T.: Agent-based modeling in marketing: guidelines for rigor. Int. J. Res. Market. 28(3), 181–193 (2011). https://doi.org/10.1016/j.ijresmar.2011.04.002. ISSN 01678116

    Article  Google Scholar 

  32. Retzlaff, C.-O., Ziefle, M., Calero Valdez, A.: The history of of agent-based modeling in the social sciences. In: Duffy, V.G., (ed.) HCII 2021, LNCS 12777, pp. 304–319. Springer, Cham (2021)

    Google Scholar 

  33. Schelling, T.C.: Models of segregation. Am. Econ. Assoc. 52(2), 604–620 (2013)

    Google Scholar 

  34. Yu, E.S.-K.: Modelling strategic relationships for process reengineering. Ph.D. thesis, University of Toronto (1996)

    Google Scholar 

  35. Söderström, E., Andersson, B., Johannesson, P., Perjons, E., Wangler, B.: Towards a framework for comparing process modelling languages. In: Pidduck, A.B., Ozsu, M.T., Mylopoulos, J., Woo, C.C. (eds.) CAiSE 2002. LNCS, vol. 2348, pp. 600–611. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47961-9_41

    Chapter  Google Scholar 

  36. Calero Valdez, A., Ziefle, M.: Human factors in the age of algorithms. Understanding the human-in-the-loop using agent-based modeling. In: Meiselwitz, G. (ed.) SCSM 2018. LNCS, vol. 10914, pp. 357–371. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91485-5_27

    Chapter  Google Scholar 

  37. Vincenot, C.E.: How new concepts become universal scientific approaches: insights from citation network analysis of agent-based complex systems science. In: Proceedings of the Royal Society B: Biological Sciences, vol. 285, p. 20172360 (2018)

    Google Scholar 

  38. Mitchell Waldrop, M.: Complexity: The Emerging Science at the Edge of Order and Chaos. Simon & Schuster, New York (1992). ISBN 0671767895

    Google Scholar 

  39. Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. The MIT Press (2015) ISBN 9780262731898. https://books.google.de/books?id=LQrhBwAAQBAJ

  40. Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, Hoboken (2009)

    Google Scholar 

  41. Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10, 115–152 (1995)

    Article  Google Scholar 

  42. Wooldridge, M., Jennings, N.R.: Simulating sprawl: a dynamic entity-based approach to modelling North American suburban sprawl using cellular automata and multi-agent systems. Ph.D. Thesis (2004)

    Google Scholar 

  43. Zamli, K.Z., Lee, P.A.: Taxonomy of process modeling languages. In: Proceedings ACS/IEEE International Conference on Computer Systems and Applications, pp. 435–437. IEEE (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Human-Computer Interaction Center, RWTH Aachen University, Campus Boulevard 57, 52076, Aachen, Germany

    Poornima Belavadi, Laura Burbach, Martina Ziefle & André Calero Valdez

Authors
  1. Poornima Belavadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura Burbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martina Ziefle
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. André Calero Valdez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Poornima Belavadi .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Belavadi, P., Burbach, L., Ziefle, M., Calero Valdez, A. (2021). Finding a Structure: Evaluating Different Modelling Languages Regarding Their Suitability of Designing Agent-Based Models. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Body, Motion and Behavior. HCII 2021. Lecture Notes in Computer Science(), vol 12777. Springer, Cham. https://doi.org/10.1007/978-3-030-77817-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77817-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77816-3

  • Online ISBN: 978-3-030-77817-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation