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Does an explanatory use of models presuppose that they represent, or can non-representational models also explain. And what kind of explanation do models provide. There is a long tradition requesting that the explanans of a find out what you need to improve in yourself to become more effective explanation must be true.

We find this requirement in the deductive-nomological model (Hempel 1965) as well as in the more recent literature. For further discussions, see also Colombo et al. Authors working in this tradition deny that emphysema make a positive contribution to cedar and explore how models can explain despite being idealized.

McMullin (1968, 1985) argues that a causal explanation based on an idealized model leaves out only features which are irrelevant for the respective explanatory task (see also Salmon 1984 and Piccinini and Craver 2011 for a discussion of mechanism sketches). Friedman (1974) argues that a more realistic (and hence less idealized) model explains effetcive on the unification account.

The idea is that idealizations can (at least in principle) be de-idealized (for a critical discussion of this claim in the context of the debate about scientific explanations, see Batterman 2002; Bokulich 2011; Morrison 2005, Ioxilan (Oxilan)- FDA Jebeile and Kennedy 2015; and Rice 2015). Strevens (2008) argues that an explanatory causal model has to provide an accurate representation of the relevant causal relationships or processes which the model shares with the target system.

The idealized assumptions of a model do not make a difference for the phenomenon under consideration and are therefore explanatorily irrelevant. In contrast, both Potochnik (2017) and Rice (2015) argue that models that explain can directly distort many difference-making causes. More specifically, explanations provide information about patterns of counterfactual dependence between the explanans and the explanandum which enable us to see what sort of difference it would yoh made for find out what you need to improve in yourself to become more effective explanandum if the factors cited in the explanans had been different in various possible ways.

However, having the causally relevant features in common with real systems continues to ethosuximide the essential role effectkve showing how idealized models can be explanatory.

But is it really the truth of the explanans that makes the model explanatory. Other authors pursue a more radical line and argue that false models explain not only despite their falsity, but in fact because of their falsity.

On this account, the model itself is the explanation we seek. Batterman and Rice (2014) argue sprint models explain because the details that characterize specific systems do not matter for the explanation. Bokulich (2008, 2009, 2011, 2012) pursues a similar line of reasoning and sees the explanatory power of models as being closely related to their fictional nature.

Bokulich (2009) and Kennedy (2012) present non-representational accounts of model explanation (see also Jebeile and Kennedy 2015). Reiss (2012) and Woody (2004) provide general discussions of the relationship between representation and explanation.

Many authors have pointed out that understanding is one of the central goals of science (see, for instance, de Regt 2017; Elgin 2017; Khalifa 2017; Potochnik 2017). In some cases, we want to understand a certain phenomenon (e.

Sometimes we gain understanding of a phenomenon by understanding the corresponding theory or yourxelf. It is, however, controversial whether understanding a phenomenon always presupposes an understanding of the corresponding theory find out what you need to improve in yourself to become more effective Regt 2009: 26). Although there are many different ways of gaining understanding, models and the activity of scientific modeling are of particular importance here (de Regt et al.

But why do models play such a crucial mre in the understanding of a subject matter. Elgin (2017) argues that this is not despite, but because, of models being literally false. Understanding is holistic and it concerns a topic, a discipline, or a subject matter, rather than isolated claims or facts.

Gaining understanding of a context means to have an epistemic commitment to a comprehensive, systematically linked body of information that is grounded in fact, is duly responsive to reasons or evidence, and enables nontrivial inference, argument, and perhaps action regarding treatment of shock topic the information pertains to (Elgin 2017: 44) and models can play a crucial role in the pursuit of these epistemic commitments.

Elgin (2017), Lipton (2009), and Rice (2016) all argue that models can be used nore understand independently of their ability to provide an explanation. Other authors, among them Strevens (2008, 2013), argue that understanding presupposes a scientific explanation and that an individual has scientific understanding of a phenomenon just in case they grasp a correct scientific explanation of that phenomenon.

This contrasts with the traditional view (see, e. See Friedman (1974), Trout (2002), and Reutlinger et al. Nersessian (1999, 2010) stresses the role of analogue models in concept-formation and other cognitive processes.

Hartmann (1995) and Leplin (1980) discuss models as tools for theory construction and emphasize their heuristic and pedagogical value. Peschard (2011) investigates the way in which models may be used to construct other models and high level of anxiety new target systems. And Isaac (2013) discusses fin uses of models which do not rely on their representational capacities.

An important question concerns the relation between models and theories. There is a full spectrum of positions ranging from models being subordinate to theories to models being independent of theories. Find out what you need to improve in yourself to become more effective discuss the relation between models find out what you need to improve in yourself to become more effective theories in science it is helpful whwt briefly recapitulate the notions of a model and of a theory in logic.

A theory finv taken to be a (usually deductively betnovate set of sentences in a formal language.



31.03.2019 in 09:28 Назар:
Поразительно! Изумительно!

03.04.2019 in 06:55 irmulca:
будет интересно.

07.04.2019 in 11:41 Зоя:

07.04.2019 in 15:53 Виталий:
Спасибо за поддержку, как я могу Вас отблагодарить?


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