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dc.contributor.authorVenkatasubramanian, V.en
dc.contributor.authorPolitis, Dimitris Nicolasen
dc.contributor.authorPatkar, P. R.en
dc.creatorVenkatasubramanian, V.en
dc.creatorPolitis, Dimitris Nicolasen
dc.creatorPatkar, P. R.en
dc.date.accessioned2019-12-02T10:38:44Z
dc.date.available2019-12-02T10:38:44Z
dc.date.issued2006
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/57745
dc.description.abstractA general holistic theory is presented for the organization of complex networks. The theory proposes that complex networks, both human-engineered and naturally evolved, are organized to meet certain design or survival objective(s) for a wide variety of operating or environmental conditions. Using the concepts of "value" of interactions and "satisfaction" in a network as generic performance measures, we show that the underlying organizing principle is to meet an overall performance target for a wide variety of operating environments as the design objective. This design requirement for reliable performance under maximum uncertainty leads to the emergence of power laws as a consequence of the Maximum Entropy Principle. The theory also predicts the emergence of exponential and Poisson distribution regimes as a function of the redundancy of the network, thus explaining all three regimes as different manifestations of the same underlying phenomenon within a unified theoretical framework. © 2005 American Institute of Chemical Engineers.en
dc.sourceAIChE Journalen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33645577759&doi=10.1002%2faic.10693&partnerID=40&md5=b90210dbf9e1fa717e317e774916afcc
dc.subjectPoisson distributionen
dc.subjectUncertain systemsen
dc.subjectEntropyen
dc.subjectComplex networksen
dc.subjectLarge scale systemsen
dc.subjectPower lawen
dc.subjectComplex systemsen
dc.subjectOptimal systemsen
dc.subjectHuman engineeringen
dc.subjectDegree distributionen
dc.subjectLogic designen
dc.subjectMaximum Entropy Principleen
dc.titleEntropy maximization as a holistic design principle for complex optimal networksen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1002/aic.10693
dc.description.volume52
dc.description.issue3
dc.description.startingpage1004
dc.description.endingpage1009
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :8</p>en
dc.source.abbreviationAIChE J.en


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