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Entropy 2017, 19(2), 73;

Identifying Critical States through the Relevance Index

Department of Computer Science and Engineering, Alma Mater Studiorum Università di Bologna, Campus of Cesena, Cesena I-47521, Italy
Department of Physics, Informatics and Mathematics, Università di Modena e Reggio Emilia, Modena I-41125, Italy
European Centre for Living Technology, Venezia I-30124, Italy
Author to whom correspondence should be addressed.
Academic Editor: Mikhail Prokopenko
Received: 7 January 2017 / Revised: 11 February 2017 / Accepted: 13 February 2017 / Published: 16 February 2017
(This article belongs to the Special Issue Complexity, Criticality and Computation (C³))
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The identification of critical states is a major task in complex systems, and the availability of measures to detect such conditions is of utmost importance. In general, criticality refers to the existence of two qualitatively different behaviors that the same system can exhibit, depending on the values of some parameters. In this paper, we show that the relevance index may be effectively used to identify critical states in complex systems. The relevance index was originally developed to identify relevant sets of variables in dynamical systems, but in this paper, we show that it is also able to capture features of criticality. The index is applied to two prominent examples showing slightly different meanings of criticality, namely the Ising model and random Boolean networks. Results show that this index is maximized at critical states and is robust with respect to system size and sampling effort. It can therefore be used to detect criticality. View Full-Text
Keywords: critical states; relevance index; Ising model; random Boolean networks; complex systems critical states; relevance index; Ising model; random Boolean networks; complex systems

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Roli, A.; Villani, M.; Caprari, R.; Serra, R. Identifying Critical States through the Relevance Index. Entropy 2017, 19, 73.

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