Modellers of biological, ecological, and environmental systems cannot take for granted the maxim 'simple means general means good'. We argue here that viewing simple models as the main way to achieve generality may be an obstacle to the progress of ecological research. We show how complex models can be both desirable and general, and how simple and complex models can be linked together to produce broad-scale and predictive understanding of biological systems.
Representation has been one of the main themes in the recent discussion of models. Several authors have argued for a pragmatic approach to representation that takes users and their interpretations into account. It appears to me, however, that this emphasis on representation places excessive limitations on our view of models and their epistemic value. Models should rather be thought of as epistemic artifacts through which we gain knowledge in diverse ways. Approaching models this way stresses their materiality and media-specificity. Focusing on models as multifunctional artifacts releases them from any preestablished and fixed representational relationships and leads me to argue for a twofold approach to representation.
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
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