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首页> 外文会议>Model-based reasoning in science and technology : Abduction, logic, and computational discovery >Virtuous Distortion Abstraction and Idealization in Model-Based Science
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Virtuous Distortion Abstraction and Idealization in Model-Based Science

机译：基于模型的科学中的良性失真抽象和理想化

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The use of models in the construction of scientific theories is as wide-spread as it is philosophically interesting (and, one might say, vexing). Neither in philosophical analysis nor scientific practice do we find a univo-cal concept of model; but there is an established usage in which a model is constituted, at least in part, by the theorist's idealizations and abstractions. Idealizations are expressed by statements known to be false. Abstractions are achieved by suppressing what is known to be true. Idealizations, we might say, over-represent empirical phenomena, whereas abstractions under-represent them. Accordingly, we might think of idealizations and abstractions as one another's duals. In saying what is false and failing to say what is true, idealization and abstraction introduce distortions into scientific theories. Even so, the received and deeply entrenched view of scientists and philosophers is that these distortions are both necessary and virtuous. A good many people who hold this view see the good of models as merely instrumental, in a sense intended to contrast with 'cognitive'. Others, however, take the stronger and more philosophically challenging position that the good done by these aspects of scientific modeling is cognitive in nature. Roughly speaking, something has instrumental value when it helps produce a result that 'works'. Something has cognitive value when it helps produce knowledge. Accordingly, a short way of making the cognitive virtue claim is as follows: Saying what's false and suppressing what is true is, for wide ranges of cases,indispensable to the production of scientific knowledge. Given the sheer volume of traffic in the modeling literature, focused discussions of what makes these distortions facilitators of scientific knowledge attracts comparatively slight analytical attention by philosophers of science and philosophically-minded scientists. This is perhaps less true of the distortions effected by abstraction than those constituted by idealization. Still, in relation to the scale of use of the models methodology, these discussions aren't remotely as widespread and, when even they do occur, are not particularly 'thick'. The principal purpose of this paper is to thicken the analysis of the cognitive virtuosity of falsehood-telling and truth-suppression. The analysis will emphasize the influence of these factors on scientific understanding.
机译：在科学理论的构建中，模型的使用已广泛传播，这在哲学上引起了人们的兴趣（也许有人会说令人讨厌）。无论是在哲学分析还是科学实践中，我们都找不到模型的单一概念。但是存在一种确定的用法，其中模型至少部分地由理论家的理想化和抽象构成。理想化由已知为错误的陈述表达。通过抑制已知的事实来实现抽象。我们可以说，理想化过度代表了经验现象，而抽象化却不足以代表它们。因此，我们可能认为理想化和抽象是彼此的对偶。理想化和抽象在说什么是错误的而没有说什么是真实的时，将歪曲引入了科学理论中。即便如此，科学家和哲学家普遍接受的，根深蒂固的观点是，这些扭曲既是必要的，也是道德的。持这种观点的许多人认为模型的优点只是工具性的，在某种意义上是与“认知”形成对比的。然而，其他人则认为科学建模的这些方面所取得的成就本质上是认知，因此采取了更强有力，更具哲学挑战性的立场。粗略地说，某种事物在帮助产生“有效”结果时具有工具价值。当某物有助于产生知识时，它具有认知价值。因此，提出认知美德主张的一种简短方法如下：在广泛的情况下，说错话而压抑真话是产生科学知识必不可少的。考虑到建模文献中的通信量非常大，因此，有关引起这些扭曲的科学知识的成因的讨论引起了科学哲学家和具有哲学思想的科学家的相对较少的分析关注。对于抽象所造成的失真，可能比理想化所造成的失真更不正确。但是，相对于模型方法的使用规模，这些讨论并不那么广泛，即使发生了，讨论也不是特别“厚实”。本文的主要目的是加深对虚假陈述和真相抑制的认知技巧的分析。分析将强调这些因素对科学理解的影响。

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