Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-30T18:13:11.411Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards a Taxonomy of the Model-Ladenness of Data

Published online by Cambridge University Press:  01 January 2022

Alisa Bokulich
Get access Rights & Permissions [Opens in a new window]

Abstract

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are data-laden and data are model-laden. In this article I elaborate and defend the second, more controversial, component of the symbiosis view and construct a taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than so-called raw data and, hence, beneficially serve the epistemic aims of science.

Type
Models and Modeling
Information
Philosophy of Science , Volume 87 , Issue 5: PSA 2018 - Proceedings of the 2018 biennial meeting of the Philosophy of Science Association. Part II Symposia Papers , December 2020 , pp. 793 - 806
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

This article was written while I was a visiting researcher at the Institute for Advanced Study at Durham University, and I gratefully acknowledge the financial support of the European Union COFUND Senior Research Fellowship, under EU grant 609412. I am especially grateful to Wendy Parker for serving as my host while there and for many stimulating discussions about this topic.

References

Batterman, R. 2013. “The Tyranny of Scales.” In The Oxford Handbook of Philosophy of Physics, ed. Batterman, R., 255–86. Oxford: Oxford University Press.CrossRefGoogle Scholar
Beven, K., Buytaert, W., and Smith, L.. 2012. “On Virtual Observatories and Modelled Realities; or, Why Discharge Must Be Treated as a Virtual Variable.” Hydrological Processes 26:1905–8.CrossRefGoogle Scholar
Bierkens, M., Finke, P., and Willigen, P. de. 2001. “Upscaling and Downscaling Methods for Environmental Research.” Dordrecht: Kluwer Academic.Google Scholar
Bokulich, A. 2018. “Using Models to Correct Data: Paleodiversity and the Fossil Record.” Synthese. https://doi.org/10.1007/s11229-018-1820-x.CrossRefGoogle Scholar
Chang, H. 2004. Inventing Temperature: Measurement and Scientific Progress. Oxford: Oxford University Press.CrossRefGoogle Scholar
Edwards, L. 1984. “Insights on Why Graphic Correlation (Shaw’s Method) Works.” Journal of Geology 92:583–97.CrossRefGoogle Scholar
Edwards, P. 1999. “Global Climate Science, Uncertainty and Politics: Data-Laden Models, Model-Filtered Data.” Science as Culture 8 (4): 437–72.CrossRefGoogle Scholar
Edwards, P.. 2010. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press.Google Scholar
Fernández, J., Pepe, A., Poland, M., and Sigmundsson, F.. 2017. “Volcano Geodesy: Recent Developments and Future Challenges.” Journal of Volcanology and Geothermal Research 344:1–12.CrossRefGoogle Scholar
Fernàndez-Garcia, D., Llerar-Meza, G., and Gómez-Hernández, J. J.. 2009. “Upscaling Transport with Mass Transfer Models: Mean Behavior and Propagation of Uncertainty.” Water Resources Research 45 (W10411): 1–16.CrossRefGoogle Scholar
Guo, Z., Hu, X., Liu, J., Liu, C., and Xiao, J.. 2019. “Geophysical Field Data Interpolation Using Stochastic Partial Differential Equations for Gold Exploration in Dayaoshan, Guangxi, China.” Minerals 9 (14): 1–12.Google Scholar
Hanson, N. 1958/2010. Patterns of Discovery: An Inquiry into the Conceptual Foundations of Science. Cambridge: Cambridge University Press.Google Scholar
Keller, G. R. 2018. “Using and Understanding Gravity Data.” Office of Research and Sponsored Projects, University of Texas at El Paso. https://research.utep.edu/Default.aspx?PageContentID=3947&tabid=38186.Google Scholar
Leonelli, S. 2016. Data-Centric Biology: A Philosophical Study. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Lindgren, F., Rue, H., and Lindström, J.. 2011. “An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach.” Journal of the Royal Statistical Society B 73:423–98.Google Scholar
Lloyd, E. 2012. “The Role of ‘Complex’ Empiricism in the Debates about Satellite Data and Climate Models.” Studies in History and Philosophy of Science 43:390–401.CrossRefGoogle Scholar
Norton, S., and Suppe, F.. 2001. “Why Atmospheric Modeling Is Good Science.” In Changing the Atmosphere: Expert Knowledge and Environmental Governance, ed. Edwards, P. and Miller, C., 67–106. Cambridge, MA: MIT Press.Google Scholar
Parker, W. 2009. “Confirmation and Adequacy-for-Purpose in Climate Modelling.” Proceedings of the Aristotelian Society 53 (Suppl.): 233–49.Google Scholar
Parker, W.. 2016. “Reanalyses and Observations: What’s the Difference?” Bulletin of the American Meteorological Society 97:1565–72.CrossRefGoogle Scholar
Parker, W.. 2017. “Computer Simulation, Measurement, and Data Assimilation.” British Journal for the Philosophy of Science 68:273–304.CrossRefGoogle Scholar
Reichle, R. 2008. “Data Assimilation Methods in the Earth Sciences.” Advances in Water Resources 31:1411–18.CrossRefGoogle Scholar
Sadler, P. 2004. “Quantitative Biostratigraphy: Achieving Finer Resolution in Global Correlation.” Annual Review of Earth and Planetary Science 32:187–213.CrossRefGoogle Scholar
Sadler, P.. 2012. “Nails, Jacks and Clamps: Mechanical Analogs as a Way to Think about Organizing Vast and Varied Stratigraphic Information Sets for Computer Algorithms That Optimize Time Lines and Calibrate Time Scales.” Geological Society of America Abstracts with Programs 44 (7): 330.Google Scholar
Sadler, P., Cooper, R., and Crampton, J.. 2014. “High-Resolution Geobiologic Time-Lines: Progress and Potential, Fifty Years after the Advent of Graphic Correlation.” Sedimentary Record 12:4–9.CrossRefGoogle Scholar
Suppes, P. 1962. “Models of Data.” In Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress, ed. Nagel, E., Suppes, P., and Tarski, A., 252–61. Stanford, CA: Stanford University Press.Google Scholar
Tal, E. 2012. “The Epistemology of Measurement: A Model-Based Account.” PhD diss., University of Toronto. http://hdl.handle.net/1807/34936.Google Scholar
Tal, E.. 2017. “Calibration: Modeling the Measurement Process.” Studies in History and Philosophy of Science 65–66:33–45.CrossRefGoogle Scholar
Wald, L. 1999. “Some Terms of Reference in Data Fusion.” IEEE Transactions on Geoscience and Remote Sensing 37 (3): 1190–93.CrossRefGoogle Scholar
Zhang, Y., and Gregg, P.. 2017. “Data Assimilation Studies for Volcano Geodesy.” Journal of Volcanology and Geothermal Research 344:13–25.CrossRefGoogle Scholar