Abstract
Despite the fact that scientific models and works of fiction are the products of the same tools, i.e. idealization or approximation, models differ from works of fiction because the idealization practices in science act as guiding instruments that lead to new knowledge. This epistemic function is characteristic of scientific models and it is a necessary condition for the success of a model, whether theory-driven or phenomenological. It is not a necessary condition for works of fiction because, when these works represent, they represent only the general features of their target thus shaping or improving our intuitions about those features of the respective targets; they do not necessarily lead to knowledge concerning the specific features of the target. Hence, if we treat scientific models as works of fiction in order to understand what models are and how they function, we run the risk of overlooking this distinct kind of epistemic function that models display.
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Notes
- 1.
I use the term ‘idealization’ in this paper as a generic term to refer to simplifications that result from the omission of features and to simplifications that result from modifications of the actual features of objects or of states of affairs. Some authors refer to the former as ‘abstraction’ and to the latter as ‘idealization’ and maintain that they are products of distinct thought processes, e.g. Cartwright [3]. Of course, there are important differences between the two kinds of simplification that have been pointed out by many authors. However, it is debatable whether these two kinds result from distinct thought processes; see for instance McMullin [12], who treats them as if they are the product of the same cognitive act. Whatever the outcome of such debate, the differences between the two kinds of simplification are largely irrelevant for the purposes of this work.
- 2.
- 3.
In the last few decades, with the advent of powerful computing machines, computer simulation techniques also perform this function.
- 4.
See Portides [18] for a more detailed description and a closer examination of phenomenological models.
- 5.
In the absence of reductive rules of classical functions to quantum mechanical operators, this is, of course, an arbitrary move. Although this is frequently the case in quantum mechanical modeling, it is not an issue of concern in this paper.
- 6.
I say ‘initial’ goal, because once a model is successful in its initial goal then it is refined, corrected or modified in order to meet other goals that an acceptable representation should. That is to say, the goals of a putative representational model are not necessarily all set in the very beginning but they can be subject to change throughout its evolutionary history.
- 7.
See Moszkowski [15] for more details regarding the liquid drop model.
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Portides, D. (2014). How Scientific Models Differ from Works of Fiction. In: Magnani, L. (eds) Model-Based Reasoning in Science and Technology. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37428-9_5
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