David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophical Studies 160 (1):1-29 (2012)
Critics of contemporary metaphysics argue that it attempts to do the hard work of science from the ease of the armchair. Physics, not metaphysics, tells us about the fundamental facts of the world, and empirical psychology is best placed to reveal the content of our concepts about the world. Exploring and understanding the world through metaphysical reflection is obsolete. In this paper, I will show why this critique of metaphysics fails, arguing that metaphysical methods used to make claims about the world are similar to scientific methods used to make claims about the world, but that the subjects of metaphysics are not the subjects of science. Those who argue that metaphysics uses a problematic methodology to make claims about subjects better covered by natural science get the situation exactly the wrong way around: metaphysics has a distinctive subject matter, not a distinctive methodology. The questions metaphysicians address are different from those of scientists, but the methods employed to develop and select theories are similar. In the first section of the paper, I will describe the sort of subject matter that metaphysics tends to engage with. In the second section of the paper, I will show how metaphysical theories are classes of models and discuss the roles of experience, common sense and thought experiments in the construction and evaluation of such models. Finally, in the last section I will discuss the way these methodological points help us to understand the metaphysical project. Getting the right account of the metaphysical method allows us to better understand the relationship between science and metaphysics, to explain why doing metaphysics successfully involves having a range of different theories (instead of consensus on a single theory), to understand the role of thought experiments involving fictional worlds, and to situate metaphysical realism in a scientifically realist context
|Keywords||Metaphysics Methodology Science Models Inference to the best explanation Intuitions Common sense Kant Theories Empirical equivalence Simplicity Theoretical virtues Explanation|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Ross P. Cameron (2007). The Contingency of Composition. Philosophical Studies 136 (1):99-121.
Stephen M. Downes (1992). The Importance of Models in Theorizing: A Deflationary Semantic View. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:142 - 153.
Michael Friedman (2001). Dynamics of Reason: The 1999 Kant Lectures at Stanford University. Csli Publications.
Peter Godfrey-Smith (2006). Theories and Models in Metaphysics. The Harvard Review of Philosophy 14 (1):4-19.
Peter Godfrey-Smith (2006). The Strategy of Model-Based Science. Biology and Philosophy 21 (5):725-740.
Citations of this work BETA
Barry Loewer (2012). Two Accounts of Laws and Time. Philosophical Studies 160 (1):115-137.
L. A. Paul (2012). Building the World From its Fundamental Constituents. Philosophical Studies 158 (2):221-256.
Jiri Benovsky (2013). From Experience to Metaphysics: On Experience‐Based Intuitions and Their Role in Metaphysics. Noûs 47 (3).
M. B. Willard (2013). Game Called on Account of Fog: Metametaphysics and Epistemic Dismissivism. [REVIEW] Philosophical Studies 164 (1):1-14.
Similar books and articles
C. L. Hardin & W. J. Hardin (2006). A Tale of Hoffman. Consciousness and Cognition 15 (1):46-47.
Richard M. Shiffrin (2010). Perspectives on Modeling in Cognitive Science. Topics in Cognitive Science 2 (4):736-750.
R. Eric O'Connor (1938). The Handmaiden of the Sciences. Thought 13 (3):501-502.
Robert L. Ashenhurst (1996). Ontological Aspects of Information Modeling. Minds and Machines 6 (3):287-394.
Laurence Thomas (2012). Self‐Deception as the Handmaiden of Evil. Midwest Studies in Philosophy 36 (1):53-61.
Patrick Grim & Nicholas Rescher (2013). How Modeling Can Go Wrong. Philosophy and Technology 26 (1):75-80.
Norman J. Wells (1998). Descartes and Suárez on Secondary Qualities a Tale of Two Readings. Review of Metaphysics 51 (3):565 - 604.
Rasmus Grønfeldt Winther (2012). Mathematical Modeling in Biology: Philosophy and Pragmatics. Frontiers in Plant Evolution and Development 2012:1-3.
Ron Sun (2008). Introduction to Computational Cognitive Modeling. In , The Cambridge Handbook of Computational Psychology. Cambridge University Press. 3--19.
Timothy R. Colburn (1998). Information Modeling Aspects of Software Development. Minds and Machines 8 (3):375-393.
Arthur M. Jacobs & Jonathan Grainger (1999). Modeling a Theory Without a Model Theory, or, Computational Modeling “After Feyerabend”. Behavioral and Brain Sciences 22 (1):46-47.
Wesley Cooper (2008). An Eldritch Tale. Philo 11 (2):133-144.
Added to index2012-04-27
Total downloads115 ( #8,943 of 1,103,039 )
Recent downloads (6 months)24 ( #6,539 of 1,103,039 )
How can I increase my downloads?