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Introduction Learning from models Problems with the pretense view Saving the fiction view References

Models, Fictions, and Representing Scientific Practice:

(Or, I don’t know much about models... but I know that we learn from them*)

Corey Dethier

University of Notre Dame Philosophy Department corey.dethier@gmail.com

March 17, 2018 Models and Simulations *subtitle with apologies to Steven French

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Introduction

Introduction Learning from models Problems with the pretense view Saving the fiction view References

The fiction view of models

Fiction view of models: ‚ Family of positions defended by Barberousse and Ludwig (2009); Frigg (2010a,b); Frigg and Nguyen (2016); Godfrey-Smith (2006); Gr¨ une-Yanoff (2009, 94); Levy (2015); Salis (2016); and Toon (2012). ‚ Models (and their constituents) should be understood or analyzed in the same manner that works of fiction (and their constituents) are analyzed.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

The pretense view of models

In practice, the fiction view of models is really the pretense view of models. ‚ Defenders of the fiction view analyze models according to Walton (1990)’s “pretense” account of fictions. ‚ Accompanied by a technical move of attaching an operator to the sentences that are “pretend-true”—e.g., “According to the fiction, P” or “It is true within the pretense that P.”

Introduction Learning from models Problems with the pretense view Saving the fiction view References

My argument

I’ll argue that: (a) There is a class of comparisons that play an essential role in

• ur ability to learn from models.

(b) The technical move introduced above is incapable of rendering these comparisons true; and “true according to the model” won’t cut it. (c) There are technical resources within the literature on fiction that can render such comparisons true. (d) The apparent costs of these resources should be accepted (by defenders of the fiction view).

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Learning from models

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Learning from models (a first pass)

We learn from a model if (and only if?): the model allows us to justify a belief that we were not previously justified in holding. E.g., from a point-mass model of the solar system, we can learn how the planets will accelerate (to a high degree of approximation); we can read the accelerations off of the model. But notice that we can’t learn about the density of the planets from such a model even though we can read these off the model too; the model doesn’t justify conclusions about density.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Learning from models and justification

The problem: what distinguishes these cases? Prior knowledge about the model: we know that it accurately represents distances and masses, but misrepresents planet sizes. And the latter plays a role in reaching conclusions about density, while only the former play a role in reaching an (approximate) conclusion about acceleration.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Representing learning from models

In order to faithfully represent learning from models, our account

• f scientific models must allow us to capture the difference

between these two cases—between the conclusions that the model justifies and those it doesn’t. Since (in at least some cases), this difference is grounded in similarities and dissimilarities between the model and the target system, faithfully representing learning from models requires that

• ur account allow us to capture these facts about the relationship

between model and world.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

The necessity of true comparisons

In other words, our account of scientific models must allow us to render certain comparisons—such as (1) below—true, or at least to discriminate between them and comparisons like (2). (1) The planets (in the model) have the same mass as they do in the world. (2) The planets (in the model) have the same volume as they do in the world. This distinction between true and false comparisons underwrites learning from models; if we can’t rule some comparisons acceptable in a way that others aren’t, we can’t represent why some conclusions are justified and others aren’t. (Notice that (1) is a comparison between an object in the model and one in the world; this will be important later.)

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Problems with the pretense view

Introduction Learning from models Problems with the pretense view Saving the fiction view References

The pretense view

The pretense view: Models (and their constituents) should be understood as reasoning that occurs within a “pretense.” Effectively, this means attaching a sentence-level operator or distinguishing between true sentences and sentences that are merely “true-in-the-pretense.” E.g., “According to the pretense / model, P.” “It is true-in-the-pretense / -model that P.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Advantages of the pretense / fiction view

Does relatively well at representing modeling practice (` a la French and Ladyman [1999]). Like works of fiction, models ‚ come in many shapes and sizes. ‚ are used for many purposes. ‚ have parts that are accurate to the world and parts that aren’t. ‚ teach us lessons about the real world. ‚ require creativity. ‚ employ props or proxies. ‚ facilitate understanding.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

How should the pretense view handle (1)?

Only two options on the pretense view for capturing what makes (1) a good comparison: Option 1: (1) is true (tout court) and (2) is false. Option 2: (1) is false but true-in-the-pretense, while (2) is just false. Neither option will work. First option won’t work: whether or model is abstract or physical and made of foam, etc., the planets just don’t have the same mass in the model and in the world!

Introduction Learning from models Problems with the pretense view Saving the fiction view References

What about true-in-the-pretense?

Simply: “it is true-in-the-pretense that P” just doesn’t say the same thing as “P.” For example, (3) and (4) have different truth values: (3) Sherlock Holmes is a better detective than any real detective. (4) According to the pretense, Sherlock Holmes is a better detective than any real detective.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Pretense view cannot account for learning

Worse: in the context of models, the “according to the pretense” claim just won’t do for the purposes of learning. If (1) is true, then the model is accurate (with respect to mass); if it is accurate, we can learn from it. If (1) is merely true-in-the-pretense, then all we know is that the model says (of itself) that is accurate. That doesn’t license inferences!

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Options for the pretense view?

Frigg (2010b) suggests a paraphrase strategy: (5) Jupiter (in the model) has the same mass that Jupiter actually has. (6) According to the model, Jupiter has a mass of 1.9 ˆ 1027kg. Jupiter has a mass of 1.9 ˆ 1027kg. Having a mass of 1.9 ˆ 1027kg is equivalent ot having a mass of 1.9 ˆ 1027kg. I.e., remove all reference to the constituents and only compare the properties.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

The necessity of constituents

Compare (6) to (7): (7) According to the model, Mercury has a mass of 1.9 ˆ 1027kg. Jupiter has a mass of 1.9 ˆ 1027kg. Having a mass of 1.9 ˆ 1027kg is equivalent ot having a mass of 1.9 ˆ 1027kg. Why does (6), but not (7), license inferences about the actual solar system? Hard to see how we’re going to spell this out without sneaking in reference to the constituents of the model; after all, what’s wrong is that the model assigns the right mass, but to the wrong object!

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Saving the fiction view

Introduction Learning from models Problems with the pretense view Saving the fiction view References

A different technical move, part I

In order to illustrate my alternative, let’s return to fiction. Compare the following two sentences. (8) According to Leslie Marmon Silko’s Ceremony, Tayo is troubled by his experiences during the war. (9) Tayo, as he is depicted in Leslie Marmon Silko’s Ceremony, is troubled by his experiences during the war. Pre-theoretically, these communicate essentially the same thing. Structurally, however, they’re quite different.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

A different technical move, part II

Unlike (9), (8) allows us to infer (10) (10) There is someone who is depicted as troubled by his experiences during the war in the same way that actual veterans were troubled by similar experiences. Why? Because in (9), Tayo appears outside of the scope of the “according to” operator.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

A different technical move, part III

The pretense view relies on a technical move that distinguishes between the real world and fiction at the level of truth-bearers (e.g., sentences). That’s insufficient for the comparisons we’re interested in. So instead we need to distinguish between the real world and fiction at the level of predication. ‚ E.g., a is a P vs. a is depicted as a P; a has P vs. a holds P ‚ Versions of the move found in van Inwagen (1977), Zalta (1983), Thomasson (1997), Azzouni (2010)

Introduction Learning from models Problems with the pretense view Saving the fiction view References

True (tout court) comparisons

This move allows us to give a true (true tout court!) paraphrase of (1): (11) The planets, as depicted by the model, have the same mass as they do in the world. This sort of comparison can justify the conclusions that we’re interested in: why are we confident that the model accurately depicts accelerations? Because it accurately depicts masses.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

A mountain out of a molehill?

This solution seems so simple; what’s the upshot of the difference? In our account, the constituents of the model / fiction—Tayo, Sherlock Holmes, the planets, etc.—appear in outside the scope of the “according to the pretense” operator. They therefore appear not just in the domain of the pretense, but also in the domain of the meta-language. Is this a worrying cost?

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Not a worrying cost

I don’t think we should be worried about the cost, however, for (again) two reasons: ‚ First, the predication-level move I’m advocating can be found in Quinean, Meinongian, neo-Carnapian, or nominalist

• ntologies.

‚ Second, the whole point of the fiction view is that models

• ught to be analyzed in the same way that fictions are.

So: better representation of the phenomenon of modeling, no worrying costs.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

Conclusion

I’ve argued for three main conclusions:

• 1. Learning from models requires us to be able to separate true

comparisons between constituents of models and constituents

• f the world and false comparisons of this sort.
• 2. The pretense view of models cannot make this separation.
• 3. Other versions of the fiction view can, and should therefore be

preferred.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

References

Azzouni, Jody. 2010. Talking about Nothing: Numbers, Hallucinations, and

• Fictions. Oxford: Oxford University Press.

Barberousse, Anouk and Ludwig, Pascal. 2009. “Models as Fictions” in Fictions in Science: Philosophical Essays on Modeling and Idealization,

• ed. Su`

arez, Mauricio, 56-73. New York, NY: Routledge. Batterman, Robert and Rice, Collin. 2014. “Minimal Model Explanations” Philosophy of Science 81.3: 349-76. French, Steven and Ladyman, James. 1999. “Reinflating the Semantic Approach” International Studies in the Philosophy of Science 13.2: 103-21. Frigg, Roman. 2010a. “Fiction and Scientific Representation” in Beyond Mimesis and Convention: Representation in Art and Science, ed. Frigg, Roman and Hunter, Matthew, 97-138. Dordrecht: Springer. Frigg, Roman. 2010b. “Models and Fiction,” Synthese 172.2: 251-68. Frigg, Roman and Nguyen, James. 2016. “The Fiction View of Models Reloaded” The Monist 99.3: 225-48.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

References

Godfrey-Smith, Peter. 2006. “The Strategy of Model-Based Science” Biology and Philosophy 21.5: 725-40. Godfrey-Smith, Peter. 2009. “Models and Fictions in Science” Philosophical Studies 143.1: 101-16. Gr¨ une-Yanoff, Till. 2009. “Learning from Minimal Economic Models” Erkenntnis 70.1: 81-99. Levy, Arnon. 2015. “Modeling without Models” Philosophical Studies 172.3: 781-98. Salis, Fiora. 2016. “The Nature of Model-World Comparisons” The Monist 99.3: 243-59. Norton. Smith, George E. 2014. “Closing the Loop: Testing Newtonian Gravity, Then and Now” in Newton and Empiricism, ed. Beiner, Zvi and Schliesser, Eric. Oxford: Oxford University Press, 262-351. Thomasson, Amie. 1997. Fiction and Metaphysics. Cambridge: Cambridge University Press.

Introduction Learning from models Problems with the pretense view Saving the fiction view References

References

Thomson-Jones, Martin. 2010. “Missing Systems and the Face Value Practice” Synthese 172.2: 283-99. Toon, Adam. 2012. Models as Make-Believe: Imagination, Fiction and Scientific Representation. New York, NY: Palgrave Macmillan. van Inwagen, Peter. 1977. “Creatures of Fiction” American Philosophical Quarterly 14.4: 299-308. Walton, Kendall L. 1990. Mimesis as Make-Believe. Cambrdige, MA: Harvard University Press. Zalta, Ed. 1983. Abstract Objects: An Introduction to Axiomatic

• Metaphysics. Dordrecht: D. Reidel.