The Best Predictive System Account of Laws of Nature

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  • March 21, 2019
Creator
  • Dorst, Chris
    • Affiliation: College of Arts and Sciences, Department of Philosophy
Abstract
  • This dissertation develops a novel theory of laws of nature in the “Best System” tradition, with the express aim of making sense of why creatures like us are interested in discovering the laws. My theory draws inspiration from David Lewis’s famous Best System Account of laws. Lewis’s account has two basic elements: the “Humean base” and the “Nomic Formula.” The laws, according to Lewis, are the results of applying the Nomic Formula to the Humean base. My account preserves this overall structure of Lewis’s view, but I disagree with him both about what sorts of facts constitute the Humean base, and about the nature of the Nomic Formula itself. In the first two chapters of my dissertation, I develop objections to Lewis’s explications of these elements, and on the basis of these objections, I propose alternative accounts of the Humean base (Chapter 1) and the Nomic Formula (Chapter 2). In short, my view is that the laws of nature are the principles of the most predictively useful systematization of the totality of macroscopic phenomena. I call the resulting view the “Best Predictive System Account” of laws. In Chapter 3, then, I attempt to explain why the laws tend to be held fixed in counterfactual reasoning. I do so by arguing that, if the Best Predictive System Account is correct, creatures like us would naturally hold fixed the laws in counterfactual reasoning for purposes of figuring out facts about the actual world.
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Advisor
  • Hall, Edward
  • Lange, Marc
  • Paul, Laurie
  • Kotzen, Matthew
  • Roberts, John
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2018
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