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Intro; Foreword; Preface; Acknowledgements; Contents; 1 Laws of Nature and the Problem of Exceptions; 1.1 The Received View of Laws; 1.1.1 The Ubiquity of Laws; 1.1.2 Tracing the Development of the Received View; 1.2 Enter Exceptions; 1.2.1 Galileo's Idealizations; 1.2.2 Hempel, Cartwright and Giere on Physical Laws; 1.2.3 Fodor and Schiffer on Special Science Laws; 1.2.4 Taking Exceptions Seriously: Braddon-Mitchell and Schrenk; 1.3 Skeptical Solutions; 1.3.1 Hedging; 1.3.2 Concretization; 1.3.3 Selectivism; 1.3.4 Nomic Eliminativism; 1.4 A Taxonomy of Non-universal Laws
1.4.1 Type-A: Ideal Laws1.4.2 Type-B: Ceteris Paribus Laws; 1.4.3 Type-C: Chancy Laws; References; 2 Governing Law Solutions to Ideal Laws; 2.1 Laws as Relations of Nomic Necessity; 2.1.1 Armstrong's Theory; 2.1.2 Iron Versus Oaken Laws; 2.1.3 Ideal Laws and Uninstantiated Laws; 2.2 Laws as Ascriptions of Capacities; 2.2.1 Cartwright's Theory; 2.2.2 Capacities for Ideal Laws; 2.2.3 Hüttemann's Capacities for Ideal Laws; 2.3 Scientific Essentialism; 2.3.1 Idealization as a Means to Uncover Essential Natures; 2.3.2 The Problem of Abstraction; References
3 Non-governing Law Solutions to Ideal Laws3.1 The Best System Account; 3.1.1 Laws as Axioms in a Deductive System; 3.1.2 Considerations from Strength; 3.1.3 Considerations from Simplicity; 3.2 Better Best System Accounts; 3.2.1 Schrenk's Special Science Index Laws; 3.2.2 Unterhuber's Generic Construal; 3.3 The Inference-Ticket View; 3.3.1 Statements of Fact or Rules of Inference?; 3.3.2 Problems for the Inference-Ticket View; References; 4 The Algorithmic Theory of Laws; 4.1 Science and Data Compression; 4.1.1 Simplicity and Economy in Scientific Theory
4.1.2 Compression as an Understanding of Simplicity4.1.3 Laws as Compression Algorithms; 4.2 The Theory Outlined; 4.2.1 Algorithmic Information Theory; 4.2.2 Laws of Nature as Maximal Compressors; 4.2.3 Objectivity and the Trivialization Problem; 4.3 Idealization and Lossy Compression; 4.3.1 Lossy Compression in Practice; 4.3.2 Predictive Redundancy; 4.3.3 Theory-Driven Data Processing; References
1.4.1 Type-A: Ideal Laws1.4.2 Type-B: Ceteris Paribus Laws; 1.4.3 Type-C: Chancy Laws; References; 2 Governing Law Solutions to Ideal Laws; 2.1 Laws as Relations of Nomic Necessity; 2.1.1 Armstrong's Theory; 2.1.2 Iron Versus Oaken Laws; 2.1.3 Ideal Laws and Uninstantiated Laws; 2.2 Laws as Ascriptions of Capacities; 2.2.1 Cartwright's Theory; 2.2.2 Capacities for Ideal Laws; 2.2.3 Hüttemann's Capacities for Ideal Laws; 2.3 Scientific Essentialism; 2.3.1 Idealization as a Means to Uncover Essential Natures; 2.3.2 The Problem of Abstraction; References
3 Non-governing Law Solutions to Ideal Laws3.1 The Best System Account; 3.1.1 Laws as Axioms in a Deductive System; 3.1.2 Considerations from Strength; 3.1.3 Considerations from Simplicity; 3.2 Better Best System Accounts; 3.2.1 Schrenk's Special Science Index Laws; 3.2.2 Unterhuber's Generic Construal; 3.3 The Inference-Ticket View; 3.3.1 Statements of Fact or Rules of Inference?; 3.3.2 Problems for the Inference-Ticket View; References; 4 The Algorithmic Theory of Laws; 4.1 Science and Data Compression; 4.1.1 Simplicity and Economy in Scientific Theory
4.1.2 Compression as an Understanding of Simplicity4.1.3 Laws as Compression Algorithms; 4.2 The Theory Outlined; 4.2.1 Algorithmic Information Theory; 4.2.2 Laws of Nature as Maximal Compressors; 4.2.3 Objectivity and the Trivialization Problem; 4.3 Idealization and Lossy Compression; 4.3.1 Lossy Compression in Practice; 4.3.2 Predictive Redundancy; 4.3.3 Theory-Driven Data Processing; References