How do models give us knowledge? The case of Carnot’s ideal heat engine

Abstract

Abstract:Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with the help of specific representational means and used in various ways, for example, for the purposes of scientific reasoning, theory construction and design of experiments and other artefacts. Furthermore, in this activity of modelling the model construction is intertwined with the construction of new phenomena, theoretical principles and new scientific concepts. We will illustrate these claims by studying the construction of the ideal heat engine by Sadi Carnot. . IntroductionIf there is any theme that unites philosophers as regards models it is that of representation. Models are generally presumed to be representations. While scientific models as specifically designed artefacts certainly belong to a class of public objects called representations, something more is implied by the idea of representation in the context of modelling. Namely, the claim that models are representations plays out their relational nature. Models are thought of as being inherently models of something and, more often than not, this something is understood in terms of some real objects, processes, or more generally, some natural "phenomena". Thus we take it to be commonly accepted in the philosophy of science that scientific models represent some real target phenomena -or target systems.This seeming agreement disguises the fact that different philosophers understand representation in vastly different ways, yet at the bottom of this consent seems to be the belief that models give us knowledge in virtue of representing (some selected aspects) of external world sufficiently accurately (Bailer-Jones 2003; da Costa and French 2000;French and Ladyman 1999;Frigg 2002; Morrison and Morgan 1999;Suárez 1999;Giere 2004). The representational conception of knowledge as that of accurate representation which underlies this belief has long roots in Western culture, a huge topic we cannot even hope to cover in this paper. However, we argue that if we are interested in how models give us knowledge in the actual scientific practice the representational approach to models proves too minimal. Although we do not want to dispute the fact that models often are used to represent some real target systems, we claim that the representational approach to models is rather

An engineering paradigm in the biomedical sciences: Knowledge as epistemic tool

“…Epistemic results (theories, models, laws, concepts) are epistemic tools constructed for epistemic uses such as construction of explanations and predictions, model-building (e.g., mechanistic, mathematical, experimental and synthetic models), creative thinking, problem-solving, computer simulations, and design of experimental equipment (Boon and Knuuttila 2009, Knuuttila and Boon 2011, Boon 2012a, Green 2013). 3.…”

“…How to validate computer models for biomedical applications, for instance, is the topic of this special issue. Knuuttila and Boon (2011) argue that much of the justification of a scientific model takes place when building it (rather than by experimental tests only).…”

“…Instead of scientific knowledge firstly being a representation of how the world is independent of humans, they propose to characterize knowledge as epistemic tools (Nersessian 2009, Boon and Knuuttila 2009, Feest 2010, Knuuttila and Boon 2011, Boon 2012a, Green 2013. 3 Here, it is suggested that 'knowledge as representation' (in a correspondence sense) is deeply embedded in the physics paradigm whereas 'knowledge as epistemic tool' is central to the engineering paradigm.…”

Models as Relational Categories

“…When allowing models a relative autonomy from theories, we may ignore, for example, some causal factors in order to explore certain others and in doing to render the models Bunrealistic.^Indeed, within philosophy of science, it has been argued that modeling often does not strive for realistic representations of its targets; it suffices that they are representative (Morrison and Morgan 1999). Some argue that this is precisely the aspect of modeling that makes models work and allows them to be conveniently mathematically represented (Humphreys 2004, p. 84-86, 116-124;Knuuttila and Boon 2011;Knuuttila 2011;Koponen 2007). Moreover, this is what allows us to learn from the models (Knuuttila 2011;Knuuttila and Boon 2011).…”

“…Some argue that this is precisely the aspect of modeling that makes models work and allows them to be conveniently mathematically represented (Humphreys 2004, p. 84-86, 116-124;Knuuttila and Boon 2011;Knuuttila 2011;Koponen 2007). Moreover, this is what allows us to learn from the models (Knuuttila 2011;Knuuttila and Boon 2011). Interestingly, as a consequence, the abstractions may make the results derived from the models intractable.…”