arXiv subjects
Publications and source records attributed to G. J. Chaitin.
Presents a history of the evolution of the author's ideas on program-size complexity and its applications to metamathematics over the course of more than four decades. Includes suggestions for further work.
Some Goedel centenary reflections on whether incompleteness is really serious, and whether mathematics should be done somewhat differently, based on using algorithmic complexity measured in bits of information. [Enriques lecture given Monday, October 30, 2006, at the University of Milan.]
In 1686 in his Discours de Metaphysique, Leibniz points out that if an arbitrarily complex theory is permitted then the notion of "theory" becomes vacuous because there is always a theory. This idea is developed in the modern theory of algorithmic information, which deals with the size of computer programs and provides a new view of Godel's work on incompleteness and Turing's work on uncomputability. Of particular interest is the halting probability Omega, whose bits are irreducible, i.e., maximally unknowable mathematical facts. More generally, these ideas constitute a kind of "digital philosophy" related to recent attempts of Edward Fredkin, Stephen Wolfram and others to view the world as a giant computer. There are also connections with recent "digital physics" speculations that the universe might actually be discrete, not continuous. This systeme du monde is presented as a coherent whole in my book Meta Math!, which will be published this fall.
We discuss mathematical and physical arguments against continuity and in favor of discreteness, with particular emphasis on the ideas of Emile Borel (1871-1956).
By using ideas on complexity and randomness originally suggested by the mathematician-philosopher Gottfried Leibniz in 1686, the modern theory of algorithmic information is able to show that there can never be a "theory of everything" for all of mathematics.
This book presents a personal account of the mathematics and metamathematics of the 20th century leading up to the discovery of the halting probability Omega. The emphasis is on history of ideas and philosophical implications.
This paper, which is dedicated to Alan Turing on the 50th anniversary of his death, gives an overview and discusses the philosophical implications of incompleteness, uncomputability and randomness.
The simultaneous appearance in May 2003 of four books on the Riemann hypothesis (RH) provoked these reflections. We briefly discuss whether the RH should be added as a new axiom, or whether a proof of the RH might involve the notion of randomness.
The information-theoretic point of view proposed by Leibniz in 1686 and developed by algorithmic information theory (AIT) suggests that mathematics and physics are not that different. This will be a first-person account of some doubts and speculations about the nature of mathematics that I have entertained for the past three decades, and which have now been incorporated in a digital philosophy paradigm shift that is sweeping across the sciences.
Most work on computational complexity is concerned with time. However this course will try to show that program-size complexity, which measures algorithmic information, is of much greater philosophical significance. I'll discuss how one can use this complexity measure to study what can and cannot be achieved by formal axiomatic mathematical theories. In particular, I'll show (a) that there are natural information-theoretic constraints on formal axiomatic theories, and that program-size complexity provides an alternative path to incompleteness from the one originally used by Kurt Godel. Furthermore, I'll show (b) that in pure mathematics there are mathematical facts that are true for no reason, that are true by accident. These have to do with determining the successive binary digits of the precise numerical value of the halting probability Omega for a "self-delimiting" universal Turing machine. I believe that these meta-theorems (a,b) showing (a) that the complexity of axiomatic theories can be characterized information-theoretically and (b) that God plays dice in pure mathematics, both strongly suggest a quasi-empirical view of mathematics. I.e., math is different from physics, but perhaps not as different as people usually think. I'll also discuss the convergence of theoretical computer science with theoretical physics, Leibniz's ideas on complexity, Stephen Wolfram's book A New Kind of Science, and how to attempt to use information theory to define what a living being is.
Two philosophical applications of the concept of program-size complexity are discussed. First, we consider the light program-size complexity sheds on whether mathematics is invented or discovered, i.e., is empirical or is a priori. Second, we propose that the notion of algorithmic independence sheds light on the question of being and how the world of our experience can be partitioned into separate entities.
We discuss views about whether the universe can be rationally comprehended, starting with Plato, then Leibniz, and then the views of some distinguished scientists of the previous century. Based on this, we defend the thesis that comprehension is compression, i.e., explaining many facts using few theoretical assumptions, and that a theory may be viewed as a computer program for calculating observations. This provides motivation for defining the complexity of something to be the size of the simplest theory for it, in other words, the size of the smallest program for calculating it. This is the central idea of algorithmic information theory (AIT), a field of theoretical computer science. Using the mathematical concept of program-size complexity, we exhibit irreducible mathematical facts, mathematical facts that cannot be demonstrated using any mathematical theory simpler than they are. It follows that the world of mathematical ideas has infinite complexity and is therefore not fully comprehensible, at least not in a static fashion. Whether the physical world has finite or infinite complexity remains to be seen. Current science believes that the world contains randomness, and is therefore also infinitely complex, but a deterministic universe that simulates randomness via pseudo-randomness is also a possibility, at least according to recent highly speculative work of S. Wolfram.
This article discusses what can be proved about the foundations of mathematics using the notions of algorithm and information. The first part is retrospective, and presents a beautiful antique, Godel's proof, the first modern incompleteness theorem, Turing's halting problem, and a piece of postmodern metamathematics, the halting probability Omega. The second part looks forward to the new century and discusses the convergence of theoretical physics and theoretical computer science and hopes for a theoretical biology, in which the notions of algorithm and information are again crucial.
I'll discuss how Goedel's paradox "This statement is false/unprovable" yields his famous result on the limits of axiomatic reasoning. I'll contrast that with my work, which is based on the paradox of "The first uninteresting positive whole number", which is itself a rather interesting number, since it is precisely the first uninteresting number. This leads to my first result on the limits of axiomatic reasoning, namely that most numbers are uninteresting or random, but we can never be sure, we can never prove it, in individual cases. And these ideas culminate in my discovery that some mathematical facts are true for no reason, they are true by accident, or at random. In other words, God not only plays dice in physics, but even in pure mathematics, in logic, in the world of pure reason. Sometimes mathematical truth is completely random and has no structure or pattern that we will ever be able to understand. It is NOT the case that simple clear questions have simple clear answers, not even in the world of pure ideas, and much less so in the messy real world of everyday life.
Transcript of G.J. Chaitin's 2 March 2000 Carnegie Mellon University School of Computer Science Distinguished Lecture. The notion of randomness is taken from physics and applied to pure mathematics in order to shed light on the incompleteness phenomenon discovered by K. Godel.
This is the transcript of a lecture given at UMass-Lowell in which I compare and contrast the work of Godel and of Turing and my own work on incompleteness. I also discuss randomness in physics vs randomness in pure mathematics.
This book is the final version of a course on algorithmic information theory and the epistemology of mathematics and physics. This is camera-ready copy prepared for publication as a book, but at the last minute I decided to publish it electronically instead. This book discusses Einstein and Godel's views on the nature of mathematics in the light of information theory, and sustains the thesis that mathematics is quasi-empirical. There is a foreword by Cris Calude of the University of Auckland, and a remark on the back cover by John Casti of the Santa Fe Institute. Supplementary material is available at the author's web site -- The frontispiece photograph is at http://www.cs.auckland.ac.nz/CDMTCS/chaitin/index.html, and the software not included in the book is at http://www.cs.auckland.ac.nz/CDMTCS/chaitin/rov.html
I'll outline the latest version of my limits of math course. The purpose of this course is to illustrate the proofs of the key information-theoretic incompleteness theorems of algorithmic information theory by means of algorithms written in a specially designed version of LISP. The course is now written in HTML with Java applets, and is available at http://www.research.ibm.com/people/c/chaitin/lm . The LISP now used is much friendlier than before, and because its interpreter is a Java applet it will run in the Netscape browser as you browse my limits of math Web site.