Skip to main content

Abductive Fallacies with Agent-Based Modeling and System Dynamics

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5466))

Abstract

Increasing usage of computer simulation as a method of pursuing science makes methodological reflection immanently important. After discussing relevant philosophical positions Winsberg’s view of simulation modeling is adapted to conceptualize simulation modeling as an abductive way of doing science. It is proposed that two main presuppositions determine the outcome of a simulation: theory and methodology. The main focus of the paper is on the analysis of the role of simulation methodologies in simulation modeling. The fallacy of applying an inadequate simulation methodology to a given simulation task is dubbed ‘abductive fallacy’. In order to facilitate a superior choice of simulation methodology three respects are proposed to compare System Dynamics and Agent-based Modeling: structure, behavior and emergence. These respects are analyzed on the level of the methodology itself and verified in case studies of the WORLD3-model and the Sugarscape model.

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

Buying options

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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brassel, K., Möhring, M., Schumacher, E., Troitzsch, K.: Can agents cover all the world? In: Conte, R., Hegselmann, R., Terna, P. (eds.) Simulating social phenomena, pp. 55–72. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  2. Casti, J.: Would-Be Worlds: How simulation is changing the frontiers of science. John Wiley & Sons, New York (1997)

    Google Scholar 

  3. Corning, P.: The re-emergence of ‘emergence: a venerable concept in search of a theory. Complexity 7(6), 18–30 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Epstein, J., Axtell, R.: Growing artificial societies. MIT Press, Washington (1996)

    Google Scholar 

  5. Giere, R.: How models are used to represent reality. Philosophy of science 71, 742–752 (2004)

    Article  Google Scholar 

  6. Gilbert, N., Troitzsch, K.: Simulation for the social scientist. Open University Press, Buckingham (1999)

    Google Scholar 

  7. Grebel, T., Pyka, A.: Agent-based modelling – A methodology for the analysis of qualitative development processes, 2004. In: Lombardi, M., Squazzoni, F. (eds.) Saggi di economia evolutiva, FrancoAngeli, Milano (2005)

    Google Scholar 

  8. Lorenz, T., Bassi, A.: Comprehensibility as a discrimination criterion for Agent-Based Modelling and System Dynamics: An empirical approach. In: Proceedings of the 23rd International Conference of the System Dynamics Society, Boston (2005), http://www.systemdynamics.org/conferences/2005/proceed/papers/BASSI175.pdf

  9. Lorenz, T., Jost, A.: Towards an orientation-framework for multiparadigm modeling. In: Größler, et al. (eds.) Proceedings of the 24th international conference of the System Dynamics Society, Nijmegen (2006), http://www.systemdynamics.org/conferences/2006/proceed/papers/LOREN178.pdf

  10. Meadows, D.H., Meadows, D.L., Randers, J., Behrens, W.: The limits to growth. Universe Books, New York (1972)

    Google Scholar 

  11. Meadows, D., Robinson, J.: The electronic oracle. John Wiley & Sons, Chichester (1985)

    Google Scholar 

  12. Morgan, M.: Experiments without Material Intervention. In: Raader, H. (ed.) The Philosophy of Scientific Experimentation, pp. 216–235. University of Pittsburgh Press, Pittsburgh (2003)

    Google Scholar 

  13. Morrison, M., Morgan, M.: Models as mediating instruments. In: Morgan, M., Morrison, M. (eds.) Models as mediators, pp. 10–37. Cambridge University Press, Cambridge (1999)

    Chapter  Google Scholar 

  14. Morrison, M.: Models as autonomous agents. In: Morgan, M., Morrison, M. (eds.) Models as mediators, pp. 38–65. Cambridge University Press, Cambridge (1999)

    Chapter  Google Scholar 

  15. Moss, S., Edmonds, B.: Towards good social science. JASS 8(4) (2005)

    Google Scholar 

  16. Oreskes, N.: Why believe a computer? In: Schneiderman, J. (ed.) The earth around us: Maintaining a livable planet, pp. 70–82. W.H. Freeman & Company, San Francisco (2000)

    Google Scholar 

  17. Parunak, H., Savit, R., Riolo, R.: Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. In: Proceedings of Workshop on Modeling Agent Based Systems, pp. 10–25 (1998)

    Google Scholar 

  18. Phelan, S.: A Note on the Correspondence Between Complexity and Systems Theory. Systemic Practice and Action Research 12(3), 237–246 (1999)

    Article  Google Scholar 

  19. Sawyer, R.: Simulating emergence and downward causation in small groups. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS, vol. 1979, pp. 49–67. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  20. Schieritz, N., Milling, P.: Modeling the Forest or Modeling the Trees. In: Proceedings of the 21st International Conference of the System Dynamics Society (2003)

    Google Scholar 

  21. Sterman, J.: Business Dynamics. Systems Thinking and Modeling for a Complex World. McGraw-Hill, Boston (2000)

    Google Scholar 

  22. Winsberg, E.: Sanctioning Models: The epistemology of simulation. Science in context 12(2), 275–292 (1999)

    Article  Google Scholar 

  23. Winsberg, E.: The hierarchy of models in simulation. In: Magnani, L., Nersessian, N., Thagard, P. (eds.) Model-based reasoning in Scientific discovery. Springer, New York (1999)

    Google Scholar 

  24. Winsberg, E.: Simulated experiments: methodology for a virtual world. Philosophy of science 70, 105–125 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lorenz, T. (2009). Abductive Fallacies with Agent-Based Modeling and System Dynamics. In: Squazzoni, F. (eds) Epistemological Aspects of Computer Simulation in the Social Sciences. EPOS 2006. Lecture Notes in Computer Science(), vol 5466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01109-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01109-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01108-5

  • Online ISBN: 978-3-642-01109-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics