Counterfactuals and Scientific Realism by Michael J. ShafferCounterfactuals and Scientific Realism by Michael J. Shaffer

Counterfactuals and Scientific Realism

byMichael J. Shaffer

Hardcover | August 13, 2012

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Scientific realism is the view that the sciences aim to discover scientific theories that are true, or at least approximately true. Scientific realism is widely accepted by both scientists and philosophers of science. However, in practice - and perhaps even in principle - scientists are forced to simplify theories by idealizing. So some more skeptical philosophers of science have questioned the acceptability of scientific realism because they claim that theories involving idealizations are not even approximately true. This book is an attempt to show that scientific realism is compatible with the presence of idealization in the sciences. The main contention of this book is that idealized theories can be treated as counterfactuals about how things are in worlds that are similar to but simpler than the actual world. So understood it is clear that they have perfectly ordinary truth conditions. So, scientific theories that incorporate idealizations can be true despite the objections of anti-realists.
MICHAEL SHAFFER is Associate Professor of Philosophy at St. Cloud State University, USA. He is the author of The Experimental Turn and the Methods of Philosophy, co-editor of What Place for the A Priori? and has published many articles about epistemology, logic and the philosophy of science.
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Title:Counterfactuals and Scientific RealismFormat:HardcoverDimensions:240 pagesPublished:August 13, 2012Publisher:Palgrave Macmillan UKLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0230308457

ISBN - 13:9780230308459

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Table of Contents

Series Editor's Preface

Acknowledgements
Introduction
The Concept of Idealization
The Ubiquity of Idealization and Its Logic
Epistemic Access, Confirmation and Idealization
Idealization, Inference to the Best Explanation and Scientific Realism
References
Index