Models as Epistemic Tools in the Engineering Sciences.

Boon, M. (Speaker)

Activity: Talk or presentationOral presentation

Description

Contribution to the Symposium "The many faces of Epistemic Tools.ABSTRACT: Models and modeling practices are ubiquitous in the engineering sciences. In this context, Tarja Knuuttila and I (2009, 2011) have defended the notion of models as epistemic tool. The idea is that models are constructed entities that can be used by (skilled) epistemic agents for specific epistemic tasks, and in that very sense, models can be called a tool. Scientific models, for instance, guide and enable different kinds of inferential reasoning, such as in deductive, inductive, quantitative, qualitative, explanatory, predictive, investigative, creative, or hypothetical reasoning about the target system. The rough idea of scientific models as epistemic tools is that models are hubs in which relevant knowledge and information of all sorts are brought together and fused by the researchers into a coherent whole that allows for inferential reasoning. We have proposed that a scientific model usually can be systematically analyzed in terms of several ingredients that refer to choices made in the construction of the model (Boon 2019). These aspects are mutually related and must be made coherent, and can be summarized as (i) the technological problem context; (ii) the target-system or phenomenon (P) for which the model is built (where P is relevant to the broader problem context); (iii) the intended epistemic function of the model (e.g., in view of solving the problem); (iv) the model type (in view of the epistemic purpose); (v) the (physical) circumstances and properties relevant to the phenomenon or target system (in view of the previous aspects); (vi) the measurable (physical) variables (which also explains how the model is connected to the real-world); (vii) idealizations, simplifications and abstractions (in view of epistemic criteria and pragmatic constraints); (viii) knowledge and background principles relevant to P; (ix) hypotheses (e.g., new concepts or explanations); and (x) the testing of the model. Conversely, in the philosophy of science, scientific models are typically understood as representations of a specific target system, and philosophers have aimed at accounts of this representational relationship. Well-known is Giere’s (2002) similarity account of representation. Getting away from this kind of representational view of models appears to be hard. Answers to the question “in virtue of what a scientific model can function as an epistemic tool in scientific reasoning processes?” usually refer to the ‘correct’ representational relationship between model and target. In this paper, (1) philosophical arguments against the similarity view of models will be summarized, and (2) an account of why models can function as epistemic tools for reasoning about their target system will be presented by in-depth analysis of the interplay between the development of technological instruments, experiments and (scientific) models in experimental research practices. It is claimed that this epistemological process is guided by apparent ontological and epistemological presuppositions. Crucial to this account is the idea that ontological and epistemological principles must be understood as regulative principles that guide and enable research practices, not constitutive principles about reality. Examples from the engineering sciences are interesting because the modeled target system often does not exist in advance.References:Boon, M., & Knuuttila, T. (2009). Models as Epistemic Tools in Engineering Sciences: a Pragmatic Approach. In A. Meijers (Ed.), Philosophy of technology and engineering sciences. Handbook of the philosophy of science (Vol. 9, pp. 687-720): Elsevier/North-Holland.Knuuttila, T., & Boon, M. (2011). How do Models give us Knowledge? The case of Carnot’s ideal Heat Engine. European Journal for Philosophy of Science, 1(3), 309-334. doi:10.1007/s13194-011-0029-3 Boon, M. (2019). Methodology in the Engineering Sciences. in: The Routledge Handbook of Philosophy of Engineering. Diane Michelfelder and Neelke Doorn (eds.).Frigg, R. & Hartmann, S. (2018), Models in Science, The Stanford Encyclopedia of Philosophy (Summer 2018 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2018/entries/models-science/>.
Period7 Jul 2019 - 12 Jul 2019
Event title2019 International Society for the History Philosophy and Social Studies of Biology biennial meeting
Event typeConference
LocationOslo, Norway
Degree of RecognitionInternational