3,127 research outputs found
Depth, Highness and DNR degrees
We study Bennett deep sequences in the context of recursion theory; in
particular we investigate the notions of O(1)-deepK, O(1)-deepC , order-deep K
and order-deep C sequences. Our main results are that Martin-Loef random sets
are not order-deepC , that every many-one degree contains a set which is not
O(1)-deepC , that O(1)-deepC sets and order-deepK sets have high or DNR Turing
degree and that no K-trival set is O(1)-deepK.Comment: journal version, dmtc
The "paradox" of computability and a recursive relative version of the Busy Beaver function
In this article, we will show that uncomputability is a relative property not
only of oracle Turing machines, but also of subrecursive classes. We will
define the concept of a Turing submachine, and a recursive relative version for
the Busy Beaver function which we will call Busy Beaver Plus function.
Therefore, we will prove that the computable Busy Beaver Plus function defined
on any Turing submachine is not computable by any program running on this
submachine. We will thereby demonstrate the existence of a "paradox" of
computability a la Skolem: a function is computable when "seen from the
outside" the subsystem, but uncomputable when "seen from within" the same
subsystem. Finally, we will raise the possibility of defining universal
submachines, and a hierarchy of negative Turing degrees.Comment: 10 pages. 0 figures. Supported by the National Council for Scientific
and Technological Development (CNPq), Brazil. Book chapter published in
Information and Complexity, Mark Burgin and Cristian S. Calude (Editors),
World Scientific Publishing, 2016, ISBN 978-981-3109-02-5, available at
http://www.worldscientific.com/worldscibooks/10.1142/10017. arXiv admin note:
substantial text overlap with arXiv:1612.0522
Empirical Encounters with Computational Irreducibility and Unpredictability
There are several forms of irreducibility in computing systems, ranging from
undecidability to intractability to nonlinearity. This paper is an exploration
of the conceptual issues that have arisen in the course of investigating
speed-up and slowdown phenomena in small Turing machines. We present the
results of a test that may spur experimental approaches to the notion of
computational irreducibility. The test involves a systematic attempt to outrun
the computation of a large number of small Turing machines (all 3 and 4 state,
2 symbol) by means of integer sequence prediction using a specialized function
finder program. This massive experiment prompts an investigation into rates of
convergence of decision procedures and the decidability of sets in addition to
a discussion of the (un)predictability of deterministic computing systems in
practice. We think this investigation constitutes a novel approach to the
discussion of an epistemological question in the context of a computer
simulation, and thus represents an interesting exploration at the boundary
between philosophical concerns and computational experiments.Comment: 18 pages, 4 figure
Kolmogorov's Structure Functions and Model Selection
In 1974 Kolmogorov proposed a non-probabilistic approach to statistics and
model selection. Let data be finite binary strings and models be finite sets of
binary strings. Consider model classes consisting of models of given maximal
(Kolmogorov) complexity. The ``structure function'' of the given data expresses
the relation between the complexity level constraint on a model class and the
least log-cardinality of a model in the class containing the data. We show that
the structure function determines all stochastic properties of the data: for
every constrained model class it determines the individual best-fitting model
in the class irrespective of whether the ``true'' model is in the model class
considered or not. In this setting, this happens {\em with certainty}, rather
than with high probability as is in the classical case. We precisely quantify
the goodness-of-fit of an individual model with respect to individual data. We
show that--within the obvious constraints--every graph is realized by the
structure function of some data. We determine the (un)computability properties
of the various functions contemplated and of the ``algorithmic minimal
sufficient statistic.''Comment: 25 pages LaTeX, 5 figures. In part in Proc 47th IEEE FOCS; this final
version (more explanations, cosmetic modifications) to appear in IEEE Trans
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