One of the challenges of defining emergence is that one observer's prior knowledge may cause a phenomenon to present itself as emergent that to another observer appears reducible. By formalizing the act of observi...
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One of the challenges of defining emergence is that one observer's prior knowledge may cause a phenomenon to present itself as emergent that to another observer appears reducible. By formalizing the act of observing as mutual perturbations between dynamical systems, we demonstrate that the emergence of algorithmicinformation does depend on the observer's formal knowledge, while being robust vis-a-vis other subjective factors, particularly: the choice of programming language and method of measurement;errors or distortions during the observation;and the informational cost of processing. This is called observer-dependent emergence (ODE). In addition, we demonstrate that the unbounded and rapid increase of emergent algorithmicinformation implies asymptotically observer-independent emergence (AOIE). Unlike ODE, AOIE is a type of emergence for which emergent phenomena will be considered emergent no matter what formal theory an observer might bring to bear. We demonstrate the existence of an evolutionary model that displays the diachronic variant of AOIE and a network model that displays the holistic variant of AOIE. Our results show that, restricted to the context of finite discrete deterministic dynamical systems, computable systems and irreducible information content measures, AOIE is the strongest form of emergence that formal theories can *** article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
Electroencephalography (EEG) as an example of electrophysiological monitoring methods has a rather long history of successful application for the diagnosis and treatment of diseases, and this success would not have be...
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Electroencephalography (EEG) as an example of electrophysiological monitoring methods has a rather long history of successful application for the diagnosis and treatment of diseases, and this success would not have been possible without effective methods of mathematical, and more recently, computer analysis. Most of these methods are based on statistics. Among the methods of EEG analysis, there is a group of meth-ods that use different versions of Shannon's entropy estimation as a "main component" and that do not differ significantly from traditional statistical approaches. Despite the external similarity, another approach is to use the Kolmogorov-Chaitin definition of complexity and the con-cepts of algorithmic information dynamics. The algorithmicdynamics toolbox includes techniques (e.g., block decomposition method) that appear to be applicable to EEG analysis. The current paper is an attempt to use the block decomposition method along with the recent addition to the management of EEG data provided by machine learn-ing, with the ultimate goal of making this data more useful to researchers and medical practitioners.
It is known that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. Applicability of these concepts to objects c...
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It is known that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. Applicability of these concepts to objects closer to the "clinical end" is less studied. A possible approach consists of encoding the state of a human body system represented by its system dynamics model using matrices and monitoring the behavior of the system through analysis of these matrices, with a potential extrapolation of the results to the clinical setting. This paper presents an attempt at using some concepts and tools, specifically the block decomposition method (BDM), coming from the new emerging field of algorithmic information dynamics, for the management of a patient, especially in the intensive care unit (ICU). It describes some aspects pertaining to the "pre-clinical" incipient stage and tries to outline eventual future clinical application.
Large arithmetic expressions are dissipative: they lose information and are robust to perturbations. Lack of conservation gives resilience to fluctuations. The limited precision of floating point and the mixture of li...
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Large arithmetic expressions are dissipative: they lose information and are robust to perturbations. Lack of conservation gives resilience to fluctuations. The limited precision of floating point and the mixture of linear and nonlinear operations make such functions anti-fragile and give a largely stable locally flat plateau a rich fitness landscape. This slows long-term evolution of complex programs, suggesting a need for depthaware crossover and mutation operators in tree-based genetic programming. It also suggests that deeply nested computer program source code is error tolerant because disruptions tend to fail to propagate, and therefore the optimal placement of test oracles is as close to software defects as practical.
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