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Epistemic Loops and Measurement Realism

Published online by Cambridge University Press:  01 January 2022

Abstract

Recent philosophy of measurement has emphasized the existence of both diachronic and synchronic “loops,” or feedback processes, in the epistemic achievements of measurement. A widespread response has been to conclude that measurement outcomes do not convey interest-independent facts about the world and that only a coherentist epistemology of measurement is viable. In contrast, I argue that a form of measurement realism is consistent with these results. The insight is that antecedent structure in measuring spaces constrains our empirical procedures such that successful measurement conveys a limited but veridical knowledge of “fixed points,” or modally stable, interest-independent features of the world.

Type
Realism
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

This article benefited from conversations with Teru Miyake, Eran Tal, George Smith, and J. E. Wolff, as well as the comments of participants at the PSA 2018 session on measurement.

References

Byerly, H., and Lazara, V.. 1973. “Realist Foundations of Measurement.” Philosophy of Science 40:1028.CrossRefGoogle Scholar
Chang, H. 2004. Inventing Temperature. Oxford: Oxford University Press.CrossRefGoogle Scholar
Chang, H.. 2007. “Scientific Progress: Beyond Foundationalism and Coherentism.” Royal Institute of Philosophy Supplement 61:120.CrossRefGoogle Scholar
Chang, H.. 2012. Is Water H2O? Dordrecht: Springer.CrossRefGoogle Scholar
Díez, J. 1997. “A Hundred Years of Numbers: An Historical Introduction to Measurement Theory, 1887–1990.” Pt. 2. Studies in History and Philosophy of Science 28:237–65.CrossRefGoogle Scholar
Krantz, D., Luce, R., Suppes, P., and Tversky, A.. 1971. Foundations of Measurement. vol. 1. New York: Academic Press.Google Scholar
LaPorte, J. 2004. Natural Kinds and Conceptual Change. Cambridge: Cambridge University Press.Google Scholar
Mari, L. 2003. “Epistemology of Measurement.” Measurement 34:1730.CrossRefGoogle Scholar
Mari, L., and Giordani, A.. 2014. “Modeling Measurement: Error and Uncertainty.” In Error and Uncertainty in Scientific Practice, ed. Boumans, Marcel, Hon, Giora, and Petersen, Arthur, 7996. London: Pickering & Chatto.Google Scholar
Michell, J. 2005. “The Logic of Measurement: A Realist Overview.” Measurement 38:285–94.CrossRefGoogle Scholar
Millikan, R. 1911. “On the Elementary Electrical Charge and the Avogadro Constant.” Physical Review 2:349–97.Google Scholar
Miyake, T. 2018. “Scientific Realism and the Earth Sciences.” In The Routledge Handbook of Scientific Realism, ed. Saatsi, Juha, 333–44. New York: Routledge.Google Scholar
Mohr, P., Newell, D., and Taylor, B.. 2016. “CODATA Recommended Values of the Fundamental Physical Constants, 2014.” Review of Modern Physics 88:035009.CrossRefGoogle Scholar
Morrison, M. 2009. “Models, Measurement and Computer Simulation: The Changing Face of Experimentation.” Philosophical Studies 143:3357.CrossRefGoogle Scholar
Parker, W. 2017. “Computer Simulation, Measurement, and Data Assimilation.” British Journal for Philosophy of Science 68:273304.CrossRefGoogle Scholar
Psillos, S. 2011. “Moving Molecules above the Scientific Horizon: On Perrin’s Case for Realism.” Journal for General Philosophy of Science 42:339–63.CrossRefGoogle Scholar
Salmon, W. 1984. Scientific Explanation and the Causal Structure of the World. Princeton, NJ: Princeton University Press.Google Scholar
Slater, M. 2017. “Plato and the Platypus: An Odd Ball and an Odd Duck; On Classificatory Norms.” Studies in History and Philosophy of Science 61:110.CrossRefGoogle Scholar
Smith, G. 2001. “J. J. Thomson and the Electron, 1897–1899.” In Histories of the Electron: The Birth of Microphysics, ed. Buchwald, Jed Z. and Warwick, Andrew. Cambridge, MA: MIT Press.Google Scholar
Smith, G., and Miyake, T.. Forthcoming. “Realism, Physical Meaningfulness, and Molecular Spectroscopy.” In Contemporary Scientific Realism: The Challenge from the History of Science, ed. Vickers, P. and Lyons, T.. Oxford: Oxford University Press.Google Scholar
Stevens, S. 1946. “On the Theory of Scales of Measurement.” Science 103 (2684): 677–80..CrossRefGoogle ScholarPubMed
Suppes, P., Krantz, D., Luce, R., and Tversky, A.. 1989. Foundations of Measurement. vol. 2. New York: Academic Press.Google Scholar
Tal, E. 2013. “Old and New Problems in Philosophy of Measurement.” Philosophy Compass 8 (12): 1159–73..CrossRefGoogle Scholar
Tal, E.. 2014. “Making Time: A Study in the Epistemology of Measurement.” British Journal for Philosophy of Science 67:297335.CrossRefGoogle Scholar
van Fraassen, B. 2008. Scientific Representation. Oxford: Oxford University Press.CrossRefGoogle Scholar
Wandell, B. 1995. Foundations of Vision. Sunderland, MA: Sinauer.Google Scholar
Weisberg, M. 2006. “Robustness Analysis.” Philosophy of Science 73:730–42.CrossRefGoogle Scholar
Worrall, J. 1989. “Structural Realism: The Best of Both Worlds.” Dialectica 43:99124.CrossRefGoogle Scholar