David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Entropy 13 (6):1076-1136 (2011)
Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results and is based on solid philosophical foundations, the requisite technical knowledge necessary for understanding this framework has caused it to remain largely unknown and unappreciated in the wider scientific community. The main contribution of this article is to convey Solomonoff induction and its related concepts in a generally accessible form with the aim of bridging this current technical gap. In the process we examine the major historical contributions that have led to the formulation of Solomonoff Induction as well as criticisms of Solomonoff and induction in general. In particular we examine how Solomonoff induction addresses many issues that have plagued other inductive systems, such as the black ravens paradox and the confirmation problem, and compare this approach with other recent approaches.
|Keywords||sequence prediction inductive inference Bayes rule Solomonoff prior Kolmogorov complexity|
|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
No references found.
Citations of this work BETA
Jan Lemeire & Dominik Janzing (2013). Replacing Causal Faithfulness with Algorithmic Independence of Conditionals. Minds and Machines 23 (2):227-249.
Similar books and articles
John D. Norton (2003). A Material Theory of Induction. Philosophy of Science 70 (4):647-670.
John D. Norton (2010). There Are No Universal Rules for Induction. Philosophy of Science 77 (5):765-777.
F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
Gerhard Schurz, Local, General and Universal Prediction Strategies: A Game-Theoretical Approach to the Problem of Induction.
John D. Norton (2013). A Material Dissolution of the Problem of Induction. Synthese 191 (4):1-20.
Franz Huber, Confirmation and Induction. Internet Encyclopedia of Philosophy.
Louis E. Loeb (2006). Psychology, Epistemology, and Skepticism in Hume's Argument About Induction. Synthese 152 (3):321 - 338.
Massimiliano Badino (2004). An Application of Information Theory to the Problem of the Scientific Experiment. Synthese 140 (3):355 - 389.
John D. Norton (2007). Probability Disassembled. British Journal for the Philosophy of Science 58 (2):141 - 171.
Gerhard Schurz (2009). Meta-Induction and Social Epistemology: Computer Simulations of Prediction Games. Episteme 6 (2):200-220.
John D. Norton, The Inductive Significance of Observationally Indistinguishable Spacetimes: (Peter Achinstein has the Last Laugh).
P. D. Magnus (2008). Demonstrative Induction and the Skeleton of Inference. International Studies in the Philosophy of Science 22 (3):303 – 315.
Cory F. Juhl (1994). The Speed-Optimality of Reichenbach's Straight Rule of Induction. British Journal for the Philosophy of Science 45 (3):857-863.
Colin Howson (1991). The Last Word on Induction? Erkenntnis 34 (1):73 - 82.
Added to index2011-06-07
Total downloads56 ( #33,860 of 1,410,123 )
Recent downloads (6 months)3 ( #75,884 of 1,410,123 )
How can I increase my downloads?