Integrating Inferentialism about Physical Theories and Representations: A Case for Phase Space Diagrams
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En este trabajo defiendo una concepción inferencial integrada sobre teorías y representaciones y su papel en la explicación del valor teórico de prácticas de representación filosóficamente despreciadas, como el uso sistemático de diagramas de espacio de fase en el contexto teórico de la mecánica estadística. Esta propuesta se apoyaría tanto en el inferencialismo sobre las representaciones científicas (Suárez 2004) como en el inferencialismo sobre las teorías físicas particulares (Wallace 2017). Defiendo que ambas perspectivas convergen de alguna manera en un inferencialismo integrado mediante la tesis de las teorías como compuestas de representaciones, tal y como se defiende desde la concepción semántica representacional que Suárez y Pero (2019) defienden.
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Anta, J. (2021). Integrating Inferentialism about Physical Theories and Representations: A Case for Phase Space Diagrams. Crítica. Revista Hispanoamericana De Filosofía, 53(158), 47–77. https://doi.org/10.22201/iifs.18704905e.2021.1270
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Albert, D., 2000, Time and Chance, Harvard University Press, Cambridge, Mass.
Bueno, O. and M. Colyvan, 2011, “An Inferential Conception of the Application of Mathematics”, Noûs, vol. 45, no. 2, pp. 345–374.
Carter, A., J. Collin and O. Palermos, 2017, “Semantic Inferentialism As (A Form of) Active Externalism”, Phenomenology and the Cognitive Sciences, vol. 16. no. 3, pp. 387–402.
Cartwright, N., 1999, The Dappled World: A Study of the Boundaries of Science, Cambridge University Press, Cambridge. (https://doi.org/10.1017/CBO9781139167093)
Contessa, G., 2007, “Scientific Representation, Interpretation, and Surrogative Reasoning”, Philosophy of Science, vol. 74, no. 1, pp. 48–68.
da Costa, N.C.A. and S. French, 2003, Science and Partial Truth: A Unitary Approach to Models and Scientific Reasoning, Oxford University Press, USA.
de Regt, H., 2017, Understanding Scientific Understanding, Oxford University Press, USA.
Frigg, R., 2008, “A Field Guide to Recent Work on the Foundations of Statistical Mechanics”, in Dean Rickles (ed.), The Ashgate Companion to Contemporary Philosophy of Physics, Ashgate, London, pp. 991–996.
Frigg, R. and S. Hartmann, 2020, “Models in Science”, in Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy, downloaded from
[accessed: 13/03/2021].
Giere, R., 2010, “An Agent-Based Conception of Models and Scientific Representation”,
Synthese, vol. 172, no. 2, pp. 269–281. (https://doi.org/10.1007/s11229-009-9506-z)
Giere, R., 2004, “How Models Are Used to Represent Reality”, Philosophy of Science, vol. 71, no. 5, pp. 742–752.
Giere, R., 1988, Explaining Science: A Cognitive Approach, University of
Chicago Press, Chicago, IL.
Hacking, I., 1983, Representing and Intervening: Introductory Topics in the Philosophy of Natural Science, Cambridge University Press, Cambridge.
Hemmo, M. and O. Shenker, 2012, The Road to Maxwell’s Demon, Cambridge University Press, Cambridge.
Hughes, R.I.G., 1997, “Models and Representation”, Philosophy of Science, vol. 64, no. 4, pp. S325–S336.
Jaynes, E.T., 1957, “Information Theory and Statistical Mechanics”, Physical Review, vol. 106, no. 4, pp. 620–630.
Kuorikoski, J. and P. Ylikoski, 2015, “External Representations and Scientific Understanding”, Synthese, vol. 192, no. 12, pp. 3817–3837.
Lyon, A. and M. Colyvan, 2008, “The Explanatory Power of Phase Spaces”, Philosophia Mathematica, vol. 16, no. 2, pp. 227–243.
McCoy, C.D., 2020, “An Alternative Interpretation of Statistical Mechanics”, Erkenntnis, vol. 85, no. 1, pp. 1–21.
Parker, D., 2011, “Information-Theoretic Statistical Mechanics without Landauer’s Principle”, British Journal for the Philosophy of Science, vol. 62, no. 4, pp. 831–856.
Pincock, C., 2012, Mathematics and Scientific Representation, Oxford University Press, USA.
Robertson, K., 2020, “Asymmetry, Abstraction, and Autonomy: Justifying Coarse-Graining in Statistical Mechanics”, British Journal for the Philosophy of Science, vol. 71, no. 2, pp. 547–579.
Robertson, K., 2019, “Stars and Steam Engines: To What Extent Do Thermodynamics and Statistical Mechanics Apply to Self-Gravitating Systems?”, Synthese, vol. 196, no. 5, pp. 1783–1808.
Shenker, O., 2019, “Information vs. Entropy vs. Probability”, European Journal for Philosophy of Science, vol. 10, no. 1, pp. 1–25.
Sklar, L., 1993, Physics and Chance: Philosophical Issues in the Foundations of Statistical Mechanics, Cambridge University Press, Cambridge.
Suárez, M., 2004, “An Inferential Conception of Scientific Representation”, Philosophy of Science, vol. 71, no. 5, pp. 767–779. (https://doi.org/10.1086/421415)
Suárez, M. and F. Pero, 2019, “The Representational Semantic Conception”, Philosophy of Science, vol. 86, no. 2, pp. 344–365.
Uffink, J., 2007, “Compendium of the Foundations of Classical Statistical Physics”, in J. Butterfield and J. Earman (eds.), Handbook for Philosophy of Physics, Elsevier, Amsterdam, downloaded from http://philsciarchive.pitt.edu
Wallace, D., 2017, “Inferential vs. Dynamical Conceptions of Physics”, in O. Lombardi, S. Fortin, F. Holik, C. López (eds.), What Is Quantum Information?, Cambridge University Press, Cambridge, pp. 179–204.
Werndl, C. and R. Frigg, 2017, “Mind the Gap: Boltzmannian versus Gibbsian Equilibrium”, Philosophy of Science, vol. 84, no. 5, pp. 1289–1302.
Bueno, O. and M. Colyvan, 2011, “An Inferential Conception of the Application of Mathematics”, Noûs, vol. 45, no. 2, pp. 345–374.
Carter, A., J. Collin and O. Palermos, 2017, “Semantic Inferentialism As (A Form of) Active Externalism”, Phenomenology and the Cognitive Sciences, vol. 16. no. 3, pp. 387–402.
Cartwright, N., 1999, The Dappled World: A Study of the Boundaries of Science, Cambridge University Press, Cambridge. (https://doi.org/10.1017/CBO9781139167093)
Contessa, G., 2007, “Scientific Representation, Interpretation, and Surrogative Reasoning”, Philosophy of Science, vol. 74, no. 1, pp. 48–68.
da Costa, N.C.A. and S. French, 2003, Science and Partial Truth: A Unitary Approach to Models and Scientific Reasoning, Oxford University Press, USA.
de Regt, H., 2017, Understanding Scientific Understanding, Oxford University Press, USA.
Frigg, R., 2008, “A Field Guide to Recent Work on the Foundations of Statistical Mechanics”, in Dean Rickles (ed.), The Ashgate Companion to Contemporary Philosophy of Physics, Ashgate, London, pp. 991–996.
Frigg, R. and S. Hartmann, 2020, “Models in Science”, in Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy, downloaded from
[accessed: 13/03/2021].
Giere, R., 2010, “An Agent-Based Conception of Models and Scientific Representation”,
Synthese, vol. 172, no. 2, pp. 269–281. (https://doi.org/10.1007/s11229-009-9506-z)
Giere, R., 2004, “How Models Are Used to Represent Reality”, Philosophy of Science, vol. 71, no. 5, pp. 742–752.
Giere, R., 1988, Explaining Science: A Cognitive Approach, University of
Chicago Press, Chicago, IL.
Hacking, I., 1983, Representing and Intervening: Introductory Topics in the Philosophy of Natural Science, Cambridge University Press, Cambridge.
Hemmo, M. and O. Shenker, 2012, The Road to Maxwell’s Demon, Cambridge University Press, Cambridge.
Hughes, R.I.G., 1997, “Models and Representation”, Philosophy of Science, vol. 64, no. 4, pp. S325–S336.
Jaynes, E.T., 1957, “Information Theory and Statistical Mechanics”, Physical Review, vol. 106, no. 4, pp. 620–630.
Kuorikoski, J. and P. Ylikoski, 2015, “External Representations and Scientific Understanding”, Synthese, vol. 192, no. 12, pp. 3817–3837.
Lyon, A. and M. Colyvan, 2008, “The Explanatory Power of Phase Spaces”, Philosophia Mathematica, vol. 16, no. 2, pp. 227–243.
McCoy, C.D., 2020, “An Alternative Interpretation of Statistical Mechanics”, Erkenntnis, vol. 85, no. 1, pp. 1–21.
Parker, D., 2011, “Information-Theoretic Statistical Mechanics without Landauer’s Principle”, British Journal for the Philosophy of Science, vol. 62, no. 4, pp. 831–856.
Pincock, C., 2012, Mathematics and Scientific Representation, Oxford University Press, USA.
Robertson, K., 2020, “Asymmetry, Abstraction, and Autonomy: Justifying Coarse-Graining in Statistical Mechanics”, British Journal for the Philosophy of Science, vol. 71, no. 2, pp. 547–579.
Robertson, K., 2019, “Stars and Steam Engines: To What Extent Do Thermodynamics and Statistical Mechanics Apply to Self-Gravitating Systems?”, Synthese, vol. 196, no. 5, pp. 1783–1808.
Shenker, O., 2019, “Information vs. Entropy vs. Probability”, European Journal for Philosophy of Science, vol. 10, no. 1, pp. 1–25.
Sklar, L., 1993, Physics and Chance: Philosophical Issues in the Foundations of Statistical Mechanics, Cambridge University Press, Cambridge.
Suárez, M., 2004, “An Inferential Conception of Scientific Representation”, Philosophy of Science, vol. 71, no. 5, pp. 767–779. (https://doi.org/10.1086/421415)
Suárez, M. and F. Pero, 2019, “The Representational Semantic Conception”, Philosophy of Science, vol. 86, no. 2, pp. 344–365.
Uffink, J., 2007, “Compendium of the Foundations of Classical Statistical Physics”, in J. Butterfield and J. Earman (eds.), Handbook for Philosophy of Physics, Elsevier, Amsterdam, downloaded from http://philsciarchive.pitt.edu
Wallace, D., 2017, “Inferential vs. Dynamical Conceptions of Physics”, in O. Lombardi, S. Fortin, F. Holik, C. López (eds.), What Is Quantum Information?, Cambridge University Press, Cambridge, pp. 179–204.
Werndl, C. and R. Frigg, 2017, “Mind the Gap: Boltzmannian versus Gibbsian Equilibrium”, Philosophy of Science, vol. 84, no. 5, pp. 1289–1302.
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