Advertisement

Scientific Models Are Distributed and Never Abstract

A Naturalistic Perspective
  • Lorenzo Magnani
Chapter
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 25)

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. This article discusses these approaches showing some of their epistemological inadequacies, also taking advantage of recent results in cognitive science. I will substantiate my revision of epistemological fictionalism reframing the received idea of abstractness and ideality of models with the help of recent results related to the role of distributed cognition (common coding) and abductive cognition (manipulative).

Keywords

Models Abstract models Idealization Abduction Fictions Distributed cognition Creativity 

Notes

Acknowledgements

For the instructive criticisms and precedent discussions and correspondence that helped me to develop my critique of fictionalism, I am indebted and grateful to John Woods, Shahid Rahman, Alirio Rosales, Mauricio Suárez, and to my collaborators Tommaso Bertolotti and Selene Arfini.

References

  1. Bardone, E.: Seeking Chances. From Biased Rationality to Distributed Cognition. Springer, Heidelberg (2011)Google Scholar
  2. Barsalou, L.W.: Cognitive and neural contributions to understanding the conceptual system. Curr. Dir. Psychol. Sci. 17(2), 91–95 (2008a)CrossRefGoogle Scholar
  3. Barsalou, L.W.: Grounded cognition. Annu. Rev. Psychol. 59, 617–645 (2008b)CrossRefGoogle Scholar
  4. Barwich, A.-S.: Science and fiction: analysing the concept of fiction in science and its limits. J. Gen. Philos. Sci. 44, 357–373 (2013)CrossRefGoogle Scholar
  5. Bertolotti, T.: From mindless modeling to scientific models. The case of emerging models. In: Magnani, L., Li, P. (eds.) Philosophy and Cognitive Science. Western and Eastern Studies, pp. 75–104. Springer, Heidelberg/Berlin (2012)CrossRefGoogle Scholar
  6. Bokulich, A.: How scientific models can explain. Synthese 1, 33–45 (2011)CrossRefGoogle Scholar
  7. Bueno, O., French, S.: How theories represent. Br. J. Philos. Sci. 62, 857–894 (2011)Google Scholar
  8. Cartwright, N.: If no capacities then no credible worlds. But can models reveal capacities? Erkenntnis 70, 45–58 (2009a)CrossRefGoogle Scholar
  9. Cartwright, R.: Models: parables v. fables. Insights 1(8), 2–10 (2009b)Google Scholar
  10. Chakravartty, A.: Informational versus functional theories of scientific representation. Synthese 172, 197–213 (2010)CrossRefGoogle Scholar
  11. Chandradekharan, S., Nersessian, N.J.: Building cognition: the construction of computational representations for scientific discovery. Cogn. Sci. 2014. doi: 10.1111/cogs.12203
  12. Chandradekharan, S.: Becoming knowledge. In: Osbek, L.M., Held, B.S. (eds.) Rational Intuition. Philosophical roots, scientific investigations, pp. 307–337. Oxford University Press, Oxford (2014)CrossRefGoogle Scholar
  13. Chandrasekharan, S.: Building to discover: a common coding model. Cogn. Sci. 33, 1059–1086 (2009)CrossRefGoogle Scholar
  14. 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
  15. Contessa, G.: Scientific representation, interpretation, and surrogative reasoning. Philos. Sci. 74, 48–68 (2007)CrossRefGoogle Scholar
  16. Contessa, G.: Scientific models and fictional objects. Synthese 172, 215–229 (2010)CrossRefGoogle Scholar
  17. Cozzo, C.: Gulliver, truth and virtue. Topoi 31, 59–66 (2012)CrossRefGoogle Scholar
  18. 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
  19. De Cruz, H., De Smedt, J.: Mathematical symbols as epistemic actions. Synthese 190/1, 3–19 (2011)Google Scholar
  20. Feyerabend, P.: Against Method. Verso, London (1975)Google Scholar
  21. Fine, A. Fictionalism. In: Suárez M. (ed.) Fictions in Science: Philosophical Essays on Modeling and Idealization, pp. 36–19. Routledge, London (2009)Google Scholar
  22. Fodor, J.: The Modularity of the Mind. The MIT Press, Cambridge (1983)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 (Translated by Strachey, J. in collaboration with Freud, A. et al.). Hogarth Press, London, 1953–1974Google 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 (2010a)CrossRefGoogle Scholar
  26. Frigg, R.: Fiction in science. In: Woods, J. (ed.) Fictions and Models: New Essays, pp. 247–287. Philosophia Verlag, Munich (2010b)Google Scholar
  27. Frigg, R.: Models and fiction. Synthese 172, 251–268 (2010c)CrossRefGoogle Scholar
  28. Galilei, G.: The Assayer [1623]. In: Discoveries and Opinions of Galileo (Translated and edited by S. Drake), pp. 231–280. Doubleday, New York (1957)Google Scholar
  29. Giere, R.N.: Explaining Science: A Cognitive Approach. University of Chicago Press, Chicago (1988)CrossRefGoogle Scholar
  30. Giere, R.: An agent-based conception of models and scientific representation. Synthese 172, 269–281 (2007)CrossRefGoogle Scholar
  31. 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
  32. Godfrey-Smith, P.: The strategy of model-based science. Biol. Philos. 21, 725–740 (2006)CrossRefGoogle Scholar
  33. Godfrey-Smith, P.: Models and fictions in science. Philos. Stud. 143, 101–116 (2009)CrossRefGoogle Scholar
  34. Gregory, R.L.: Perception as hypothesis. In: Gregory, R.L. (ed.) The Oxford Companion to the Mind, pp. 608–611. Oxford University Press, New York (1987)Google Scholar
  35. Hintikka, J.: What is abduction? The fundamental problem of contemporary epistemology. Trans. Charles S. Peirce Soc. 34, 503–533 (1998)Google Scholar
  36. Hutchins, E.: Cognitive artifacts. In: Wilson, R.A., Keil, F.C. (eds.) Encyclopedia of the Cognitive Sciences, pp. 126–7. The MIT Press, Cambridge (1999)Google Scholar
  37. Josephson, J.R., Josephson, S.G. (eds.) Abductive Inference. Computation, Philosophy, Technology. Cambridge University Press, Cambridge (1994)Google Scholar
  38. Kant, I.: Critique of Pure Reason (Translated by Kemp Smith, N. originally published 1787, reprint 1998). MacMillan, London (1929)Google Scholar
  39. Kirsh, D., Maglio, P.: On distinguishing epistemic from pragmatic action. Cogn. Sci. 18, 513–549 (1994)Google Scholar
  40. Kuorikoski, J., Lehtinen, A.: Incredible worlds, credible results. Erkenntnis 70, 119–131 (2009)Google Scholar
  41. Magnani, L.: Abduction, Reason, and Science. Processes of Discovery and Explanation. Kluwer Academic, Plenum Publishers, New York (2001)CrossRefGoogle Scholar
  42. Magnani, L.: Conjectures and manipulations. Computational modeling and the extra-theoretical dimension of scientific discovery. Mind. Mach. 14, 507–537 (2004a)CrossRefGoogle Scholar
  43. Magnani, L.: Model-based and manipulative abduction in science. Found. Sci. 9, 219–247 (2004b)CrossRefGoogle Scholar
  44. Magnani, L.: Abduction and chance discovery in science. Int. J. Knowl.-Based Intell. Eng. 11, 273–279 (2007)Google Scholar
  45. Magnani, L.: Abductive Cognition. The Epistemological and Eco-Cognitive Dimensions of Hypothetical Reasoning. Springer, Heidelberg (2009)Google Scholar
  46. Magnani, L.: Understanding Violence. The Interwining of Morality, Religion, and Violence: A Philosophical Stance. Springer, Heidelberg (2011)Google Scholar
  47. Magnani, L.: Scientific models are not fictions. Model-based science as epistemic warfare. In: Magnani, L., Li, P. (eds.) Philosophy and Cognitive Science. Western and Eastern Studies, pp. 1–38. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  48. Mäki, U.: MISSing the world. Models as isolations and credible surrogate systems. Erkenntnis 70, 29–43 (2009)CrossRefGoogle Scholar
  49. Mizrahi, M.: Idealizations and scientific understanding. Philos. Stud. 160(2), 237–252 (2011)CrossRefGoogle Scholar
  50. Naylor, R.: Real experiment and didactic demonstration. Isis 67(3), 398–419 (1976)CrossRefGoogle Scholar
  51. Nersessian, N.J., Chandradekharan, S.: Hybrid analogies in conceptual innovation in science. Cogn. Syst. Res. 10(3), 178–188 (2009)Google Scholar
  52. Odling-Smee, F.J., Laland, K.N., Feldman, M.W.: Niche Construction. The Neglected Process in Evolution. Princeton University Press, Princeton (2003)Google Scholar
  53. Park, W.: Abduction and estimation in animals. Found. Sci. 17(4), 321–337 (2012)CrossRefGoogle Scholar
  54. Peirce, C.S.: Collected Papers of Charles Sanders Peirce, vols. 1–6, Hartshorne, C., Weiss, P. (eds.); vols. 7–8, Burks, A.W. (ed.). Harvard University Press, Cambridge, MA, (1931–1958)Google Scholar
  55. Portides, D.P.: The relation between idealization and approximation in scientific model construction. Sci. Educ. 16, 699–724 (2007)CrossRefGoogle Scholar
  56. Raftopoulos, A.: Is perception informationally encapsulated? The issue of theory-ladenness of perception. Cogn. Sci. 25, 423–451 (2001)CrossRefGoogle Scholar
  57. Raftopoulos, A.: Reentrant pathways and the theory-ladenness of perception. Philos. Sci. 68, S187–S189 (2001) (Proceedings of PSA 2000 Biennal Meeting)Google Scholar
  58. Raftopoulos, A.: Cognition and Perception. How Do Psychology and Neural Science Inform Philosophy? The MIT Press, Cambridge (2009)Google Scholar
  59. Robinson, A.: Non-Standard Analysis. North Holland, Amsterdam (1966)Google Scholar
  60. 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
  61. Rowbottom, D.P.: Models in biology and physics: What’s the difference. Found. Sci. 14, 281–294 (2009)CrossRefGoogle Scholar
  62. Steel, D.: Epistemic values and the argument from inductive risk. Philos. Sci. 77, 14–34 (2010)CrossRefGoogle Scholar
  63. Stjernfelt, F.: Diagrammatology. An Investigation on the Borderlines of Phenomenology, Ontology, and Semiotics. Springer, Berlin (2007)Google Scholar
  64. 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
  65. Suárez, M.: Fictions, inference, and realism. In: Woods, J. (ed.) Fictions and Models: New Essays, pp. 225–245. Philosophia Verlag, Munich (2010)Google Scholar
  66. Sugden, R.: Credible worlds: the status of theoretical models in economics. J. Econ. Method. 7, 1–31 (2000)CrossRefGoogle Scholar
  67. Sugden, R.: Credible worlds, capacities and mechanisms. Erkenntnis 70, 3–27 (2009)CrossRefGoogle Scholar
  68. Thom, R.: Esquisse d’une s´emiophysique (Translated by Meyer, V.: Semio Physics: A Sketch, Addison Wesley, Redwood City, CA, 1990). InterEditions, Paris (1988)Google Scholar
  69. Thomson-Jones, M.: Missing systems and the face value practice. Synthese 172, 283–299 (2010)CrossRefGoogle Scholar
  70. Toon, A.: The ontology of theoretical modelling: Models. Synthese 172, 301–315 (2010)CrossRefGoogle Scholar
  71. 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. Abduction, Logic, and Computational Discovery, pp. 533–558. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  72. Weisberg, M.: Three kinds of idealizations. J. Philos. 104(12), 639–659 (2007)CrossRefGoogle Scholar
  73. 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
  74. 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, pp. 3–30. Springer, Heidelberg (2010)Google Scholar
  75. Woods, J. (ed.): Fictions and Models: New Essays. Philosophia Verlag, Munich (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Humanities, Philosophy Section, and Computational Philosophy LaboratoryUniversity of PaviaPaviaItaly

Personalised recommendations