Models and Simulations

  • Nancy J. Nersessian
  • Miles MacLeod
Part of the Springer Handbooks book series (SHB)


In this chapter we present some of the central philosophical issues emerging from the increasingly expansive and sophisticated roles computational modeling is playing in the natural and social sciences. Many of these issues concern the adequacy of more traditional philosophical descriptions of scientific practice and accounts of justification for handling computational science, particularly the role of theory in the generation and justification of physical models. However, certain novel issues are also becoming increasingly prominent as a result of the spread of computational approaches, such as nontheory-driven simulations , computational methods of inference, and the important, but often ignored, role of cognitive processes in computational model building.

Most of the philosophical literature on models and simulations focuses on computational simulation, and this is the focus of our review. However, we wish to note that the chief distinguishing characteristic between a model and a simulation (model) is that the latter is dynamic. They can be run either as constructed or under a range of experimental conditions. Thus, the broad class of simulation models should be understood as comprising dynamic physical models and mental models, topics considered elsewhere in this volume.

This chapter is organized as follows. First in Sect. 5.1 we discuss simulation in the context of well-developed theory (usually physics-based simulations). Then in Sect. 5.2 we discuss simulation in contexts where there are no over-arching theories of the phenomena, notably agent-based simulations and those in systems biology. We then turn to issues of whether and how simulation modeling introduces novel concerns for the philosophy of science in Sect. 5.3. Finally, we conclude in Sect. 5.4 by addressing the question of the relation between human cognition and computational simulation, including the relationship between the latter and thought experimenting.


System Biology Computational Simulation Cognitive Enhancement Semantic View Computational Tractability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

integrative systems biology



We gratefully acknowledge the support of the US National Science Foundation grant DRL097394084. Our analysis has benefited from collaboration with members of the Cognition and Learning in Interdisciplinary Cultures (CLIC) Research Group at the Georgia Institute of Technology, especially with Sanjay Chandrasekharan. Miles MacLeod’s participation was also supported by a postdoctoral fellowship at the TINT Center, University of Helsinki.


  1. 1.
    E. Winsberg: Sanctioning models: The epistemology of simulation, Sci. Context 12(2), 275–292 (1999)CrossRefGoogle Scholar
  2. 2.
    E. Winsberg: Models of success vs. the success of models: Reliability without truth, Synthese 152, 1–19 (2006)CrossRefGoogle Scholar
  3. 3.
    E. Winsberg: Computer simulation and the philosophy of science, Philos. Compass 4/5, 835–845 (2009)CrossRefGoogle Scholar
  4. 4.
    E. Winsberg: Science in the Age of Computer Simulation (Univ. of Chicago Press, Chicago 2010)CrossRefGoogle Scholar
  5. 5.
    E. Winserg: Computer simulations in science. In: The Stanford Encyclopedia of Philosophy, ed. by E.N. Zalta (Stanford Univ., Stanford 2014), Google Scholar
  6. 6.
    N. Cartwright: The Dappled World: A Study of the Boundaries of Science (Cambridge Univ. Press, Cambridge 1999)CrossRefzbMATHGoogle Scholar
  7. 7.
    M.S. Morgan, M. Morrison: Models as mediating instruments. In: Models as Mediators: Perspectives on Natural and Social Science, ed. by M.S. Morgan, M. Morrison (Cambridge Univ. Press, Cambridge 1999)CrossRefGoogle Scholar
  8. 8.
    J. Lenhard: Computer simulation: The cooperation between experimenting and modeling, Philos. Sci. 74(2), 176–194 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    E. Winsberg: Simulations, models, and theories: Complex physical systems and their representations, Philos. Sci. 68(3), 442–454 (2001)CrossRefGoogle Scholar
  10. 10.
    W. Parker: Computer simulation through an error-statistical lens, Synthese 163(3), 371–384 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    P. Humphreys: Extending Ourselves: Computational Science, Empiricism, and Scientific Method (Oxford Univ. Press, New York 2004)CrossRefGoogle Scholar
  12. 12.
    P. Humphreys: The philosophical novelty of computer simulation methods, Synthese 169, 615–626 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    W. Parker: Computer simulation. In: The Routledge Companion to Philosophy of Science, ed. by S. Psillos, M. Curd (Routledge, London 2013) pp. 135–145Google Scholar
  14. 14.
    E. Fox Keller: Models, simulation, and computer experiments. In: The Philosophy of Scientific Experimentation, ed. by H. Radder (Univ. of Pittsburgh Press, Pittsburgh 2003) pp. 198–215Google Scholar
  15. 15.
    S. Peck: Agent-based models as fictive instantiations of ecological processes, Philos. Theory Biol. 4, 1–12 (2012)Google Scholar
  16. 16.
    T. Grüne-Yanoff, P. Weirich: Philosophy of simulation, simulation and gaming, Interdiscip. J. 41(1), 1–31 (2010)Google Scholar
  17. 17.
    M.A. Bedau: Weak emergence and computer simulation. In: Models, Simulations, and Representations, ed. by P. Humphreys, C. Imbert (Routledge, New York 2011) pp. 91–114Google Scholar
  18. 18.
    S. Peck: The Hermeneutics of ecological simulation, Biol. Philos. 23(3), 383–402 (2008)CrossRefGoogle Scholar
  19. 19.
    R. Frigg: Models and fiction, Synthese 172(2), 251–268 (2010)CrossRefGoogle Scholar
  20. 20.
    T. Grüne-Yanoff: The explanatory potential of artificial societies, Synthese 169(3), 539–555 (2009)CrossRefGoogle Scholar
  21. 21.
    M. MacLeod, N.J. Nersessian: Building simulations from the ground-up: Modeling and theory in systems biology, Philos. Sci. 80(4), 533–556 (2013)CrossRefGoogle Scholar
  22. 22.
    E.O. Voit: Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists (Cambridge Univ. Press, Cambridge 2000)Google Scholar
  23. 23.
    M. MacLeod, N.J. Nersessian: The creative industry of systems biology, Mind Soc. 12, 35–48 (2013)CrossRefGoogle Scholar
  24. 24.
    S. Chandrasekharan, N.J. Nersessian: Building cognition: The construction of external representations for discovery, Cogn. Sci. 39(8), 1727–1763 (2015), doi: 10.1111/cogs.12203 CrossRefGoogle Scholar
  25. 25.
    S. Chandrasekharan, N.J. Nersessian: Building cognition: The construction of computational representations for scientific discovery, Cogn. Sci. 39(8), 1727–1763 (2015)CrossRefGoogle Scholar
  26. 26.
    H. Kitano: Looking beyond the details: A rise in system-oriented approaches in genetics and molecular biology, Curr. Genet. 41(1), 1–10 (2002)CrossRefGoogle Scholar
  27. 27.
    H.V. Westerhoff, D.B. Kell: The methodologies of systems biology. In: Systems Biology: Philosophical Foundations, ed. by F.C. Boogerd, F.J. Bruggeman, J.S. Hofmeyr, H.V. Westerhoff (Elsevier, Amsterdam 2007) pp. 23–70CrossRefGoogle Scholar
  28. 28.
    R. Frigg, J. Reiss: The philosophy of simulation: Hot new issues or same old stew, Synthese 169, 593–613 (2009)MathSciNetCrossRefGoogle Scholar
  29. 29.
    E. Winsberg: Simulated experiments: Methodology for a virtual world, Philos. Sci. 70(1), 105–125 (2003)CrossRefGoogle Scholar
  30. 30.
    D.G. Mayo: Error and the Growth of Experimental Knowledge (Univ. of Chicago Press, Chicago 1996)CrossRefGoogle Scholar
  31. 31.
    N. Gilbert, K. Troitzsch: Simulation for the Social Scientist (Open Univ. Press, Philadelphia 1999)Google Scholar
  32. 32.
    F. Guala: Models, simulations, and experiments. In: Model-based reasoning: Science, technology, values, ed. by L. Magani, N.J. Nersessian (Kluwer Academic/Plenum Publishers, New York 2002) pp. 59–74CrossRefGoogle Scholar
  33. 33.
    F. Guala: Paradigmatic experiments: The ultimatum game from testing to measurement device, Philos. Sci. 75, 658–669 (2008)CrossRefGoogle Scholar
  34. 34.
    M. Morgan: Experiments without material intervention: Model experiments, virtual experiments and virtually experiments. In: The Philosophy of Scientific Experimentation, ed. by H. Radder (University of Pittsburgh Press, Pittsburgh 2003) pp. 216–235Google Scholar
  35. 35.
    W. Parker: Does matter really matter? Computer simulations, experiments and materiality, Synthese 169(3), 483–496 (2009)CrossRefGoogle Scholar
  36. 36.
    E. Winsberg: A tale of two methods, Synthese 169(3), 575–592 (2009)CrossRefGoogle Scholar
  37. 37.
    M. MacLeod, N.J. Nersessian: Coupling simulation and experiment: The bimodal strategy in integrative systems biology, Stud. Hist. Philos. Sci. Part C 44, 572–584 (2013)CrossRefGoogle Scholar
  38. 38.
    W.S. Parker: Predicting weather and climate: Uncertainty, ensembles and probability, Stud. Hist. Philos. Sci. Part B 41(3), 263–272 (2010)CrossRefGoogle Scholar
  39. 39.
    W.S. Parker: Whose probabilities? Predicting climate change with ensembles of models, Philos. Sci. 77(5), 985–997 (2010)CrossRefGoogle Scholar
  40. 40.
    M. MacLeod, N.J. Nersessian: Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology, Stud. Hist. Philos. Sci. Part C 49(1), 1–11 (2015)CrossRefGoogle Scholar
  41. 41.
    J. Lenhard: Surprised by a nanowire: Simulation, control, and understanding, Philos. Sci. 73(5), 605–616 (2006) Google Scholar
  42. 42.
    N.J. Nersessian: Creating Scientific Concepts (MIT Press, Cambridge 2008)Google Scholar
  43. 43.
    N.J. Nersessian: How do engineering scientists think? Model-based simulation in biomedical engineering research laboratories, Top. Cogn. Sci. 1, 730–757 (2009)CrossRefGoogle Scholar
  44. 44.
    W. Callebaut: Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology, Stud. Hist. Philos. Sci. Part C 43(1), 69–80 (2012)MathSciNetCrossRefGoogle Scholar
  45. 45.
    J. Bohannon: Gamers unravel the secret life of protein, Wired 17 (2009),, Last accessed 06-06-2016
  46. 46.
    F. Khatib, F. DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, S. Cooper, M. Kazmierczyk, M. Gilski, S. Krzywda, H. Zabranska, I. Pichova, J. Thompson, Z. Popovic, M. Jaskolski, D. Baker: Crystal structure of a monomeric retroviral protease solved by protein folding game players, Nat. Struct. Mol. Biol. 18(10), 1175–1177 (2011)CrossRefGoogle Scholar
  47. 47.
    S. Chandrasekharan, N.J. Nersessian, V. Subramanian: Computational modeling: Is this the end of thought experiments in science? In: Thought Experiments in Philosophy, Science and the Arts, ed. by J. Brown, M. Frappier, L. Meynell (Routledge, London 2013) pp. 239–260Google Scholar
  48. 48.
    S. Chandrasekharan: Building to discover: A common coding model, Cogn. Sci. 33(6), 1059–1086 (2009)CrossRefGoogle Scholar
  49. 49.
    N.J. Nersessian: Engineering concepts: The interplay between concept formation and modeling practices in bioengineering sciences, Mind Cult. Activ. 19, 222–239 (2012)CrossRefGoogle Scholar
  50. 50.
    C.G. Langton: Self-reproduction in cellular automata, Physica D 10, 135–144 (1984)CrossRefzbMATHGoogle Scholar
  51. 51.
    C.G. Langton: Computation at the edge of chaos: Phase transitions and emergent computation, Physica D 42, 12–37 (1990)MathSciNetCrossRefGoogle Scholar
  52. 52.
    C. Reynolds: Flocks, herds, and schools: A distributed behavioral model, Comp. Graph. 21(4), 25–34 (1987)CrossRefGoogle Scholar
  53. 53.
    K. Sims: Evolving 3D morphology and behavior by competition, Artif. Life 1(4), 353–372 (1994)CrossRefGoogle Scholar
  54. 54.
    W. Banzhaf: Self-organization in a system of binary strings. In: Artificial Life IV, ed. by R. Brooks, P. Maes (MIT Press, Cambridge MA 2011) pp. 109–119Google Scholar
  55. 55.
    L. Edwards, Y. Peng, J. Reggia: Computational models for the formation of protocell structure, Artif. Life 4(1), 61–77 (1998)CrossRefGoogle Scholar
  56. 56.
    N.J. Nersessian, E. Kurz-Milcke, W.C. Newstetter, J. Davies: Research laboratories as evolving distributed cognitive systems, Proc. 25th Annu. Conf. Cogn. Sci. Soc. (2003) pp. 857–862Google Scholar
  57. 57.
    L. Osbeck, N.J. Nersessian: The distribution of representation, J. Theor. Soc. Behav. 36, 141–160 (2006)CrossRefGoogle Scholar
  58. 58.
    E. Hutchins: Cognition in the Wild (MIT Press, Cambridge 1995)Google Scholar
  59. 59.
    E. Hutchins: How a cockpit remembers its speeds, Cogn. Sci. 19(3), 265–288 (1995)CrossRefGoogle Scholar
  60. 60.
    E.A. Di Paolo, J. Noble, S. Bullock: Simulation models as opaque thought experiments. In: Artificial Life VII, ed. by M.A. Bedau, J.S. McCaskill, N.H. Packard, S. Rasmussen (MIT Press, Cambridge 2000) pp. 497–506Google Scholar
  61. 61.
    J. Lenhard: When experiments start. Simulation experiments within simulation experiments, Int. Workshop Thought Exp. Comput. Simul. (2010)Google Scholar
  62. 62.
    N.J. Nersessian: In the theoretician’s laboratory: Thought experimenting as mental modeling, Proc. Philos. Assoc. Am., Vol. 2 (1992) pp. 291–301Google Scholar
  63. 63.
    N.J. Nersessian, C. Patton: Model-based reasoning in interdisciplinary engineering. In: Handbook of the Philosophy of Technology and Engineering Sciences, ed. by A. Meijers (Elsevier, Amsterdam 2009) pp. 687–718Google Scholar
  64. 64.
    S. Chandrasekharan: Becoming knowledge: Cognitive and neural mechanisms that support scientific intuition. In: Rational Intuition: Philosophical Roots, Scientific Investigations, ed. by L.M. Osbeck, B.S. Held (Cambridge University Press, Cambridge 2014) pp. 307–337CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Dept. of Psychology, William James HallHarvard UniversityCambridgeUSA
  2. 2.Department of PhilosophyUniversity of TwenteEnschedeNetherlands

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