444 research outputs found
A framework for the local information dynamics of distributed computation in complex systems
The nature of distributed computation has often been described in terms of
the component operations of universal computation: information storage,
transfer and modification. We review the first complete framework that
quantifies each of these individual information dynamics on a local scale
within a system, and describes the manner in which they interact to create
non-trivial computation where "the whole is greater than the sum of the parts".
We describe the application of the framework to cellular automata, a simple yet
powerful model of distributed computation. This is an important application,
because the framework is the first to provide quantitative evidence for several
important conjectures about distributed computation in cellular automata: that
blinkers embody information storage, particles are information transfer agents,
and particle collisions are information modification events. The framework is
also shown to contrast the computations conducted by several well-known
cellular automata, highlighting the importance of information coherence in
complex computation. The results reviewed here provide important quantitative
insights into the fundamental nature of distributed computation and the
dynamics of complex systems, as well as impetus for the framework to be applied
to the analysis and design of other systems.Comment: 44 pages, 8 figure
Local information transfer as a spatiotemporal filter for complex systems
We present a measure of local information transfer, derived from an existing
averaged information-theoretical measure, namely transfer entropy. Local
transfer entropy is used to produce profiles of the information transfer into
each spatiotemporal point in a complex system. These spatiotemporal profiles
are useful not only as an analytical tool, but also allow explicit
investigation of different parameter settings and forms of the transfer entropy
metric itself. As an example, local transfer entropy is applied to cellular
automata, where it is demonstrated to be a novel method of filtering for
coherent structure. More importantly, local transfer entropy provides the first
quantitative evidence for the long-held conjecture that the emergent traveling
coherent structures known as particles (both gliders and domain walls, which
have analogues in many physical processes) are the dominant information
transfer agents in cellular automata.Comment: 12 page
Predictability: a way to characterize Complexity
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy,
Shannon entropy and algorithmic complexity is discussed. In particular, we
emphasize how a characterization of the unpredictability of a system gives a
measure of its complexity. Adopting this point of view, we review some
developments in the characterization of the predictability of systems showing
different kind of complexity: from low-dimensional systems to high-dimensional
ones with spatio-temporal chaos and to fully developed turbulence. A special
attention is devoted to finite-time and finite-resolution effects on
predictability, which can be accounted with suitable generalization of the
standard indicators. The problems involved in systems with intrinsic randomness
is discussed, with emphasis on the important problems of distinguishing chaos
from noise and of modeling the system. The characterization of irregular
behavior in systems with discrete phase space is also considered.Comment: 142 Latex pgs. 41 included eps figures, submitted to Physics Reports.
Related information at this http://axtnt2.phys.uniroma1.i
Expressing the entropy of lattice systems as sums of conditional entropies
Whether a system is to be considered complex or not depends on how one
searches for correlations. We propose a general scheme for calculation of
entropies in lattice systems that has high flexibility in how correlations are
successively taken into account. Compared to the traditional approach for
estimating the entropy density, in which successive approximations builds on
step-wise extensions of blocks of symbols, we show that one can take larger
steps when collecting the statistics necessary to calculate the entropy density
of the system. In one dimension this means that, instead of a single sweep over
the system in which states are read sequentially, one take several sweeps with
larger steps so that eventually the whole lattice is covered. This means that
the information in correlations is captured in a different way, and in some
situations this will lead to a considerably much faster convergence of the
entropy density estimate as a function of the size of the configurations used
in the estimate. The formalism is exemplified with both an example of a free
energy minimisation scheme for the two-dimensional Ising model, and an example
of increasingly complex spatial correlations generated by the time evolution of
elementary cellular automaton rule 60
Coarse-Grained Probabilistic Automata Mimicking Chaotic Systems
Discretization of phase space usually nullifies chaos in dynamical systems.
We show that if randomness is associated with discretization dynamical chaos
may survive and be indistinguishable from that of the original chaotic system,
when an entropic, coarse-grained analysis is performed. Relevance of this
phenomenon to the problem of quantum chaos is discussed.Comment: 4 pages, 4 figure
Some properties of cellular automata with equicontinuity points
We investigate topological and ergodic properties of cellular automata having
equicontinuity points. In this class surjectivity on a transitive SFT implies
existence of a dense set of periodic points. Our main result is that under the
action of such an automaton any shift ergodic measure converges in Cesaro Mean
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