## Algorithmic Information Theory

*10 Jan 2016 21:35*

Fix your favorite universal computer $U$. Now consider any string of symbols $x$, or anything which we can describe by means of a string of symbols. Among all the programs which can be run on $U$, there will be some which will produce $x$ and then stop. There will therefore be a minimum length for such programs. Call this length (x)$. This is the "algorithmic information content" of $x$ (relative to $U$), or its Kolmgorov (-Chaitin-Solomonoff) complexity.

Note that one program which could produce $x$ would
be `print(`$x$); stop; (or its equivalent in the language of
$U$), so (x)$ can't be much bigger than $|x|$, the length of $x$.
Sometimes, clearly, (x)$ can be much smaller than $x$: if $x$ just consists
of the same symbol repeated over and over, say 0, we could use the
program `for (i in 1:n) { print("0"); }; stop;` and give
it `n=`$|x|$, for a total length of a constant (the loop-and-print bit)
and $\log{|x|}$ (the number of symbols needed to write the length of $x$).
While precise values will, irritatingly, be relative to the universal computer
$U$, because the computer is universal, it can emulate any other computer $V$
with a finite-length program which we might call (V)$. Hence (x) \leq
K_V(x) + K_U(V)$, and algorithmic information content differs by at most an
additive, string-independent constant across computers.

It turns out that the quantity (x)$ shares many formal properties with the (Shannon) entropy of a random variable, typically written $H[X]$, so that one can define analogs of all the usual information-theoretic quantities in purely algorithmic terms. Along these lines, we say that $x$ is "incompressible" if (x) \approx |x|$. What makes this mathematically interesting is that incompressible sequences turn out to provide a model of sequences of independent and identically distributed random variables, and, vice versa, the typical sample path of an IID stochastic process is incompressible. This generalizes: almost every trajectory of an ergodic stochastic process has a Kolmogorov complexity whose growth rate equals its entropy rate (Brudno's theorem). This, by the way, is why I don't think Kolmogorov complexity makes a very useful complexity measure: it's maximal for totally random things!

Unfortunately, algorithmic information content is also incomputable, and
doesn't even have computable approximations. (In particular,
you can't approximate it with `gzip`.)

See also: Complexity Measures; Ergodic Theory; Information Theory; the Minimum Description Length Principle; Probability

- Recommended, big picture:
- Cover and Thomas, Elements of Information Theory [specifically the chapter on Kolmogorov complexity]
- Ming Li and Paul M. B. Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications

- Recommended, close-ups:
- Peter Gacs, John T. Tromp and Paul M. B. Vitanyi, "Algorithmic Statistics", IEEE Transactions on Information Theory
**47**(2001): 2443--463, arxiv:math.PR/0006233 - Stefano Galatolo, Mathieu Hoyrup, and Cristóbal Rojas,
"Effective symbolic dynamics, random points, statistical behavior, complexity
and entropy", arxiv:0801.0209
[
*All*, not almost all, Martin-Lof points are statistically typical.] - Jan Lemeire, Dominik Janzing, "Replacing Causal Faithfulness with Algorithmic Independence of Conditionals", Minds and Machines
**23**(2013): 227--249 - G. W. Müller, "Randomness and extrapolation", Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 2 (Univ. of Calif. Press, 1972), 1--31 [On a notion of randomness supposedly related to, but stronger than, that of Martin-Löf.]
- Bastian Steudel, Nihat Ay, "Information-theoretic inference of common ancestors", arxiv:1010.5720
- Paul M. B. Vitanyi and Ming Li, "Minimum Description Length
Induction, Bayesianism, and Kolmogorov Complexity", IEEE Transactions on
Information Theory
**46**(2000): 446--464, cs.LG/9901014 - Vladimir Vovk, "Superefficiency from the Vantage Point of Computability", Statistical Science
**24**(2009): 73--86

- Modesty forbids me to recommend:
- The notes and slides from lecture 8 in my "Chaos, Complexity and Inference" class

- To read:
- Luis Antunes, Bruno Bauwens, Andre Souto, Andreia Teixeira, "Sophistication vs Logical Depth", arxiv:1304.8046
- John C. Baez, Mike Stay, "Algorithmic Thermodynamics", arxiv:1010.2067
- Fabio Benatti, Tyll Krueger, Markus Mueller, Rainer Siegmund-Schultze and Arleta Szkola, "Entropy and Algorithmic Complexity in Quantum Information Theory: a Quantum Brudno's Theorem", quant-ph/0506080
- Laurent Bienvenu, Adam Day, Mathieu Hoyrup, Ilya Mezhirov, Alexander Shen, "A constructive version of Birkhoff's ergodic theorem for Martin-Löf random points", arxiv:1007.5249
- Laurent Bienvenu, Peter Gacs, Mathieu Hoyrup, Cristobal Rojas, Alexander Shen, "Algorithmic tests and randomness with respect to a class of measures", arxiv:1103.1529
- Laurent Bienvenu, Rod Downey, "Kolmogorov Complexity and Solovay Functions", arxiv:0902.1041
- Laurent Bienvenu, Alexander Shen, "Algorithmic information theory and martingales", arxiv:0906.2614
- Claudio Bonanno, "The Manneville map: topological, metric and algorithmic entropy," math.DS/0107195
- Claudio Bonanno and Pierre Collet, "Complexity for Extended Dynamical Systems", Communications in Mathematical Physics
**275**(2007): 721--748, math/0609681 - The Computer Journal,
**42:4**(1999) [Special issue on Kolmogorov complexity and inference] - Boris Darkhovsky, Alexandra Pyriatinska, "Epsilon-complexity of continuous functions", arxiv:1303.1777
- David Doty, "Every sequence is compressible to a random one", cs.IT/0511074 ["Kucera and Gacs independently showed that every infinite sequence is Turing reducible to a Martin-Lof random sequence. We extend this result to show that every infinite sequence S is Turing reducible to a Martin-Lof random sequence R such that the asymptotic number of bits of R needed to compute n bits of S, divided by n, is precisely the constructive dimension of S."]
- David Doty and Jared Nichols, "Pushdown Dimension", cs.IT/0504047
- Peter Gács, "Uniform test of algorithmic randomness over a
general space", Theoretical Computer
Science
**341**(2005): 91--137 ["The algorithmic theory of randomness is well developed when the underlying space is the set of finite or infinite sequences and the underlying probability distribution is the uniform distribution or a computable distribution. These restrictions seem artificial. Some progress has been made to extend the theory to arbitrary Bernoulli distributions (by Martin-Lof) and to arbitrary distributions (by Levin). We recall the main ideas and problems of Levin's theory, and report further progress in the same framework...."] - Travis Gagie, "Compressing Probability Distributions", cs.IT/0506016 [
*Abstract*(in full): "We show how to store good approximations of probability distributions in small space."] - Stefano Galatolo, Mathieu Hoyrup, Cristóbal Rojas
- "Dynamical systems, simulation, abstract computation", arxiv:1101.0833
- "A constructive Borel-Cantelli Lemma. Constructing orbits with required statistical properties", arxiv:0711.1478

- Peter Grünwald and Paul Vitányi, "Shannon Information and Kolmogorov Complexity", cs.IT/0410002
- Mrinalkanti Ghosh, Satyadev Nandakumar, Atanu Pal, "Ornstein Isomorphism and Algorithmic Randomness", arxiv:1404.0766
- Michael Hochman, "Upcrossing Inequalities for Stationary Sequences and Applications to Entropy and Complexity", arxiv:math.DS/0608311 [where "complexity" = algorithmic information content]
- Mathieu Hoyrup, Cristobal Rojas, "Computability of probability measures and Martin-Lof randomness over metric spaces", arxiv:0709.0907
- S. Jalalai, A. Maleki and R. G. Baraniuk, "Minimum Complexity Pursuit for Universal Compressed Sensing", IEEE Transactions on Information Theory
**60**(2014): 2253--2268, arxiv:1208.5814 - Takakazu Mori, Yoshiki Tsujii, Mariko Yasugi, "Computability of Probability Distributions and Characteristic Functions", arxiv:1307.6357
- Andrej Muchnik, "Algorithmic randomness and splitting of supermartingales", arxiv:0807.3156
- Markus Mueller, "Stationary Algorithmic Probability", arxiv:cs/0608095
- E. Rivals and J.-P. Delahae, "Optimal Representation in Average
Using Kolmogorov Complexity," Theoretical Computer Science
**200**(1998): 261--287 - Jason Rute, Topics in Algorithmic Randomness and Computable Analysis
- Andrei N. Soklakov, "Complexity Analysis for Algorithmically Simple Strings," cs.LG/0009001
- H. Takashashi, "Redundancy of Universal Coding, Kolmogorov
Complexity, and Hausdorff Dimension", IEEE Transactions on Information
Theory
**50**(2004): 2727--2736 - Nikolai Vereshchagin and Paul Vitanyi, "Kolmogorov's Structure Functions with an Application to the Foundations of Model Selection," cs.CC/0204037
- Paul Vitanyi, "Randomness," math.PR/0110086
- Vladimir V'yugin, "On Instability of the Ergodic Limit Theorems with Respect to Small Violations of Algorithmic Randomness", arxiv:1105.4274
- C. S. Wallace and David L. Dowe, "Minimum Message Length and Kolmogorov Complexity", The Computer Journal
**42**(1999): 270--283