Abstract
In the current epistemological debate scientific models are not only considered as useful devices for explaining facts or discovering new entities, laws, and theories, but also rubricated under various new labels: from the classical ones, as abstract entities and idealizations, to the more recent, as fictions, surrogates, credible worlds, missing systems, make-believe, parables, functional, epistemic actions, revealing capacities.
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Notes
- 1.
In this perspective I basically agree with the distinction between epistemic and non-epistemic values as limpidly depicted in Steel (2010). In chapter eight I will illustrate how, in recent times, various kinds of what I call epistemic irresponsibility are precisely jeopardizing the efficiency of these “epistemic weapons”.
- 2.
Suárez’s approach to scientific models as fictions is actually more sophisticated than it may appear from my few notes. Basically, Suárez does not defend the view according to which models are fictions: even if he defends the view that models contain or lead to fictional assumptions, he explicitly rejects the identification of models and fictions, preferring instead to stay “quietist” about the ontology of models, and focusing rather on modeling as an activity—see in particular his introduction to the 2009 Routledge volume he edited entitled Fictions in Science (Suárez 2009b).
- 3.
I will reconsider the demarcation problem in Sect. 3.2.1 of the following chapter.
- 4.
The analytic literature on scientific models has recently clearly acknowledged both the role of scientific models in extended/distributed cognition but also their related capacity to favor “understanding”. The increase of explanatory inferential ability—which favors understanding—can in turn be broken down along different dimensions of explanatory power: non-sensitivity, precision, factual accuracy, degree of integration, and cognitive salience: “The explanatory power of the model, and consequently the amount and type of understanding it can provide, amounts to the number and importance of these inferences it enables. [...] Understanding the model and understanding with the model should be kept” (Kuorikoski and Ylikoski 2015, p. 3834). Toon (2015, p. 3874) too exploits extended cognition to propose a new gracious view of the nature of understanding: “Understanding is not always in the head. Instead, it involves brain, body and world”. Finally, on the problem of degrees of understanding of phenomena and its relationship with explanationist and manipulationist interpretation cf. the rich (Kelp 2015, p. 3794).
- 5.
Representational delegations are those cognitive acts that transform the natural environment in a cognitive one.
- 6.
I introduced this concept in the previous chapter, Sect. 1.4.1.
- 7.
An analysis of the differences between models in biology and physics and of the distinction between natural, concrete, and abstract models from a traditional epistemological perspective is illustrated in Rowbottom (2009). A comparison between experiments as commonly thought to have epistemic privilege over simulations provided by models is given by Parke (2014). The importance of the different roles played in science by thought experiments, simulations, and computer simulations is further studied in El Skaf and Imbert (2013), taking advantage of a unique conceptual framework.
- 8.
Thomson-Jones (2012) too acknowledges, even if in the framework of an analytic scenario, not indebted to cognitive science, the importance of “concrete” models, and establishes a novel and useful distinction between mathematical and non-mathematical models together with the concept of “propositional model”.
- 9.
The semiotic (iconic) status of models—in general—has also been extensively acknowledged by the recent analytic literature, cf. for example Kralemann and Lattmann (2013), who amply illustrate a Peircean-based approach. As also observed by the authors, the semiotic theory of models seems to contribute to the solution of the “ontological puzzle” of models.
- 10.
Actually the concept is much more complicated also illustrating its relationship with abductive cognition and cognitive niches. .
- 11.
The complicated analysis of some seminal Peircean philosophical considerations concerning abduction (which refers to all the cognitive processes that lead to hypotheses), perception, inference, and instinct, which I consider are still important to current cognitive and epistemological research, is provided in Magnani (2009, Chap. 5).
- 12.
- 13.
- 14.
“The basic argument for common coding is an adaptive one, where organisms are considered to be fundamentally action systems. In this view, sensory and cognitive systems evolved to support action, and they are therefore dynamically coupled to action systems in ways that help organisms act quickly and appropriately. Common coding, and the resultant replication of external movements in body coordinates, provides one form of highly efficient coupling. Since both biological and nonbiological movements are equally important to the organism, and the two movements interact in unpredictable ways, it is beneficial to replicate both types of movements in body coordinates, so that efficient responses can be generated” Chandrasekharan (2009, p. 1069): in this quoted paper the reader can find a rich reference to the recent literature on embodied cognition and common coding.
- 15.
Written natural languages are intertwined with iconic aspects too. Stjernfelt (2007) provides a full analysis of the role of icons and diagrams in Peircean philosophical and semiotic approach, also taking into account the Husserlian tradition of phenomenology.
- 16.
In a perspective that does not take into account the results of cognitive science but instead adopts the narrative/literary framework about models as make-believe, Toon (2010) too recognizes the role of external models in perturbing mental models to favor imagination: “Without taking a stance in the debate over proper names in fiction, I think we may use Walton’s analysis to provide an account of our prepared description and equation of motion. We saw [...] that these are not straightforward descriptions of the bouncing spring. Nevertheless, I believe, they do represent the spring, in Walton’s sense: they represent the spring by prescribing imaginings about it. When we put forward our prepared description and equation of motion, I think, those who are familiar with the process of theoretical modelling understand that they are to imagine certain things about the bouncing spring. Specifically, they are required to imagine that the bob is a point mass, that the spring exerts a linear restoring force, and so on” (p. 306).
- 17.
It is not surprising to find in recent analytic academic articles about models in science the reference to the concept of imagination: for example Levy nicely stresses that modeling is not only the representation of target phenomena, but is also intimately linked to the imagination, “[...] we utilize the imagination as a means of describing and reasoning about a real-world object. [...] models are imaginative descriptions of real-world phenomena” (Levy 2015, pp. 791 and 797). Also Meynell (2014, p. 4149) stresses the imaginative role of those particular kinds of models that are the thought experiments.
- 18.
On ignorance and epistemic irresponsibility see Chap. 8, Sect. 8.5.
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Magnani, L. (2017). Distributed Model-Based Science. In: The Abductive Structure of Scientific Creativity. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-59256-5_2
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