Informal Approaches to Developing Simulation Models

Chapter
Part of the Understanding Complex Systems book series (UCS)

Why Read This Chapter?

To get to know some of the issues, techniques and tools involved in building simulation models in the manner that probably most people in the field do this. That is, not using the “proper” computer science techniques of specification and design, but rather using a combination of exploration, checking and consolidation.

Abstract

This chapter describes the approach probably taken by most people in the social sciences when developing simulation models. Instead of following a formal approach of specification, design and implementation, what often seems to happen in practice is that modellers start off in a phase of exploratory modelling, where they don’t have a precise conception of the model they want but a series of ideas and/or evidence they want to capture. They then may develop the model in different directions, backtracking and changing their ideas as they go. This phase continues until they think they may have a model or results that are worth telling others about. This then is (or at least should be) followed by a consolidation phase where the model is more rigorously tested and checked so that reliable and clear results can be reported. In a sense what happens in this later phase is that the model is made so that it is as if a more formal and planned approach had been taken.

There is a danger of this approach: that the modeller will be tempted by apparently significant results to rush to publication before sufficient consolidation has occurred. There may be times when the exploratory phase may result in useful and influential personal knowledge but such knowledge is not reliable enough to be up to the more exacting standards expected of publicly presented results. Thus it is only with careful consolidation of models that this informal approach to building simulations should be undertaken.

References

  1. Alam SJ, Geller A, Meyer R, Werth B (2010) Modelling contextualized reasoning in complex societies with “endorsements”. J Artif Soc Soc Simul 13(4). http://jasss.soc.surrey.ac.uk/13/4/6.html
  2. Bergenti F, Gleizes M-P, Zambonelli F (eds) (2004) Methodologies and software engineering for agent systems: the agent-oriented software engineering handbook. Kluwer, BostonMATHGoogle Scholar
  3. Cartwright N (1983) How the laws of physics lie. Clarendon, OxfordCrossRefGoogle Scholar
  4. David N (2013) Validating simulations. Chapter 8 in this volumeGoogle Scholar
  5. Epstein J (2008) Why model? J Artif Soc Soc Simul 11(4). http://jasss.soc.surrey.ac.uk/11/4/12.html
  6. Evans A, Heppenstall A, Birkin M (2013) Understanding simulation results. Chapter 9 in this volumeGoogle Scholar
  7. Galán J et al (2013) Detecting and avoiding errors and artefacts. Chapter 6 in this volumeGoogle Scholar
  8. Gilbert N, Bankes S (2002) Platforms and methods for agent-based modelling. Proc Natl Acad Sci USA 99(3):7197–7198CrossRefGoogle Scholar
  9. Grimm V et al (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987–991CrossRefGoogle Scholar
  10. Grimm V, Polhill G, Touza J (2013) Documenting simulations using ODD. Chapter 7 in this volumeGoogle Scholar
  11. Henderson-Sellers B, Giorgini P (eds) (2005) Agent-oriented methodologies. Idea Group, HersheyGoogle Scholar
  12. Jonker C, Treur J (2013) A formal approach to building compositional agent-based simulations. Chapter 5 in this volumeGoogle Scholar
  13. Kuhn T (1969) The structure of scientific revolutions. University of Chicago Press, ChicagoGoogle Scholar
  14. Law AM (2008) How to build valid and credible simulation models. In: Mason SJ et al. (eds) Proceedings of the 2008 winter simulation conference, Miami, FL. http://www.informs-sim.org/wsc08papers/007.pdf
  15. Luck M, Ashri R, d’Inverno M (2004) Agent-based software development. Artech House, LondonMATHGoogle Scholar
  16. Nikolai C, Madey G (2009) Tools of the trade: a survey of various agent based modelling platforms. J Artif Soc Soc Simul 12(2). http://jasss.soc.surrey.ac.uk/12/2/2.html
  17. Parker DC, Meretsky V (2004) Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics. Agric Ecosyst Environ 101(2–3):233–250CrossRefGoogle Scholar
  18. Polhill JG, Edmonds B (2007) Open access for social simulation. J Artif Soc Soc Simul 10(3). http://jasss.soc.surrey.ac.uk/10/3/10.html
  19. Polhill G, Gotts N, Law ANR (2001) Imitative versus non-imitative strategies in a land-use simulation. Cybern Syst 32(1–2):285–307MATHGoogle Scholar
  20. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82:609–623CrossRefGoogle Scholar
  21. Shannon RE (1998) Introduction to the art and science of simulation. In: Medeiros DJ, Watson EF, Carson JS, Manivannan MS (eds) Proceedings of the 1998 winter simulation conference, Washington, D.C. http://www.informs-sim.org/wsc98papers/001.PDF
  22. Tobias R, Hofmann C (2004) Evaluation of free Java-libraries for social-scientific agent based simulation. J Artif Soc Soc Simul 7(1). http://jasss.soc.surrey.ac.uk/7/1/6.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Centre for Policy Modelling, Business SchoolManchester Metropolitan UniversityManchesterUK

Personalised recommendations