Scientific Models Are Not Fictions

Model-Based Science as Epistemic Warfare
  • Lorenzo Magnani
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 2)

Abstract

In the current epistemological debate scientific models are not only considered as useful devices for explaining facts or discovering new entities, laws, and theories, but also rubricated under various new labels: from the classical ones, as abstract entities and idealizations, to the more recent, as fictions, surrogates, credible worlds, missing systems, make-believe, parables, functional, epistemic actions, revealing capacities. The paper discusses these approaches showing some of their epistemological inadequacies, also taking advantage of recent results in cognitive science. The main aim is to revise and criticize fictionalism, also reframing the received idea of abstractness and ideality of models with the help of recent results coming from the area of distributed cognition (common coding) and abductive cognition (manipulative). The article also illustrates how scientific modeling activity can be better described taking advantage of the concept of “epistemic warfare”, which sees scientific enterprise as a complicated struggle for rational knowledge in which it is crucial to distinguish epistemic (for example scientific models) from non epistemic (for example fictions, falsities, propaganda) weapons. Finally I will illustrate that it is misleading to analyze models in science by adopting a confounding mixture of static and dynamic aspects of the scientific enterprise. Scientific models in a static perspective (for example when inserted in a textbook) certainly appear fictional to the epistemologist, but their fictional character disappears in case a dynamic perspective is adopted. A reference to the originative role of thought experiment in Galileo’s discoveries and to usefulness of Feyerabend’s counterinduction in criticizing the role of resemblance in model-based cognition is also provided, to further corroborate the thesis indicated by the article title.

Keywords

Thought Experiment Target System Common Code Rational Knowledge Heavy Body 
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.

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References

  1. Arcangeli, M.: Imagination in Thought Experimentation: Sketching a Cognitive Approach to Thought Experiments. In: Magnani, L., Carnielli, W., Pizzi, C. (eds.) Model-Based Reasoning in Science and Technology. SCI, vol. 314, pp. 571–587. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. Bardone, E.: Seeking Chances. From Biased Rationality to Distributed Cognition. Springer, Heidelberg (2011)Google Scholar
  3. Bardone, E.: Not by Luck Alone: The Importance of Chance-Seeking and Silent Knowledge in Abductive Cognition. In: Magnani, L., Li, L. (eds.) Philosophy and Cognitive Science. SAPERE, vol. 2, pp. 187–205. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. Barsalou, L.W.: Cognitive and neural contributions to understanding the conceptual system. Current Directions in Psychological Science 17(2), 91–95 (2008)CrossRefGoogle Scholar
  5. Barsalou, L.W.: Grounded cognition. Annual Review of Psychology 59, 617–645 (2008)CrossRefGoogle Scholar
  6. Bertolotti, T.: From Mindless Modeling to Scientific Models. The Case of Emerging Models. In: Magnani, L., Li, L. (eds.) Philosophy and Cognitive Science. SAPERE, vol. 2, pp. 77–106. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. Bokulich, A.: How scientific models can explain. Synthese 1, 33–45 (2011)CrossRefGoogle Scholar
  8. Boumans, M.J.: Mathematics as quasi-matter to build models as instruments. In: Weber, M., Dieks, D., Gonzalez, W.J., Hartman, S., Stadler, F., Stöltzner, M. (eds.) Probabilities, Laws, and Structures, pp. 307–318. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. Bueno, O., French, S.: How theories represent. The British Journal for the Philosophy of Science (2011), doi:10.1093/bjps/axr010Google Scholar
  10. Cartwright, N.: How the Laws of Physics Lie. Oxford University Press, Oxford (1983)CrossRefGoogle Scholar
  11. Cartwright, N.: Nature’s Capacities and Their Measurement. Oxford University Press, Oxford (1989)Google Scholar
  12. Cartwright, N.: If no capacities then no credible worlds. But can models reveal capacities? Erkenntnis 70, 45–58 (2009)CrossRefGoogle Scholar
  13. Cartwright, R.: Models: Parables v. fables. Insights 1(8), 2–10 (2009)Google Scholar
  14. Chakravartty, A.: Informational versus functional theories of scientific representation. Synthese 172, 197–213 (2010)CrossRefGoogle Scholar
  15. Chandrasekharan, S.: Building to discover: a common coding model. Cognitive Science 33, 1059–1086 (2009)CrossRefGoogle Scholar
  16. Clark, K.L.: Negation as failure. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp. 94–114. Plenum, New York (1978)Google Scholar
  17. Contessa, G.: Scientific representation, interpretation, and surrogative reasoning. Philosophy of Science 74, 48–68 (2007)CrossRefGoogle Scholar
  18. Contessa, G.: Scientific models and fictional objects. Synthese 172, 215–229 (2010)CrossRefGoogle Scholar
  19. da Costa, N.C., French, S.: Science and Partial Truth. A Unitary Approach to Models and Scientific Reasoning. Oxford University Press, Oxford (2003)Google Scholar
  20. de Cruz, H., de Smedt, J.: Mathematical symbols as epistemic actions. Synthese (2011), doi:10.1007/s11229-010-9837-9Google Scholar
  21. Feyerabend, P.: Against Method. Verso, London-New York (1975)Google Scholar
  22. Fine, A.: Fictionalism. In: Suárez, M. (ed.) Fictions in Science: Philosophical Essays on Modeling and Idealization, pp. 19–36. Routledge, London (2009)Google Scholar
  23. French, S.: Keeping quiet on the ontology of models. Synthese 172, 231–249 (2010)CrossRefGoogle Scholar
  24. Freud, S.: The Standard Edition of the Complete Psychological Works of Sigmund Freud. Hogarth Press, London (1953); Translated by Strachey, J. in collaboration with Freud, A., et al.Google Scholar
  25. Frigg, R.: Fiction and scientific representation. In: Frigg, R., Hunter, M.C. (eds.) Beyond Mimesis and Nominalism: Representation in Art and Science, pp. 97–138. Springer, Heidelberg (2010)Google Scholar
  26. Frigg, R.: Fiction in science. In: Woods, J. (ed.) Fictions and Models: New Essays, pp. 247–287. Philosophia Verlag, Munich (2010)Google Scholar
  27. Frigg, R.: Models and fiction. Synthese 172, 251–268 (2010)CrossRefGoogle Scholar
  28. Galilei, G.: Dialogues Concerning Two New Sciences (1638). Mac Millan, New York (1914); Translated from the Italian and Latin by Crew, H., De Salvio, A. Introduction by Favaro, A. Original title Discorsi e dimostrazioni matematiche, intorno a due nuove scienze, Discourses and Mathematical Demonstrations Relating to Two New SciencesGoogle Scholar
  29. Galilei, G.: The Assayer (1623). In: Drake, S. (ed. & trans.) Discoveries and Opinions of Galileo, pp. 231–280. Doubleday, New York (1957)Google Scholar
  30. Gendler, T.S.: Galileo and the indispensability of scientific thought experiment. The British Journal for the Philosophy of Science 49(3), 397–424 (1998)CrossRefGoogle Scholar
  31. Gendler, T.S.: Thought experiments rethought – and reperceived. Philosophy of Science 71, 1152–1164 (2004)CrossRefGoogle Scholar
  32. Giere, R.N.: Explaining Science: a Cognitive Approach. University of Chicago Press, Chicago (1988)Google Scholar
  33. Giere, R.: An agent-based conception of models and scientific representation. Synthese 172, 269–281 (2007)CrossRefGoogle Scholar
  34. Giere, R.: Why scientific models should not be regarded as works of fiction. In: Suárez, M. (ed.) Fictions in Science. Philosophical Essays on Modeling and Idealization, pp. 248–258. Routledge, London (2009)Google Scholar
  35. Godfrey-Smith, P.: The strategy of model-based science. Biology and Philosophy 21, 725–740 (2006)CrossRefGoogle Scholar
  36. Godfrey-Smith, P.: Models and fictions in science. Philosophical Studies 143, 101–116 (2009)CrossRefGoogle Scholar
  37. Hintikka, J.: What is abduction? The fundamental problem of contemporary epistemology. Transactions of the Charles S. Peirce Society 34, 503–533 (1998)Google Scholar
  38. Hutchins, E.: Cognitive artifacts. In: Wilson, R.A., Keil, F.C. (eds.) Encyclopedia of the Cognitive Sciences, pp. 126–127. The MIT Press, Cambridge (1999)Google Scholar
  39. Kant, I.: Critique of Pure Reason. MacMillan, London (1929); Translated by Kemp Smith, N. originally published (1787), reprint (1998)Google Scholar
  40. Kirsh, D., Maglio, P.: On distinguishing epistemic from pragmatic action. Cognitive Science 18, 513–549 (1994)CrossRefGoogle Scholar
  41. Kuorikoski, J., Lehtinen, A.: Incredible worlds, credible results. Erkenntnis 70, 119–131 (2009)CrossRefGoogle Scholar
  42. Magnani, L.: Abduction, Reason, and Science. Processes of Discovery and Explanation. Kluwer Academic/Plenum Publishers, New York (2001)CrossRefGoogle Scholar
  43. Magnani, L.: Philosophy and Geometry.Theoretical and Historical Issues. Kluwer Academic Publisher, Dordrecht (2001)Google Scholar
  44. Magnani, L.: Conjectures and manipulations. Computational modeling and the extra-theoretical dimension of scientific discovery. Minds and Machines 14, 507–537 (2004)CrossRefGoogle Scholar
  45. Magnani, L.: Model-based and manipulative abduction in science. Foundations of science 9, 219–247 (2004)CrossRefGoogle Scholar
  46. Magnani, L.: Abduction and chance discovery in science. International Journal of Knowledge-Based and Intelligent Engineering 11, 273–279 (2007)Google Scholar
  47. Magnani, L.: Abductive Cognition. The Epistemological and Eco-Cognitive Dimensions of Hypothetical Reasoning. Springer, Heidelberg (2009)Google Scholar
  48. Magnani, L.: Understanding Violence. The Interwining of Morality, Religion, and Violence: A Philosophical Stance. Springer, Heidelberg (2011)Google Scholar
  49. Mäki, U.: MISSing the world. Models as isolations and credible surrogate systems. Erkenntnis 70, 29–43 (2009)CrossRefGoogle Scholar
  50. Manders, K.: The Euclidean diagram. In: Mancosu, P. (ed.) Philosophy of Mathematical Practice, pp. 112–183. Clarendon Press, Oxford (2008)Google Scholar
  51. Miller, G.A.: Mistreating psychology in the decades of brain. Perspectives on Psychological Science 5, 716–743 (2010)CrossRefGoogle Scholar
  52. Mizrahi, M.: Idealizations and scientific understanding. Philosophical Studies (2011), http://www.springerlink.com/content/e33h421502t20118/, doi: 10.1007/s11098-011-9716-3
  53. Morrison, M.: Fictions, representations, and reality. In: Suárez, M. (ed.) Fictions in Science: Philosophical Essays on Modeling and Idealization, pp. 110–135. Routledge, London (2009)Google Scholar
  54. Mumma, J.: Proofs, pictures, and Euclid. Synthese 175, 255–287 (2010)CrossRefGoogle Scholar
  55. Nersessian, N.J., Chandradekharan, S.: Hybrid analogies in conceptual innovation in science. Cognitive Systems Research 10(3), 178–188 (2009)CrossRefGoogle Scholar
  56. Nersessian, N.J.: In the theoretician’s laboratory: thought experimenting as mental modelling. In: Hull, D., Forbes, M., Okruhlik, K. (eds.) PSA 1992, East Lansing, MI, vol. 2, pp. 291–301. Philosophy of Science Association (1993)Google Scholar
  57. Newton, I.: Philosophiae Naturalis Principia Mathematica. General Scholium (1726), 3rd edn. Cohen, I. B., Whitman, A. (trans.) University of California Press, Berkeley (1999)Google Scholar
  58. Odling-Smee, F.J., Laland, K.N., Feldman, M.W.: Niche Construction. The Neglected Process in Evolution. Princeton University Press, Princeton (2003)Google Scholar
  59. Park, W.: Abduction and estimation in animals. Foundations of Science (2011), doi: 10.1007/s10699-011-9275-2Google Scholar
  60. Peirce, C.S.: Collected Papers of Charles Sanders Peirce. In: Hartshorne, C., Weiss, P. (eds.) vol. 1-6; Burks, A.W. (ed.) vol. 7-8. Harvard University Press, Cambridge (1931-1958)Google Scholar
  61. Portides, D.P.: The relation between idealization and approximation in scientific model construction. Science & Education 16, 699–724 (2007)CrossRefGoogle Scholar
  62. Robinson, A.: Non-Standard Analysis. North Holland, Amsterdam (1966)Google Scholar
  63. Rouse, J.: Laboratory fictions. In: Suárez, M. (ed.) Fictions in Science: Philosophical Essays on Modeling and Idealization, pp. 37–55. Routledge, London (2009)Google Scholar
  64. Rowbottom, D.P.: Models in biology and physics: what’s the difference. Foundations of Science 14, 281–294 (2009)CrossRefGoogle Scholar
  65. Steel, D.: Epistemic values and the argument from inductive risk. Philosophy of Science 77, 14–34 (2010)CrossRefGoogle Scholar
  66. Stjernfelt, F.: Diagrammatology. An Investigation on the Borderlines of Phenomenology, Ontology, and Semiotics. Springer, Berlin (2007)Google Scholar
  67. Suárez, M.: Scientific fictions as rules of inference. In: Suárez, M. (ed.) Fictions in Science: Philosophical Essays on Modeling and Idealization, pp. 158–178. Routledge, London (2009)Google Scholar
  68. Suárez, M. (ed.): Fictions in Science: Philosophical Essays on Modeling and Idealization. Routledge, London (2009)Google Scholar
  69. Suárez, M.: Fictions, inference, and realism. In: Woods, J. (ed.) Fictions and Models: New Essays, pp. 225–245. Philosophia Verlag, Munich (2010)Google Scholar
  70. Sugden, R.: Credible worlds: the status of theoretical models in economics. Journal of Economic Methodology 7, 1–31 (2000)CrossRefGoogle Scholar
  71. Sugden, R.: Credible worlds, capacities and mechanisms. Erkenntnis 70, 3–27 (2009)CrossRefGoogle Scholar
  72. Thagard, P.: The passionate scientist: emotion in scientific cognition. In: Carruthers, P., Stich, S., Siegal, M. (eds.) The Cognitive Basis of Science, pp. 235–250. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  73. Thom, R.: Esquisse d’une sémiophysique. InterEditions, Paris (1988); Meyer, V.(trans.): Semio Physics: a Sketch. Addison Wesley, Redwood City (1990)Google Scholar
  74. Thomson-Jones, M.: Missing systems and the face value practice. Synthese 172, 283–299 (2010)CrossRefGoogle Scholar
  75. Toon, A.: The ontology of theoretical modelling: models. Synthese 172, 301–315 (2010)CrossRefGoogle Scholar
  76. Vorms, M.: The Theoretician’s Gambits: Scientific Representations, Their Formats and Content. In: Magnani, L., Carnielli, W., Pizzi, C. (eds.) Model-Based Reasoning in Science and Technology. SCI, vol. 314, pp. 533–558. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  77. Weisberg, M.: Three kinds of idealizations. Journal of Philosophy 104(12), 639–659 (2007)Google Scholar
  78. Woods, J., Rosales, A.: Unifying the fictional. In: Woods, J. (ed.) Fictions and Models: New Essays, pp. 345–388. Philosophia Verlag, Munich (2010)Google Scholar
  79. Woods, J., Rosales, A.: Virtuous Distortion. Abstraction and Idealization in Model-Based Science. In: Magnani, L., Carnielli, W., Pizzi, C. (eds.) Model-Based Reasoning in Science and Technology. SCI, vol. 314, pp. 3–30. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  80. Woods, J. (ed.): Fictions and Models: New Essays. Philosophia Verlag, Munich (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lorenzo Magnani
    • 1
    • 2
  1. 1.Department of Arts and Humanities, Philosophy Section and Computational, Philosophy LaboratoryUniversity of PaviaPaviaItaly
  2. 2.Department of PhilosophySun Yat-sen UniversityGuangzhouP.R. China

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