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Towards a Best Predictive System Account of Laws of Nature

This article argues for a revised best system account (BSA) of laws of nature. David Lewis’s original BSA has two main elements. On the one hand, there is the Humean base, which is the totality of particular matters of fact that obtain in the history of the universe. On the other hand, there is what I call the ‘nomic formula’, which is a particular operation that gets applied to the Humean base in order to output the laws of nature. My revised account focuses on this latter element of the view. Lewis conceives of the nomic formula as a balance of simplicity and strength, but I argue that this is a mistake. Instead, I motivate and develop a different proposal for the standards that figure into the nomic formula, and I suggest a rationale for why these should be the correct standards. Specifically, I argue that the nomic formula should be conceived as a collection of desiderata designed to generate principles that are predictively useful to creatures like us. The resulting view—which I call the ‘best predictive system’ account of laws—is thus able to explain why scientists are interested in discovering the laws, and it also gives rise to laws with the sorts of features that we find in actual scientific practice 1. Introduction2. The LOPP3. A Problem with Lewis's Formula4. A Pragmatic Account of the Nomic Formula 4.1. Informative dynamics4.2. Wide applicability4.3. Spatial locality4.4. Temporal locality4.5. Spatial, temporal, and rotational symmetries4.6. Predictively useful properties4.7. Simplicity4.8. Recap 5 Conclusion Introduction The LOPP A Problem with Lewis's Formula A Pragmatic Account of the Nomic Formula 4.1. Informative dynamics4.2. Wide applicability4.3. Spatial locality4.4. Temporal locality4.5. Spatial, temporal, and rotational symmetries4.6. Predictively useful properties4.7. Simplicity4.8. Recap Informative dynamics Wide applicability Spatial locality Temporal locality Spatial, temporal, and rotational symmetries Predictively useful properties Simplicity Recap 5 Conclusion