249,649 research outputs found
Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results
We apply methods for estimating the algorithmic complexity of sequences to
behavioural sequences of three landmark studies of animal behavior each of
increasing sophistication, including foraging communication by ants, flight
patterns of fruit flies, and tactical deception and competition strategies in
rodents. In each case, we demonstrate that approximations of Logical Depth and
Kolmogorv-Chaitin complexity capture and validate previously reported results,
in contrast to other measures such as Shannon Entropy, compression or ad hoc.
Our method is practically useful when dealing with short sequences, such as
those often encountered in cognitive-behavioural research. Our analysis
supports and reveals non-random behavior (LD and K complexity) in flies even in
the absence of external stimuli, and confirms the "stochastic" behaviour of
transgenic rats when faced that they cannot defeat by counter prediction. The
method constitutes a formal approach for testing hypotheses about the
mechanisms underlying animal behaviour.Comment: 28 pages, 7 figures and 2 table
Image Characterization and Classification by Physical Complexity
We present a method for estimating the complexity of an image based on
Bennett's concept of logical depth. Bennett identified logical depth as the
appropriate measure of organized complexity, and hence as being better suited
to the evaluation of the complexity of objects in the physical world. Its use
results in a different, and in some sense a finer characterization than is
obtained through the application of the concept of Kolmogorov complexity alone.
We use this measure to classify images by their information content. The method
provides a means for classifying and evaluating the complexity of objects by
way of their visual representations. To the authors' knowledge, the method and
application inspired by the concept of logical depth presented herein are being
proposed and implemented for the first time.Comment: 30 pages, 21 figure
An Algorithmic Approach to Information and Meaning
I will survey some matters of relevance to a philosophical discussion of
information, taking into account developments in algorithmic information theory
(AIT). I will propose that meaning is deep in the sense of Bennett's logical
depth, and that algorithmic probability may provide the stability needed for a
robust algorithmic definition of meaning, one that takes into consideration the
interpretation and the recipient's own knowledge encoded in the story attached
to a message.Comment: preprint reviewed version closer to the version accepted by the
journa
Modeling of Phenomena and Dynamic Logic of Phenomena
Modeling of complex phenomena such as the mind presents tremendous
computational complexity challenges. Modeling field theory (MFT) addresses
these challenges in a non-traditional way. The main idea behind MFT is to match
levels of uncertainty of the model (also, problem or theory) with levels of
uncertainty of the evaluation criterion used to identify that model. When a
model becomes more certain, then the evaluation criterion is adjusted
dynamically to match that change to the model. This process is called the
Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes
of the mind and natural evolution. This paper provides a formal description of
DLP by specifying its syntax, semantics, and reasoning system. We also outline
links between DLP and other logical approaches. Computational complexity issues
that motivate this work are presented using an example of polynomial models
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