3.3 Criticisms of the Integration Account of Modeling
There are three primary objections to the integration account of modeling. First, it is guilty of the fallacy of composition; second, the account does not help us distinguish models from non-models; and third, it commits the naturalistic fallacy.
3.3.1 Parts/Wholes
The most obvious objection to the integration account is that it commits the fallacy of composition. According to this fallacy, a whole is much more than simply the sum of its parts. For example, if we ask someone what a car is and she answers that a car is made up of: an engine, spark plugs, a timing belt, an air filter, muffler, tires, doors, steering wheel, seats, etc., we would argue that she has not adequately answered the question. Although it is true that a car is composed of all of these parts, it does not follow that this is all there is to being a car. Even the most exhaustive list of components will not tell you anything about the purpose of a car, which seems to be inimical when
considering what a car is. Similarly, the integration account may tell us all about the different components that go into constructing a model, but this does not mean it tells us what a model is. According to Morrison and Morgan, models are important because they
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“are a source of both explanatory and predictive power,”23 while Suarez maintains that their significance has more to do with their ability to help agents make inferences about the target being modeled.24 If the integration account of modeling does not tell us anything about a model’s ability to explain, predict, or draw inferences, then its applicability is severely limited and thus, ultimately flawed.
3.3.2 Models vs. Non-models
The integration account of modeling provides us with a poor account of modeling because it does not allow us to distinguish models from other scientific concepts like theories and laws. For instance, if we take the definition of model in section 4.1.3 and substitute the word ‘theory’ for ‘model,’ we have a working definition of ‘theory’: A theory is a temporary collection of integrated “items” whose purpose is to contribute to human knowledge by helping scientists solve different kinds of scientific problems, while at the same time also making them aware of untenable research programs. A good
definition provides both necessary and sufficient conditions for a concept. The integration account of modeling supplies us with the former, but not the latter and that is why the terms ‘model’ and ‘theory’ become interchangeable.
3.3.3 Naturalistic Fallacy
A third criticism of the integration account is that it is guilty of the naturalistic fallacy. According to one version of the fallacy, the difficult task of evaluating the
validity of x is replaced by an explanation of how x comes to be. For instance, naturalized epistemology is concerned with all of the psychological processes that produce our
23. Morrison and Morgan, “Introduction,” in Morgan and Morrison, Models as Mediators, 6.
24. Suarez, “An Inferential Conception of Scientific Representation,” 773.
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beliefs; it is not at all concerned with justification. Quoting W.V.Quine, “The stimulation of his sensory receptors is all the evidence anybody has had to go on, ultimately, in arriving at his picture of the world. Why not just see how this construction really proceeds? Why not settle for psychology?”25 Some philosophers, however, doubt if naturalist epistemology is even epistemology at all. Traditionally understood, epistemology has been the study of knowledge and its conditions. It is concerned primarily with two questions: (1) What is knowledge?, and (2) What is the criteria for knowledge?. According to Jaegwon Kim, if philosophers follow Quine’s lead and abandon these questions altogether, then not only will epistemology go out of business, but knowledge itself also will because knowledge and justification are “inseparably tied.”26 Traditional epistemologists agree that naturalized epistemology is an informative discipline with far reaching consequences regarding how we come to our beliefs, but because it does not concern itself with normative and evaluative concerns, it is not, and will never be, epistemology per se.
The more traditional way of framing this criticism is to argue that the integration account falls under the aegis of the context of discovery and not the context of
justification. Those who oppose a naturalized epistemology believe there is a strict dividing line between how a theory is discovered and its justification. Because the integration account only concerns itself with the psychological, social, and historical processes that lead to a discovery of a theory or a production of a model, it is a purely descriptive enterprise. The context of discovery, however, has nothing to do with the
25. W.V. Quine, “Epistemology Naturalized,” Knowledge: Readings in Contemporary
Epistemology, ed. Sven Bernecker and Fred Dretske (Oxford: Oxford University Press, 2000), 269 – 270.
26. Jaegwon Kim, “What is ‘Naturalized Epistemology?’,” in Bernecker and Dretske, Knowledge, 286.
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context of justification. Those sympathetic with the context of justification believe that it is one thing to describe the various contexts within which a scientific theory or model arises, wholly another to argue that the theory or model itself is acceptable. According to Harvey Siegel, “What is crucial for epistemology is not the actual train of thought which culminates in an epistemologically potent pronouncement; rather, epistemology is concerned with evaluating, with establishing the potency of, that pronouncement.”27 Put simply, discovery is not in the justification business.
Just as Kim accused Quine of naturalizing epistemology and in the process giving up on the most important task of epistemology (i.e. justification), it might similarly be argued that in settling for a naturalized account of models I am giving up on one of the most significant issues in scientific modeling and that is developing a criteria for models.
Those critical of naturalizing epistemology argue that it is one thing to list all of the different components that go into constructing various models and wholly another to list all of the conditions that distinguish models from non-models and good models from bad ones. Naturalized epistemology might be fruitful for burgeoning fields such as the cognitive science of science, but it is not very helpful for the field of scientific modeling that approaches scientific models from a strictly philosophical point of view.