Skip to main content
SpringerLink
Log in

How can computer simulations produce new knowledge?

  • Original Paper in Philosophy of Science
  • Published:
European Journal for Philosophy of Science Aims and scope Submit manuscript

Abstract

It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some objections and reject the view that computer simulations produce knowledge because they are experiments. I conclude by comparing thought experiments and computer simulations, assuming that both are arguments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. The importance of this question is also stressed by Winsberg (1999), p. 277, Stöckler (2000), p. 366 and Barberousse et al. (2009), pp. 558–559.

  2. That thought experiments can produce new knowledge is the majority view in the philosophical literature; see e.g. Gendler (2004), p. 1153 and Cooper (2005), p. 328. A dissenting voice is Atkinson and Peijnenburg (2004). Moue et al. (2006) and Brown and Fehige (2010) review the current philosophical research literature about scientific thought experiments, see also Kühne (2005).

  3. See Brown (1991, 2004), Norton (1996, 2004a) for the most important contributions; see also Gendler (1998, 2000, 2004) and McAllister (2004).

  4. See Humphreys (1990), Hartmann (1996) for pioneering philosophical works about CSs, Humphreys (2004) and the essays collected in Frigg et al. (2009, 2011) and the monograph Winsberg (2010) for more recent philosophical contributions about CSs.

  5. Works that state this aim very clearly are Winsberg (1999), Stöckler (2000) and Barberousse et al. (2009).

  6. See Bailer-Jones (2003), Suárez (2003, 2004) and Giere (2004) for related discussions.

  7. Cf. Frigg and Reiss (2009), p. 596 for a similar distinction.

  8. For instance, Humphreys (2004), p. 71 draws a comparison between argument schemes and what he calls computational templates.

  9. See e.g. Perret-Gallix (2002), p. 488 for evidence; cf. also Galison (1997), particularly p. 689.

  10. Galileo’s TE can be found in Galilei (1933), Vol. VIII, 107–109 (see Galilei 1974 for a translation into English); see Brown (1991), p. 43, Norton (1996), pp. 340–345, Gendler (1998), Atkinson and Peijnenburg (2004), McAllister (2004) and Kühne (2005), pp. 41–57 for the philosophical discussion about Galileo’s TE. See Brown (1991), pp. 3–6 and Norton (1996), pp. 349–351 for Stevin’s TE. For philosophical thought experiments see e.g. Cohnitz (2006).

  11. See Norton (1991, 1996, 2004a, b) for articulation and defense; see Irvine (1991), pp. 149–150 for a similar view.

  12. Here the “context of discovery” has to be taken with some grain of salt. The context of discovery concerns what is done when a TE is run. It is not about the construction and first discovery of a TE. Norton’s argument does not say much about the construction of TEs. In this paper, I understand “context of discovery” in the sense in which Norton uses the term.

  13. But can we really gain new knowledge by running through an argument? Some might want to deny this because the conclusion of the argument was already entailed or supported by prior knowledge. If knowledge is closed under deduction and under sound inductive argument, we cannot obtain new knowledge by going through an argument. But the assumption that knowledge is closed in this way is not very plausible to begin with, and when we run through a sound argument, the conclusion can at least be new in the sense that we did not believe it before. If this psychological sense of novelty is not sufficient at this point, it should be pointed out what the stronger sense of novelty is and why thought experiments provide new knowledge in this stronger sense (cf. Norton 1996, p. 346).

  14. Such TEs include Newton’s bucket experiment, Stevin’s thought experiment (ibid., pp. 347–351) and an argument concerning the continuum hypothesis (see Norton 2004a, pp. 1147–1148).

  15. Very briefly, my own view is as follows: I agree with Norton that TEs can be reconstructed as arguments in some way. But it is naïve to think that we reason from the premises to the conclusion of this argument when we conduct a thought experiment on our own (i.e., if we think what may happen in a certain counterfactual scenario as pictured by a TE). At least in some kinds of TEs, we “know” more intuitively what will happen. This point has implications for both the context of justification and the context of discovery.

  16. Cf. Weisberg (2007)

  17. Checked 12/2011

  18. See e.g. Peebles (1980), Part II, Efstathiou et al. (1985), Bertschinger (1998), Klypin (2000) and Dolag et al. (2008) for such simulations. Hockney and Eastwood (1988) provide a general introduction to particle simulations.

  19. Balzer (2009), p. 324 assumes likewise that the inputs to, and the outputs of, simulations can be represented as sentences. –Since my example is from physics, I am speaking of physical characteristics such as mass etc. But nothing hinges on the characteristics being from physics, and in other examples we would be concerned with chemical characteristics etc.

  20. By stressing the empirical significance of the premises I do not mean to exclude statements about unobservables and their characteristics (provided we can refer to unobservables). Rather, what I call “empirical meaning” has to extend beyond purely mathematical significance and to refer to concrete systems in some however indirect way.

  21. I take the notion of an argument scheme from Kitcher (1981), Section 5.

  22. Note that we cannot generalize our initial reconstructing argument by quantifying over p and C in each premise. It is important that we first fix one system and coordinates and then go through the whole argument.

  23. See Press et al. (2007), Section 1.1 for an introduction.

  24. See e.g. Press et al. (2007), Section 1.1 again.

  25. See Press et al. (2007), p. 10 for an estimate.

  26. She will not even think that the original differential equations are literally true of the target because they rest upon idealizations. For the time being, I will bracket this fact, and return to it later on page 24.

  27. Approximation errors quantify the differences between the solutions to the difference equations and to the exact differential equations.

  28. See Press et al. (2007), p. 11 for this interaction.

  29. See e.g. Kronsjö (1979), pp. 1–2 for a definition of algorithms.

  30. As every utterance, the statement of results from simulations is subject to rules of pragmatics; see e.g. Grice (1989) for such rules.

  31. Cf. Stöckler (2000), p. 368. Note that D is not an analogue of what Norton (1991), p. 131 calls the elimination thesis and Gendler (1998), p. 401 calls the dispensibility thesis. The latter theses claim that thought experiments could be replaced using arguments that are not thought experiment-like.

  32. D does not imply that the reconstructing argument itself is dispensable. Also, the use of the computer is only dispensable in principle, but not in practice: Many epistemic tasks cannot be completed without computers in practice. As Humphreys (2004, Section 5.5; 2009, pp. 623–624) and Winsberg (2010), p. 5 point out, the perspective of what is possible in principle is often not relevant in the philosophy of science; and to the extent to which the perspective is irrelevant, D is not applicable.

  33. If we executed the reconstructed argument accurately, we would not obtain the values that the computer outputs, but values that do not suffer from any roundoff errors. That our values differ from the output by the computer does not pose any problem; for epistemological purposes, the simulation becomes certainly dispensable.

  34. Some simulation scientists, e.g. Oberkampf and Roy (2010), Chs. 10–11 recommend that validation include certain experiments, which they call validation experiments. Clearly, such experiments are not merely inferences. But if such experiments are run, it is not very plausible to take them to be literal part of a computer simulation study.

  35. This is a term that is introduced in CSs based upon the Navier-Stokes equations to smooth out shocks. Without eddy viscosity, the simulations can be caught in numerical problems. See e.g. Winsberg (2010), pp. 13–15.

  36. See page 13 above.

  37. Statements that refer to non-existing particles rest upon false presuppositions. They are either wrong, as suggested by Russell (1905) or have a truth gap (Strawson 1950). In either case, they are not true and it seems spurious how they should constitute knowledge.

  38. An argument with false premises and conclusions can of course generate new knowledge if the conclusion is known to be false and used to reject the premises. But we are here concerned with a different case; we refer to arguments the premises and conclusions of which are obviously wrong, e.g. because they assume the wrong type of ontology. Also, the focus of this paper is on simulations the results or conclusions of which produce new knowledge.

  39. According to Weisberg (2007), the analysis of a model is a crucial step in modeling.

  40. See Clark (2007) for another defense of the view. Consult the essays collected in Menary (2010) for more discussion.

  41. For other very interesting reflections about reasoning see Grice (2001).

  42. Reasoning can also lead one to abandon a mental state, but we can safely bracket this case for our purposes.

  43. Wedgwood only introduces basic steps of reasoning to exclude external deviant causal chains (ibid., pp. 660–665). I do not think it to be absolutely necessary that the steps are “atomic” in that no analysis into other steps of reasoning is possible. We have only to guarantee that the basic steps entirely belong to the cognitive system. If this is right, it is immaterial whether the basic steps are “atomic” or not.

  44. In this way, we obtain an argument in which the algorithm corresponds to many premises. I have suggested on p. 20 that we can always unpack the algorithm into a larger number of assumptions or premises.

  45. One may object that Wedgwood (2006) take pains to isolate the reasoner from her environment in order to rule out external deviant causal chains (ibid., pp. 665–666). This may suggest that Wedgwood’s account is only applicable to human reasoners. I do not agree with this objection. What Wedgwood has to achieve if the account is to work, is to isolate the cognitive system that is the bearer of some chain of reasoning. If the extended mind thesis is true, then cognitive systems may extend beyond a human being.

  46. Recall page 19.

  47. See the quotation on my page 8 above.

  48. E.g. Gendler (2004), p. 1154.

  49. I am here in substantial agreement with Barberousse et al. (2009). See also Giere (2009) for a criticism of Morrison’s argument.

  50. Recall Humphreys’s point that CSs are the modern successors of TEs (see my page 2 above and Humphreys 2004, p. 115).

  51. Cf. the discussion about Galileo’s TE with the falling bodies; see e.g. Atkinson and Peijnenburg (2004).

  52. See Fine (2009) and references therein for philosophical discussion about the EPR argument.

  53. Cf. Lenhard and Winsberg (2010).

  54. Explanations are arguments according to the DN-model of explanation (Hempel and Oppenheim 1948). Even though many objections have been levelled against the DN-model (see Salmon 1989, particularly pp. 46–50, for an overview), most of them do not cast doubts on the idea that explanations are arguments. That explanations provide arguments is also important for e.g. the unificationist view of explanation (see Friedman 1974 and Kitcher 1981 for classic references).

  55. See Friedman (1974) for the connection between explanation and understanding.

  56. For an interesting assessment of the explanatory power of artificial society simulations see Grüne-Yanoff (2009). See also Weber (1999) for simulations and explanation.

  57. Cf. Suárez (2004)

  58. The paper by Barberousse et al. (2009) has a lot to say about the process of running simulations, but it does not end up with a clear statement about how this process produces knowledge.

  59. See Gillespie (1976) and Gillespie (1977) for famous examples of Monte Carlo simulations.

References

  • Atkinson, D., & Peijnenburg, J. (2004). Galileo and prior philosophy. Studies in History and Philosophy of Science, 35, 115–136.

    Article  Google Scholar 

  • Bailer-Jones, D. M. (2003). When scientic models represent. International Studies in the Philosophy of Science, 17, 59–75.

    Article  Google Scholar 

  • Balzer, W. (2009). Die Wissenschaft und ihre M ethoden (2nd ed.). Karl Alber, Freiburg und München.

    Google Scholar 

  • Barberousse, A., Franceschelli, S., & Imbert, C. (2009). Computer simulations as experiments. Synthese, 169, 557–574.

    Article  Google Scholar 

  • Bartuccelli, M. V., Gentile, G., & Georgiou, K. (2001). On the dynamics of a vertically-driven damped planar pendulum. Proceedings of the Royal Society of London, Series A, 457, 1–16.

    Article  Google Scholar 

  • Bertschinger, E. (1998). Simulations of structure formation in the universe. Annual Review of Astronomy and Astrophysics, 36, 599–654.

    Article  Google Scholar 

  • Bishop, M. A. (1999). Why thought experiments are not arguments. Philosophy of Science, 66, 543–541.

    Article  Google Scholar 

  • Brown, J. R., & Fehige, Y. (2010). Thought experiments. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (winter 2010 ed.).

  • Brown, J. R. (1991). The laboratory of the mind: Thought experiments in the natural sciences. Routledge, London.

    Google Scholar 

  • Brown, J. R. (2004). Peeking into Plato’s haeven. Philosophy of Science, 71, 1126–1138.

    Article  Google Scholar 

  • Clark, A. (2007). Curing cognitive hiccups: A defense of the extended mind. Journal of Philosophy, 104, 163–192.

    Google Scholar 

  • Clark, A., & Chalmers, D. J. (1998). The extended mind. Analysis, 58(1), 7–19.

    Article  Google Scholar 

  • Cohnitz, D. (2006). Gedankenexperimente in der Philosophie. Mentis, Paderborn.

    Google Scholar 

  • Cooper, R. (2005). Thought experiments. Metaphilosophy, 3, 328–347.

    Article  Google Scholar 

  • Dolag, K., Borgani, S., Schindler, S., Diaferio, A., & Bykov, A. M. (2008). Simulation techniques for cosmological simulations. Space Science Reviews, 134, 229–268. Preprint under 0801.1023v1.

  • Efstathiou, G., Davis, M., White, S. D. M., & Frenk, C. S. (1985). Numerical techniques for large cosmological N-body simulations. Astrophysical Journal Supplement Series, 57, 241–260.

    Article  Google Scholar 

  • Einstein, A., Podolsky, B., & Rosen, N. (1935). Can quantum-mechanical description of physical reality be considered complete? Physical Review, 47, 777–780.

    Article  Google Scholar 

  • Einstein, A. (1961). Relativity, the special and the g eneral theory. A popular e xposition. Methuen, London, 1920, here quoted after edition published by Crown, New York.

  • Fine, A. (2009). The Einstein-Podolsky-Rosen argument in quantum theory. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2009 ed.). http://plato.stanford.edu/archives/fall2009/entries/qt-epr/.

  • Frankfurt, H. G. (1978). The problem of action. American Philosophical Quarterly, 15, 157–62.

    Google Scholar 

  • Friedman, M. (1974). Explanation and scientific understanding. Journal of Philosophy, 71, 5–19.

    Article  Google Scholar 

  • Frigg, R. P., & Reiss, J. (2009). The philosophy of simulation: Hot new issues or same old stew? Synthese, 169, 593–613.

    Article  Google Scholar 

  • Frigg, R. P., Hartmann, S., & Imbert, C. (Eds.) (2009). Models and simulations. Special Issue. Synthese (Vol. 169, pp. 425–626).

  • Frigg, R. P., Hartmann, S., & Imbert, C. (Eds.) (2011). Models and simulations 2. Special Issue. Synthese (Vol. 180, pp. 1–77).

  • Galilei, G. (1933). Le Opere di G alileo Galilei. Florence: G. Barbèra.

    Google Scholar 

  • Galilei, G. (1974). Two new sciences. Translation by S. Drake. Madison (WI): University of Wisconsin Press.

  • Galison, P. (1996). Computer simulations and the trading zone. In P. Galison & D. J. Stump (Eds.), The disunity of science. Boundaries, contexts, and power (pp. 118–157). Stanford: Stanford University Press.

    Google Scholar 

  • Galison, P. (1997). Image and logic. A materical culture of microphysics. Chicago: University of Chicago Press.

    Google Scholar 

  • Gendler, T. S. (1998). Galileo and the indispensability of scientific thought experiment. British Journal for the Philosophy of Science, 49, 397–424.

    Article  Google Scholar 

  • Gendler, T. S. (2000). Thought experiment. O n the powers and l imits of imaginary c ases. New York: Garland Publishing.

    Google Scholar 

  • Gendler, T. S. (2004). Thought experiments rethought and reperceived. Philosophy of Science, 71, 1152–1163.

    Article  Google Scholar 

  • Giere, R. N. (2004). How models are used to represent. Philosophy of Science, 71, 742–752.

    Article  Google Scholar 

  • Giere, R. N. (2009). Is computer simulation changing the face of experimentation? Philosophical Studies, 143(1), 59–62.

    Article  Google Scholar 

  • Gillespie, D. T. (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics, 22, 403–434.

    Article  Google Scholar 

  • Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81, 2340–2361.

    Article  Google Scholar 

  • Gramelsberger, G. (2010). Computerexperimente. Zum W andel der Wissenschaft im Z eitalter des Computers. Transcript, Bielefeld.

    Google Scholar 

  • Gramelsberger, G. (2011a). Generation of evidence in simulation runs: Interlinking with models for predicting weather and climate change. Simulation & Gaming, 42(2), 212–224.

    Article  Google Scholar 

  • Gramelsberger, G. (2011b). What do numerical (climate) models really represent?. Studies in History and Philosophy of Science, Part A 42, 296–302. Model-Based Representation in Scientific Practice.

    Article  Google Scholar 

  • Grice, P. (1989). Logic and conversation. In H. P. Grice (Ed.), Studies in the ways of w ords (pp. 1–143). Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Grice, P. (2001). Aspects of reason. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Grüne-Yanoff, T. (2009). The explanatory potential of artificial societies. Synthese, 169, 539–555.

    Article  Google Scholar 

  • Hartmann, S. (1996). The world as a process: Simulations in the natural and social sciences. In R. Hegselmann, K. G. Troitzsch, & U. Mueller (Eds.), Modelling and simulation in the social sciences from the philosophy of science point of view (pp. 77–100). Dordrecht: Kluwer. Quoted from the revised version at http://philsci-archive.pitt.edu/archive/00002412/.

    Google Scholar 

  • Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15, 135–175.

    Article  Google Scholar 

  • Hockney, R. W., & Eastwood, J. W. (1988). Computer simulation using particles (special student ed.). ed., Adam Hilger, Bristol etc.

  • Humphreys, P. (1990). Computer simulations. In PSA: Proceedings of the biennial meeting of the philosophy of science association 1990 (pp. 497–506) (English).

  • Humphreys, P. (2004). Extending ourselves: C omputational science, e mpiricism, and scientific m ethod. New York: Oxford University Press.

    Google Scholar 

  • Humphreys, P. (2009). The philosophical novelty of computer simulation methods, Synthese, 169, 615–626.

    Article  Google Scholar 

  • Irvine, A. D. (1991). Thought experiments in scientific reasoning. In: T. Horowitz & G. J. Massey (Eds.), Thought experiments in science and philosophy (pp. 149–165). Savage, MD: Rowman and Littlefield.

    Google Scholar 

  • Kitcher, P. (1981). Explanatory unification. Philosophy of Science, 48, 507–531.

    Article  Google Scholar 

  • Klypin, A. (2000). Numerical simulations in cosmology I: Methods. astro-ph/0005502.

  • Kronsjö, L. (1979). Algorithms: Their c omplexity and efficiency. Chichester: Wiley.

    Google Scholar 

  • Kuhn, T. S. (1964). A function for thought experiments. In L’Aventure de la S cience, Mé langes Alexandre K oyré, Hermann, Paris (Vol. 2). (Reprinted in Kuhn, T. S., The Essential Tension: Selected Studies in Scientific Tradition and Change. Chicago: University of Chicago Press, 1977, pp. 240–265, 307–334).

  • Kühne, U. (2005). Die Methode des G edankenexperiments. Suhrkamp, Frankfurt am Main.

    Google Scholar 

  • Küppers, G., & Lenhard, J. (2005a). Computersimulationen: Modellierungen 2. Ordnung. Journal for General Philosophy of Science, 36(2), 305–329.

    Article  Google Scholar 

  • Küppers, G., & Lenhard, J. (2005b). Validation of simulation: Patterns in the social and natural sciences. Journal of Artificial Societies and Social Simulation, 8(4), 3.

    Google Scholar 

  • Lenhard, J. (2007). Computer simulation: The cooperation between experimenting and modeling. Philosophy of Science, 74, 176–194.

    Article  Google Scholar 

  • Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B, 41(3), 253–262.

    Article  Google Scholar 

  • Mach, E. (1926). Erkenntnis und Irrtum. S kizzen zur Psychologie der F orschung (5th ed.). Johann Ambrosius Barth, Leipzig (coincides with the 4th ed.).

  • McAllister, J. (2004). Thought experiments and the belief in phenomena. Philosophy of Science, 71, 1164–1175.

    Article  Google Scholar 

  • Menary, R. (Ed.) (2010). The extended m ind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Morrison, M. (2009). Models, measurement and computer simulation: The changing face of experimentation. Philosophical Studies, 143, 33–57.

    Article  Google Scholar 

  • Moue, A., Masavetas, K. A., & Karyianni, H. (2006). Tracing the development of thought experiments in the philosophy of natural sciences. Journal for General Philosophy of Science, 37, 61–75.

    Article  Google Scholar 

  • Norton, S. D., & Suppe, F. (2001). Why atmospheric modeling is good science. In P. Edwards & C. Miller (Eds.), Changing the atmosphere (pp. 67–106). Cambridge, MA: MIT Press.

    Google Scholar 

  • Norton, J. D. (1991). Thought experiments in Einstein’s work. In T. Horowitz & G. J. Massey (Eds.), Thought experiments in science and philosophy (pp. 129–144). Savage, MD: Rowman and Littlefield.

    Google Scholar 

  • Norton, J. D. (1996). Are thought experiments just what you thought? Canadian Journal of Philosophy, 26, 333–366.

    Google Scholar 

  • Norton, J. D. (2004a). On thought experiments: Is there more to the argument? Proceedings of the 2002 Biennial Meeting of the Philosophy of Science Association, Philosophy of Science, 71, 1139–1151.

    Google Scholar 

  • Norton, J. D. (2004b). Why thought experiments do not transcend empiricism. In C. Hitchcock (Ed.), Contemporary debates in the philosophy of science (pp. 44–66). Oxford: Blackwell.

    Google Scholar 

  • Oberkampf, W. L., & Roy, C. J. (2010). Verification and validation in scientific computing. Cambridge: Cambridge University Press.

    Google Scholar 

  • Pang, T. (2006). An introduction to computational physics (2nd ed.). Cambridge, MA: Cambridge University Press.

    Book  Google Scholar 

  • Parker, W. S. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169(3), 483–496.

    Article  Google Scholar 

  • Peebles, P. J. E. (1980). The large scale structure of the universe. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Perret-Gallix, D. (2002). Simulation and event generation in high-energy physics. Computer Physics Communications, 147(1–2), 488–493.

    Article  Google Scholar 

  • Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical recipes. T he art of s cientific computing (3rd ed.), New York: Cambridge University Press.

    Google Scholar 

  • Russell, B. (1905). On denoting. Mind, 14, 479–493. (Reprinted in Russell, Bertrand, Essays in Analysis, London: Allen & Unwin, pp. 103–119 (1973))

  • Salmon, W. (1989). Four decades of s cientific explanation. Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Soter, S. (2007). Are planetary systems filled to capacity? Scientific American, 95(5), 424.

    Google Scholar 

  • Stöckler, M. (2000). On modeling and simulations as instruments for the study of complex systems. In M. Carrier, G. J. Massey, & L. Ruetsche (Eds.), Science at the century’s end: Philosophical questions on the progress and limits of science (pp. 355–373). Pittsburgh, PA: University of Pittsburgh Press.

    Google Scholar 

  • Strawson, P. F. (1950). On referring. Mind, 59, 320–344.

    Article  Google Scholar 

  • Suárez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17, 225–244.

    Article  Google Scholar 

  • Suárez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71, 767–779.

    Article  Google Scholar 

  • Tymoczko, T. (1979). The four-color problem and its philosophical significance. Journal of Philosophy, 76, 57–83.

    Article  Google Scholar 

  • Weber, K. (1999). Simulation und Erklärung. Waxmann, Münster.

    Google Scholar 

  • Wedgwood, R. (2006). The normative force of reasoning. Noũs, 40, 660–686.

    Google Scholar 

  • Weisberg, M. (2007). Who is a modeler? British Journal for Philosophy of Science, 58, 207–233.

    Article  Google Scholar 

  • Winsberg, E. (1999). Sanctioning models. The Epistemology of Simulation. Science in Context, 12, 275–292.

    Article  Google Scholar 

  • Winsberg, E. (2001). Simulations, models, and theories: Complex physical systems and their representations. Philosophy of Science (Proceedings), 68, 442–454.

    Google Scholar 

  • Winsberg, E. (2010). Science in the age of computer simulations. Chicago: University of Chicago Press.

    Google Scholar 

Download references

Acknowledgements

An earlier version of this paper was presented at the workshop “Thought experiments and Computer Simulations” at the IHPST, Paris in March 2010. I’m grateful to the organizers and the other participants. Special thanks to John Norton for discussion and encouragement.

Author information

Authors and Affiliations

  1. Institute for Philosophy and Political Science, TU Dortmund, 44221, Dortmund, Germany

    Claus Beisbart

Authors
  1. Claus Beisbart
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claus Beisbart.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Beisbart, C. How can computer simulations produce new knowledge?. Euro Jnl Phil Sci 2, 395–434 (2012). https://doi.org/10.1007/s13194-012-0049-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13194-012-0049-7

Keywords

Search

Navigation