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Models, philosophy, logic: A model is obtained when a logical formula provides true statements by inserting objects instead of the free variables. One problem is the exclusion of unintended models. See also model theory.
Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments.

Author Concept Summary/Quotes Sources

Meteorology on Models - Dictionary of Arguments

Edwards I 371
Models/Meteorology/Climatology/Edwards: With ever more sophisticated interpolation algorithms and better methods for adjudicating differences between incoming data and the first-guess field, objective analysis became a modeling process in its own right.( >Weather forecasting/Edwards
Gridded analysis “products,” as they are known, constituted models of data, in Patrick Suppes’s well-known phrase(1): “structures into which data are embedded that add additional mathematical structure.”(2) The philosopher Ronald Giere once put the point as follows: . . . when testing the fit of a model with the world, one does not compare that model with data but with another model, a model of the data. . . .The actual data are processed in various ways so as to fit into a model of the
Edwards I 372
data. It is this latter model, and not the data itself, that is used to judge the similarity between the higher level model and the world. . . . It is models almost all the way down.(3) >Climate data/Edwards.
Weather forecasting: Traditionally, scientists and philosophers alike understood mathematical models as expressions of theory - as constructs that relate dependent and independent variables to one another according to physical laws. On this view, you make a model to test a theory (or one expression of a theory). You take some measurements, fill them in as values for initial conditions in the model, then solve the equations, iterating into the future. from the point of view of operational forecasting, the main goal of analysis is not to explain weather but to reproduce it. You are generating a global data image, simulating and observing at the same time, checking and adjusting your simulation and your observations against each other. As the philosopher Eric Winsberg has argued, simulation modeling of this sort doesn’t test theory; it applies theory. This mode—application, not justification, of theory - is “unfamiliar to most philosophy of science.”(4)
Edwards I 394
Models/data/Edwards: Meanwhile, global data sets are produced by simulations, which are constrained but not determined by instrumental observations. In earlier work I described this relationship as “model-data symbiosis,” a mutually beneficial but also mutually dependent relationship.(5) This idea aligns with recent work by philosophers of science on “models as mediators” - a semi-autonomous “third force” in science, functioning in the spaces between the real world, instrumentation, and theory.(6) As Margaret Morrison and Mary Morgan argue, Scientific models have certain features which enable us to treat them as a technology. They provide us with a tool for investigation, giving the user the potential to learn about the world or about theories or both. Because of their characteristics of autonomy and representational power, and their ability to effect a relation between scientific theories and the world, they can act as a powerful agent in the learning process. That is to say, models are both a means to and a source of knowledge.(7)
>Weather data/metereology, >Model bias/climatology.

1. P. Suppes, “Models of Data,” in Logic, Methodology, and the Philosophy of Science: Proceedings of the 1960 Congress, ed. E. Nagel et al. (Stanford University Press, 1962).
2. F. Suppe, “Understanding Scientific Theories: An Assessment of Developments, 1969–8,” Philosophy of Science 67 (2000), 112. See also S. D. Norton and F. Suppe, “Why Atmospheric Modeling Is Good Science,” in Changing the Atmosphere: Expert Knowledge and Environmental Governance, ed. C. A. Miller and P. N. Edwards (MIT Press, 2001).
3. R. N. Giere, “Using Models to Represent Reality,” in Model-Based Reasoning in Scientific Discovery, ed. L. Magnani et al. (Springer, 1999), 55.
4. E. Winsberg, “Sanctioning Models: The Epistemology of Simulation,” Science in Context 12, no. 2 (1999), 275.
5. Edwards, “Global Climate Science, Uncertainty and Politics.”
6.. Morgan and Morrison, Models as Mediators.
7. M. Morrison and M. S. Morgan, “Models as Mediating Instruments,” in Models as Mediators: Perspectives on Natural and Social Sciences, ed. M. S. Morgan and M. Morrison (Cambridge University Press, 1999).

Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments
The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
Edwards I
Paul N. Edwards
A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge 2013

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