¿Surrogative Reasoning as Representational or Logical-Based Thinking?
Abstract The aim of our paper is to carry out a critical analysis of the notion of representation as a basis for hypothesis generation in scientific modelling. Indeed, we will show the inconsistencies generated by this way of grounding hypothesis generation in some of the most representative approaches to scientific representation. Depending on the approach and the definition of representation considered, we show that these inconsistencies range from the use of non-logical resources to a certain circularity in the definitions. The idea underlying all this critique is that surrogative reasoning must find its foundations in logic itself.
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Balzer, W., Moulines, C. U., & Sneed, J. D. (1987). An Architectonic for Science. The Structuralist Program (Vol. 186). Dordrecht: Springer Netherlands.
Callender, C. & Cohen, J. (2006). There Is No Special Problem About Scientific Representation. Theoria, 21(1), 67-84.
Cartwright, N., Shomar, T., & Suárez, M. (1995). The tool box of science. Tools for the building of models with a superconductivity example. Pozna? Studies in the Philosophy of the Sciences and the Humanities, 44, 137-149.
Cassini, A. (2016). Modelos científicos. Diccionario Interdisciplinar Austral (DIA). http:// dia.austral.edu.ar/Modelos
Chakravartty, A. (2010). Informational versus functional theories of scientific representation. Synthese, 172, 197-213. https://doi.org/10.1007/s11229-009-9502-3
Chakravartty, A. (2017) Scientific Realism. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/sum2017/entries/scientific-realism/
Contessa, G. (2007). Scientific representation, interpretation, and surrogative reasoning. Philosophy of Science, 74(1), 48-68. https://doi.org/10.1086/519478
da Costa, N. C. A., & French, S. (2003). Science and Partial Truth. A Unitary Approach to Models and Scientific Reasoning. New York: Oxford University Press.
Diéguez, A. (1998). Realismo científico. Una introducción al debate actual en la filosofía de la ciencia. Málaga: Universidad de Málaga.
Frigg, R., & Nguyen, J. (2016). Scientific representation. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/scientific-representation/
Frigg, R. & Nguyen, J. (2017). Models and representation. In L. Magnani & T. Bertolotti (eds.), Handbook of Model-Based Science (pp. 49-102). Dordrecht-New York: Springer.
Giere, R. (1988). Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.
Giere, R. (1999). Science without Laws. Chicago: University of Chicago Press.
Kitcher, P. (1993). The Advancement of Science. Science without Legend, Objectivity without Il lusions. Oxford: Oxford University Press.
Knuuttila, T., & Merz, M. (2009). Understanding by modeling. An objectual approach. In H. W. de Regt, S. Leonelli, & K. Eigner (eds.), Scientific Understanding. Philosophical Perspectives (pp. 146-168). Pittsburgh: University of Pittsburgh Press.
Lalande, A. (1997). Vocabulaire technique et critique de la philosophie, Volume 1. Paris: Quadrige, PUF.
Lopez-Orellana, Rodrigo (2020). Sobre la modelización y la comprensión científicas. Un enfoque inferencial y dinámico aplicado al modelo evo-devo Polypterus de la plasticidad fenotípica. PhD. Thesis. Salamanca: Universidad de Salamanca.
López-Orellana, R., & Redmond, J. (2021). Crítica a la noción de modelo de Patrick Suppes. Revista de Filosofía, 78, 135-155. https://revistafilosofia.uchile.cl/index.php/RDF/article/view/65672
Lopez-Orellana, R., Redmond, J., & Cortés-García, D. (2019). An inferential and dynamic approach to modeling and understanding in biology. RHV, (14), 315-334. https://doi.org/10.22370/rhv2019iss14pp315-334
Mäki, U. (2009). MISSing the world. models as isolations and credible surrogate systems. Erkenn, 70, 29-43. https://doi.org/10.1007/s10670-008-9135-9
Morrison, M. (1999). Models as autonomous agents. In M. Morrison & M. S. Morgan (eds.), Models as Mediators. Perspectives on Natural and Social Science (pp. 38-65). Cambridge: Cambridge University Press.
Morrison, M., & Morgan, M. S. (1999). Models as mediating instruments. In M. Morrison & M. S. Morgan (eds.), Models as Mediators. Perspectives on Natural and Social Science (pp. 10-37). Cambridge: Cambridge University Press.
Psillos, S. (1999). Scientific Realism: How Science Tracks Truth. London, New York: Routledge.
Redmond, J. (2021). A free dialogical logic for surrogate reasoning: generation of hypothesis without ontological commitments. Theoria, 36(3), 297-320. https://doi.org/10.1387/theoria.21902
Redmond, J., Valladares, D. L., & Lopez-Orellana, R. (2017). Modelizaciones galileanas y objetos ideales. In G. Cuadrado & L. E. Gómez (eds.), Ciencias de la ingeniería en el siglo XXI. Nuevos enfoques en su lógica, enseñanza y práctica (pp. 51-61). Mendoza: Universidad Tecnológica Nacional.
Reicher, Maria (2019). Nonexistent Objects. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2019/entries/nonexistent-objects/
Shapiro, S., & Kissel, T. K. (2018). Classical Logic. Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/logic-classical/
Sneed, J. D. (1971). The Logical Structure of Mathematical Physics. Dordrecht: D. Reidel
Stegmüller, W. (1970). Theorie und Erfahrung (Vol. 2). Berlin: Springer-Verlag.
Stegmüller, W. (1973). Theorienstrukturen und Theorien-Dynamik. Zweiter Halbband Theorienstrukturen und Theoriendynamik (Vol. 2/2). Berlin: Springer-Verlag.
Suárez, M. (2003). Scientific representation: against similarity and isomorphism. International Studies in the Philosophy of Science, 17(3), 225-244.
Suárez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71(5), 767-779.
Suárez, M. (2016). Representation in science. In P. Humphreys (ed.), Oxford Handbook of the Philosophy of Science (pp. 440-459). Oxford: Oxford University Press
Sugden, R. (2000). Credible worlds: the status of theoretical modelos in economics. Journal of Economic Methodology, 7(1), 1-31. https://doi.org/10.1080/135017800362220
Suppes, P. (1960). A comparison of the meaning and uses of models in mathematics and the empirical sciences. Synthese, 12(2-3), 287-301.
Suppes, P. (1962). Models of data. In E. Nagel, P. Suppes, & A. Tarski (eds.), Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress (pp. 252-261). Stanford: Stanford University Press.
Suppes, P. (1970). Set-Theoretical Structures in Science. Stanford: Stanford University Press
Suppes, P. (1974). The axiomatic method in the empirical sciences. In L. Henkin (ed.), Proceedings of the Tarski Symposium (Vol. XXV, pp. 465-479). Providence: American Mathematical Society.
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87(3), 449-508. https://doi.org/10.1007/BF00499820
van Fraassen, B. C. (1980). The Scientific Image. Oxford: Clarendon Press
van Fraassen, B. C. (1987). The semantic approach to scientific theories. In N. J. Nersessian (ed.), The Process of Science. Contemporary Philosophical Approaches to Understanding Scientific Practice (pp. 105-124). Lancaster: Kluwer Academic Publishers.
Worrall, J. (1989). Structural realism: The best of both worlds? Dialectica, 43, 99-124. https://doi.org/10.1111/j.1746
Callender, C. & Cohen, J. (2006). There Is No Special Problem About Scientific Representation. Theoria, 21(1), 67-84.
Cartwright, N., Shomar, T., & Suárez, M. (1995). The tool box of science. Tools for the building of models with a superconductivity example. Pozna? Studies in the Philosophy of the Sciences and the Humanities, 44, 137-149.
Cassini, A. (2016). Modelos científicos. Diccionario Interdisciplinar Austral (DIA). http:// dia.austral.edu.ar/Modelos
Chakravartty, A. (2010). Informational versus functional theories of scientific representation. Synthese, 172, 197-213. https://doi.org/10.1007/s11229-009-9502-3
Chakravartty, A. (2017) Scientific Realism. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/sum2017/entries/scientific-realism/
Contessa, G. (2007). Scientific representation, interpretation, and surrogative reasoning. Philosophy of Science, 74(1), 48-68. https://doi.org/10.1086/519478
da Costa, N. C. A., & French, S. (2003). Science and Partial Truth. A Unitary Approach to Models and Scientific Reasoning. New York: Oxford University Press.
Diéguez, A. (1998). Realismo científico. Una introducción al debate actual en la filosofía de la ciencia. Málaga: Universidad de Málaga.
Frigg, R., & Nguyen, J. (2016). Scientific representation. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/scientific-representation/
Frigg, R. & Nguyen, J. (2017). Models and representation. In L. Magnani & T. Bertolotti (eds.), Handbook of Model-Based Science (pp. 49-102). Dordrecht-New York: Springer.
Giere, R. (1988). Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.
Giere, R. (1999). Science without Laws. Chicago: University of Chicago Press.
Kitcher, P. (1993). The Advancement of Science. Science without Legend, Objectivity without Il lusions. Oxford: Oxford University Press.
Knuuttila, T., & Merz, M. (2009). Understanding by modeling. An objectual approach. In H. W. de Regt, S. Leonelli, & K. Eigner (eds.), Scientific Understanding. Philosophical Perspectives (pp. 146-168). Pittsburgh: University of Pittsburgh Press.
Lalande, A. (1997). Vocabulaire technique et critique de la philosophie, Volume 1. Paris: Quadrige, PUF.
Lopez-Orellana, Rodrigo (2020). Sobre la modelización y la comprensión científicas. Un enfoque inferencial y dinámico aplicado al modelo evo-devo Polypterus de la plasticidad fenotípica. PhD. Thesis. Salamanca: Universidad de Salamanca.
López-Orellana, R., & Redmond, J. (2021). Crítica a la noción de modelo de Patrick Suppes. Revista de Filosofía, 78, 135-155. https://revistafilosofia.uchile.cl/index.php/RDF/article/view/65672
Lopez-Orellana, R., Redmond, J., & Cortés-García, D. (2019). An inferential and dynamic approach to modeling and understanding in biology. RHV, (14), 315-334. https://doi.org/10.22370/rhv2019iss14pp315-334
Mäki, U. (2009). MISSing the world. models as isolations and credible surrogate systems. Erkenn, 70, 29-43. https://doi.org/10.1007/s10670-008-9135-9
Morrison, M. (1999). Models as autonomous agents. In M. Morrison & M. S. Morgan (eds.), Models as Mediators. Perspectives on Natural and Social Science (pp. 38-65). Cambridge: Cambridge University Press.
Morrison, M., & Morgan, M. S. (1999). Models as mediating instruments. In M. Morrison & M. S. Morgan (eds.), Models as Mediators. Perspectives on Natural and Social Science (pp. 10-37). Cambridge: Cambridge University Press.
Psillos, S. (1999). Scientific Realism: How Science Tracks Truth. London, New York: Routledge.
Redmond, J. (2021). A free dialogical logic for surrogate reasoning: generation of hypothesis without ontological commitments. Theoria, 36(3), 297-320. https://doi.org/10.1387/theoria.21902
Redmond, J., Valladares, D. L., & Lopez-Orellana, R. (2017). Modelizaciones galileanas y objetos ideales. In G. Cuadrado & L. E. Gómez (eds.), Ciencias de la ingeniería en el siglo XXI. Nuevos enfoques en su lógica, enseñanza y práctica (pp. 51-61). Mendoza: Universidad Tecnológica Nacional.
Reicher, Maria (2019). Nonexistent Objects. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2019/entries/nonexistent-objects/
Shapiro, S., & Kissel, T. K. (2018). Classical Logic. Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/logic-classical/
Sneed, J. D. (1971). The Logical Structure of Mathematical Physics. Dordrecht: D. Reidel
Stegmüller, W. (1970). Theorie und Erfahrung (Vol. 2). Berlin: Springer-Verlag.
Stegmüller, W. (1973). Theorienstrukturen und Theorien-Dynamik. Zweiter Halbband Theorienstrukturen und Theoriendynamik (Vol. 2/2). Berlin: Springer-Verlag.
Suárez, M. (2003). Scientific representation: against similarity and isomorphism. International Studies in the Philosophy of Science, 17(3), 225-244.
Suárez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71(5), 767-779.
Suárez, M. (2016). Representation in science. In P. Humphreys (ed.), Oxford Handbook of the Philosophy of Science (pp. 440-459). Oxford: Oxford University Press
Sugden, R. (2000). Credible worlds: the status of theoretical modelos in economics. Journal of Economic Methodology, 7(1), 1-31. https://doi.org/10.1080/135017800362220
Suppes, P. (1960). A comparison of the meaning and uses of models in mathematics and the empirical sciences. Synthese, 12(2-3), 287-301.
Suppes, P. (1962). Models of data. In E. Nagel, P. Suppes, & A. Tarski (eds.), Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress (pp. 252-261). Stanford: Stanford University Press.
Suppes, P. (1970). Set-Theoretical Structures in Science. Stanford: Stanford University Press
Suppes, P. (1974). The axiomatic method in the empirical sciences. In L. Henkin (ed.), Proceedings of the Tarski Symposium (Vol. XXV, pp. 465-479). Providence: American Mathematical Society.
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87(3), 449-508. https://doi.org/10.1007/BF00499820
van Fraassen, B. C. (1980). The Scientific Image. Oxford: Clarendon Press
van Fraassen, B. C. (1987). The semantic approach to scientific theories. In N. J. Nersessian (ed.), The Process of Science. Contemporary Philosophical Approaches to Understanding Scientific Practice (pp. 105-124). Lancaster: Kluwer Academic Publishers.
Worrall, J. (1989). Structural realism: The best of both worlds? Dialectica, 43, 99-124. https://doi.org/10.1111/j.1746
Redmond, J., & Lopez-Orellana, R. (2022). ¿Surrogative Reasoning as Representational or Logical-Based Thinking?. ArtefaCToS. Revista De Estudios Sobre La Ciencia Y La tecnología, 11(2), 191–207. https://doi.org/10.14201/art2022112191207 (Original work published October 29, 2022)
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