Models and Explanation

  • Alisa Bokulich
Part of the Springer Handbooks book series (SHB)


Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but also in their received scientific explanations. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: Can the fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory how-possibly model explanation from a genuine how-actually model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions.


Model Explanation Causal Explanation Scientific Explanation Counterfactual Dependence Functional Abstraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

mechanism-model-mapping constraint




fast enabling link




reinforcement learning


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© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Center for Philosophy and History of ScienceBoston UniversityBostonUSA

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