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E-raamat: Hume's Problem Solved

(Heinrich-Heine-Universität Düsseldorf)
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  • Ilmumisaeg: 07-May-2019
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  • Keel: eng
  • ISBN-13: 9780262352451
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  • Formaat: EPUB+DRM
  • Sari: The MIT Press
  • Ilmumisaeg: 07-May-2019
  • Kirjastus: MIT Press
  • Keel: eng
  • ISBN-13: 9780262352451

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A new approach to Hume's problem of induction that justifies the optimality of induction at the level of meta-induction.

A new approach to Hume's problem of induction that justifies the optimality of induction at the level of meta-induction.

Hume's problem of justifying induction has been among epistemology's greatest challenges for centuries. In this book, Gerhard Schurz proposes a new approach to Hume's problem. Acknowledging the force of Hume's arguments against the possibility of a noncircular justification of the reliability of induction, Schurz demonstrates instead the possibility of a noncircular justification of the optimality of induction, or, more precisely, of meta-induction (the application of induction to competing prediction models). Drawing on discoveries in computational learning theory, Schurz demonstrates that a regret-based learning strategy, attractivity-weighted meta-induction, is predictively optimal in all possible worlds among all prediction methods accessible to the epistemic agent. Moreover, the a priori justification of meta-induction generates a noncircular a posteriori justification of object induction. Taken together, these two results provide a noncircular solution to Hume's problem.

Schurz discusses the philosophical debate on the problem of induction, addressing all major attempts at a solution to Hume's problem and describing their shortcomings; presents a series of theorems, accompanied by a description of computer simulations illustrating the content of these theorems (with proofs presented in a mathematical appendix); and defends, refines, and applies core insights regarding the optimality of meta-induction, explaining applications in neighboring disciplines including forecasting sciences, cognitive science, social epistemology, and generalized evolution theory. Finally, Schurz generalizes the method of optimality-based justification to a new strategy of justification in epistemology, arguing that optimality justifications can avoid the problems of justificatory circularity and regress.

Preface ix
1 The Problem of Induction
1(10)
1.1 The Notion of Induction: Conceptual Clarifications
1(4)
1.2 David Hume and the Problem of Justifying Induction
5(3)
1.3 Plan of the Book
8(3)
2 On Failed Attempts to Solve the Problem of Induction
11(16)
2.1 Can Induction Be Avoided?
11(2)
2.2 Is Induction Rational "by Definition"? Rationality and Cognitive Success
13(3)
2.3 Can Induction Be Justified by Assumptions of Uniformity?
16(2)
2.4 Can Circular Justifications of Induction Have Epistemic Value?
18(4)
2.5 Can Induction Be Justified by Abduction or Inference to the Best Explanation?
22(2)
2.6 The Role of Induction and Abduction for Instrumentalism and Realism
24(3)
3 The Significance of Hume's Problem for Contemporary Epistemology
27(20)
3.1 The Aims of Epistemology
27(2)
3.2 Foundation-Oriented Epistemology and Its Main Problems
29(6)
3.3 Coherentism and Its Shortcomings
35(3)
3.4 Externalism and Its Shortcomings
38(5)
3.5 The Necessity of Reliability Indicators for the Social Spread of Knowledge
43(1)
3.6 Conclusion: A Plea for Foundation-Oriented Epistemology
44(3)
4 Are Probabilistic Justifications of Induction Possible?
47(30)
4.1 Why Genuine Confirmation Needs Induction Axioms
47(5)
4.2 Digression: Goodman's Paradox and the Problem of Language Relativity
52(5)
4.3 Statistical Principal Principle and Narrowest Reference Classes
57(4)
4.4 Statistical Principal Principle and Exchangeability as Weak Induction Axioms
61(7)
4.5 Indifference Principle as an Induction Axiom
68(4)
4.6 Inductive Probabilities without the Principle of Indifference?
72(3)
4.7 Is Skepticism Unavoidable?
75(2)
5 A New Start: Meta-Induction, Optimality Justifications, and Prediction Games
77(32)
5.1 Reichenbach's Best Alternative Approach
77(1)
5.2 Reliability Justifications versus Optimality Justifications
78(3)
5.3 Shortcomings of Reichenbach's Best Alternative Approach
81(1)
5.4 Object-Induction versus Meta-Induction
82(3)
5.5 Prediction Games
85(5)
5.6 Classification of Prediction Methods and Game-Theoretic Reflections
90(4)
5.7 Definitions of Optimality, Access-Optimality, and (Access-) Dominance
94(5)
5.8 Three Related Approaches: Formal Learning Theory, Computational Learning Theory, and Ecological Rationality Research
99(3)
5.9 Simple and Refined (Conditionalized) Inductive Methods
102(7)
6 Kinds of Meta-Inductive Strategies and Their Performance
109(54)
6.1 Imitate the Best (ITB): Achievements and Failures
110(12)
6.2 Epsilon-Cautious Imitate the Best (εITB)
122(4)
6.3 Systematic Deception: Fundamental Limitations of One-Favorite Meta-lnduction
126(5)
6.3.1 General Facts about Nonconverging Frequencies
126(1)
6.3.2 Nonconvergent Success Oscillations and Systematic Deceivers
127(2)
6.3.3 Limitations of One-Favorite Meta-Induction
129(2)
6.4 Deception Detection and Avoidance Meta-Induction (ITBN)
131(4)
6.5 Further Variations of One-Favorite Meta-Induction
135(3)
6.6 Attractivity-Weighted Meta-Induction (AW) for Real-Valued Predictions
138(9)
6.6.1 Simple AW
140(4)
6.6.2 Exponential AW
144(1)
6.6.3 Access-Superoptimality
145(2)
6.7 Attractivity-Weighted Meta-Induction for Discrete Predictions
147(9)
6.7.1 Randomized AW Meta-Induction
149(4)
6.7.2 Collective AW Meta-Induction
153(3)
6.8 Further Variants of Weighted Meta-Induction
156(7)
6.8.1 Success-Based Weighting
157(4)
6.8.2 Worst-Case Regrets and Division of Epistemic Labor
161(2)
7 Generalizations and Extensions
163(34)
7.1 Bayesian Predictors and Meta-Inductive Probability Aggregation
163(6)
7.2 Intermittent Prediction Games
169(11)
7.2.1 Take the Best (TTB)
172(5)
7.2.2 Intermittent AW
177(3)
7.3 Unboundedly Growing Numbers of Players
180(6)
7.3.1 New Players with Self-Completed Success Evaluation
181(2)
7.3.2 Meta-Induction over Player Sequences
183(3)
7.4 Prediction of Test Sets
186(2)
7.5 Generalization to Action Games
188(3)
7.6 Adding Cognitive Costs
191(3)
7.7 Meta-Induction in Games with Restricted Information
194(3)
8 Philosophical Conclusions and Refinements
197(36)
8.1 A Noncircular Solution to Hume's Problem
197(18)
8.1.1 Epistemological Explication of the Optimality Argument
197(6)
8.1.2 Radical Openness and Universal Learning Ability
203(1)
8.1.3 Meta-Induction and Fundamental Disagreement
204(2)
8.1.4 Fundamentalistic Strategies and the Freedom to Learn
206(2)
8.1.5 A Posteriori Justification of Object-Induction
208(2)
8.1.6 Bayesian Interpretation of the Optimality Argument
210(2)
8.1.7 From Optimal Predictions to Rational (Degrees of) Belief
212(3)
8.2 Conditionalized Meta-Induction
215(7)
8.3 From Optimality to Dominance
222(11)
8.3.1 Restricted Dominance Results
222(2)
8.3.2 Discriminating between Inductive and Noninductive Prediction Methods
224(4)
8.3.3 Bayesian Interpretation of Dominance
228(5)
9 Defense against Objections
233(40)
9.1 Meta-Induction and the No Free Lunch Theorem
233(27)
9.1.1 The Long-Run Perspective
235(10)
9.1.2 The Short-Run Perspective
245(15)
9.2 The Problem of Infinitely Many Prediction Methods
260(13)
9.2.1 Infinitely Many Methods and Failure of Access-Optimality
260(2)
9.2.2 Restricted Optimality Results for Infinitely Many Methods
262(4)
9.2.3 Defense of the Cognitive Finiteness Assumption
266(2)
9.2.4 The Problem of Selecting the Candidate Set
268(2)
9.2.5 Goodman's Problem at the Level of Prediction Methods
270(3)
10 Interdisciplinary Applications
273(32)
10.1 Meta-Induction and Ecological Rationality: Application to Cognitive Science
273(11)
10.2 Meta-Induction and Spread of Knowledge: Application to Social Epistemology
284(13)
10.2.1 Prediction Games in Epistemic Networks
287(2)
10.2.2 Local Meta-Induction and Spread of Reliable Information
289(4)
10.2.3 Imitation without Success Information: Consensus Formation without Spread of Knowledge
293(2)
10.2.4 Conclusion
295(2)
10.3 Meta-Induction, Cooperation, and Game Theory: Application to Cultural Evolution
297(8)
11 Conclusion and Outlook: Optimality Justifications as a Philosophical Program
305(10)
11.1 Optimality Justifications as a Means of Stopping the Justificational Regress
305(2)
11.2 Generalizing Optimality Justifications
307(7)
11.2.1 The Problem of the Basis: Introspective Beliefs
307(1)
11.2.2 The Choice of the Logic
307(3)
11.2.3 The Choice of a Conceptual System
310(1)
11.2.4 The Choice of a Theory
310(1)
11.2.5 The Justification of Abductive Inference
311(3)
11.3 New Foundations for Foundation-Oriented Epistemology
314(1)
12 Appendix: Proof of Formal Results
315(32)
Formal Symbols and Abbreviations 347(4)
Memos, Definitions, Propositions, Theorems, Figures, and Tables 351(4)
References 355(16)
Subject Index 371(12)
Author Index 383