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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

Climatology on Models - Dictionary of Arguments

Edwards I 474
Models/climatology/Edwards: Simon Shackley, described two “epistemic lifestyles” in climate modeling. In his terms,
a) climate seers use models to “understand and explore the climate system, with particular emphasis on its sensitivity to changing variables and processes,” seeing the models as tools for this purpose. Meanwhile,
b) climate model constructors see models as an end in themselves. They seek to “capture the full complexity of the climate system [in models] which can then be used for various applications.” Climate model constructors are more likely to focus on increased “realism,” an adjective referring not to accuracy but to the inclusion in the model of all physical processes that influence the climate. Climate seers, by contrast, tend to focus on modeling the most fundamental and best understood processes, and to use a variety of different models, including simpler zero-, one-, and two-dimensional models.(1)
Edwards I 475
The model constructors’ approach also has a political dimension, since those who challenge GCM results often argue that they do not account for the effects of some unincluded process, such as cosmic rays.(2) Adding more processes reduces modelers’ vulnerability to this line of attack, though at the same time it increases the opportunities to question the accuracy of parameterizations.
Edwards I 476
Corrections: Controversies about tuning rage both inside and outside the climate modeling community. The philosopher-physicist Arthur Petersen notes that “simulationists hold divergent views on the norm of not adding ad hoc corrections to models.”(3) Some accept these corrections as necessary; others view them almost as morally suspect and seek to eliminate them. David Randall and Bruce Wielicki argue that tuning “artificially prevents a model from producing a bad result.” Noting that some modelers refer to tuning as “calibration”—exploiting that term’s positive connotations - Randall and Wielicki write: “Tuning is bad empiricism. Calibration is bad empiricism with a bag over its head.” Yet Randall and Wielicki also acknowledge that, in the case of physical processes that are known to be important but are not well understood, there may be no choice.(4) In general, modelers view tuning as a necessary evil. Most try to observe certain constraints. Tuning should not, for example, bring the tuned variable outside its known range of observed behavior.
Edwards I 478
Provability of models: the logic of simulation modeling does not require, or even permit, definitive proof. For example, parameterizations by definition do not derive from exact physical principles; no one expects them to prove perfectly accurate. Naomi Oreskes, Kristin Shrader-Frechette, and Kenneth Belitz argued in the pages of Science that talk of “verification” or “validation” of models was bad epistemology.(5) The word ‘verification’, they wrote, normally implies definitive proof. But models, Oreskes et al. argued, are essentially intricate inductive arguments.
Edwards: This implies only that model results agree with observations. This agreement, by itself, tells us nothing about whether the model reached its results for the right reasons.
, >Provability, >Observation.

1. Shackley. “Epistemic Lifestyles.” Changing the atmosphere: Expert knowledge and environmental governance, 107-33. 2001. Cambridge: MA MIT Press.
2. H. Svensmark and N. Calder, The Chilling Stars: The New Theory of Climate Change (Icon Books, 2007).
3. A. Petersen, Simulating Nature: A Philosophical Study of Computer-Simulation Uncertainties and Their Role in Climate Science and Policy Advice (Het Spinhuis, 2007), 39.
4. D. A. Randall and B. A. Wielicki, “Measurements, Models, and Hypotheses in the Atmospheric Sciences,” Bulletin of the American Meteorological Society 78, no. 3 (1997), 403–.
5. N. Oreskes et al., “Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences,” Science 263, no. 5147 (1994): 641–.

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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