The Local Information Dynamics of Distributed Computation in Complex Systems [electronic resource] / by Joseph T. Lizier.
By: Lizier, Joseph T [author.].
Contributor(s): SpringerLink (Online service).
Material type: BookSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XXIV, 236 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642329524.Subject(s): Engineering | Coding theory | Artificial intelligence | Bioinformatics | Complexity, Computational | Engineering | Complexity | Coding and Information Theory | Artificial Intelligence (incl. Robotics) | Computational Biology/BioinformaticsAdditional physical formats: Printed edition:: No titleDDC classification: 620 Online resources: Click here to access onlineIntroduction -- Computation in complex systems -- Information storage -- Information transfer -- Information modifications -- Information dynamics in networks and phase transitions -- Coherent information structure in complex computation -- Information transfer in biological and bio-inspired systems -- Conclusion.
The nature of distributed computation in complex systems has often been described in terms of memory, communication and processing. This thesis presents a complete information-theoretic framework to quantify these operations on information (i.e. information storage, transfer and modification), and in particular their dynamics in space and time. The framework is applied to cellular automata, and delivers important insights into the fundamental nature of distributed computation and the dynamics of complex systems (e.g. that gliders are dominant information transfer agents). Applications to several important network models, including random Boolean networks, suggest that the capability for information storage and coherent transfer are maximized near the critical regime in certain order-chaos phase transitions. Further applications to study and design information structure in the contexts of computational neuroscience and guided self-organization underline the practical utility of the techniques presented here. .
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