完备性(序理论)
抽象
认识论
解释力
推论
可能性
机制(生物学)
规范(哲学)
科学哲学
计算机科学
心理学
数理经济学
数学
哲学
逻辑回归
机器学习
数学分析
纯数学
作者
Carl F. Craver,David M. Kaplan
摘要
Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and articulate norms of completeness for mechanistic explanations that have no such unwanted implications. 1 Introduction2 A Balancing Act: When Do Details Matter?3 The Norms of Causal Explanation4 The Norms of Constitutive Explanation5 Salmon-Completeness6 From More Details to More Relevant Details7 Non-explanatory Virtues of Abstraction8 From Explanatory Models to Explanatory Knowledge9 Mechanistic Completeness Reconsidered10 Conclusion
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