Algorithmic Information Theory
14 Jul 2022 14:33
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 \( K_U(x) \). 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 \( K_U(x) \) can't be much bigger than $|x|$, the length of $x$.
Sometimes, clearly, \( K_U(x) \) 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)
plus $\log{|x|}$ (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 \( K_U(V) \). Hence
\[
K_U(x) \leq K_V(x) + K_U(V),
\]
and algorithmic information content differs by at most an
additive, string-independent constant across computers.
It turns out that the quantity \( K_U(x) \) 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 \( K_U(x) \approx |x| \). 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!
But it would be a good measure of (effective) stochasticity, if we could
actually calculate it.
Unfortunately, we cannot calculate it. There is a lovely little proof
of this, due to Nohre, which I learned of from a paper by Rissanen,
and can't resist rehearsing. Suppose there was a program $P$ which
could read in an object $x$ and return its algorithmic information
content relative to some universe computer, so $P(X) = K_U(x)$.
We now use $P$ to construct a new program $Q$ which compresses incompressible
objects.
- Sort all sequences by length, and then alphabetically.
- For the \( i^{\mathrm{th}} \) sequence $x^i$, use $P$ to find \( K_U(x^i) \).
- If \( K_U(x^i) \leq |V| \), then keep going.
- Otherwise, set $z$ to $x^i$, return $z$, and stop.
$V$ outputs $z$ and stops, so \( K_U(z) \leq |V|\), but, by construction, \(
K_U(z) > |V| \), a contradiction. The only way out of this contradiction
is to deny the premise that $P$ exists: there is no algorithm which calculates
the Kolmogorov complexity of an arbitrary string. You can in
fact strengthen this to say that there is no algorithm which approximates \( K_U \). (In particular,
you can't approximate it with gzip.)
See also:
Complexity Measures;
Ergodic Theory;
Information Theory;
the Minimum Description Length Principle;
Probability;
"Occam"-style Bounds for Long Programs
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.]
- Tom F. Sterkenburg, "Solomonoff Prediction and Occam's Razor",
Philosophy of Science 83 (2016): 459--479, phil-sci/12429
- 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
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
- George Barmpalias and Andrew Lewis-Pye, "Compression of Data Streams Down to Their Information Content", IEEE Transactions on Information Theory 65 (2019): 4471--4485
- 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
- Łukasz Dębowski, "Is Natural Language a Perigraphic Process? The Theorem about Facts and Words Revisited", Entropy 20 (2018): 85
- 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
- Sven Neth, "A Dilemma for Solomonoff Prediction", arxiv:2206.06473
- Andrew Nies, Computability and Randomness
- 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