Identifying Critical States through the Relevance Index

Roli, Andrea; Villani, Marco; Caprari, Riccardo; Serra, Roberto

doi:10.3390/e19020073

Cited by 15 publications

(13 citation statements)

References 21 publications

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“…In our previous works, we extended the approach to non-stationary dynamical regimes, in order to apply the method to a broad range of systems, including abstract models of gene regulatory networks and simulated social [10], chemical [32], and biological [33] systems. The resulting approach could also be used to identify the critical states of complex dynamical systems [24].…”

Section: Context and Related Workmentioning

confidence: 99%

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A Relevance Index Method to Infer Global Properties of Biological Networks

Villani

Sani

Amoretti

et al. 2018

Communications in Computer and Information Science

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Section: Context and Related Workmentioning

confidence: 99%

“…Thus, its presence is useless for the purposes of a dynamical analysis and no CRS include it. Indeed, it is active in transient states, but this kind of analysis is out of the scope of this work (see[24] for a first comparison of the results of RI application to transients and asymptotic states).…”

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confidence: 99%

A Relevance Index Method to Infer Global Properties of Biological Networks

Villani

Sani

Amoretti

et al. 2018

Communications in Computer and Information Science

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“…The RI makes it possible to identify, as components of a system, relevant sets of variables that show an integrated behaviour and interact more weakly with the rest of the system. The RI method has been applied with interesting results to several systems: some of them had been artificially designed in order to test the effectiveness of the technique, while others referred to interesting physical, chemical, biological, or socio-economic systems [6,7]. In addition, the efficiency of the method has also been improved by using a parallel implementation of the RI computation [8] and some metaheuristics to deal with the "curse of dimensionality" when analysing high-dimensional systems [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Information‐Theoretic Approach to the Detection of Structures in Complex Systems

et al. 2018

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Systems that exhibit complex behaviours often contain inherent dynamical structures which evolve over time in a coordinated way. In this paper, we present a methodology based on the Relevance Index method aimed at revealing the dynamical structures hidden in complex systems. The method iterates two basic steps: detection of relevant variable sets based on the computation of the Relevance Index, and application of a sieving algorithm, which refines the results. This approach is able to highlight the organization of a complex system into sets of variables, which interact with one another at different hierarchical levels, detected, in turn, in the different iterations of the sieve. The method can be applied directly to systems composed of a small number of variables, whereas it requires the help of a custom metaheuristic in case of systems with larger dimensions. We have evaluated the potential of the method by applying it to three case studies: synthetic data generated by a nonlinear stochastic dynamical system, a small-sized and well-known system modelling a catalytic reaction, and a larger one, which describes the interactions within a social community, that requires the use of the metaheuristic. The experiments we made to validate the method produced interesting results, effectively uncovering hidden details of the systems to which it was applied.

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“…The results for finite-size systems identify a critical interval, rather than an exact critical threshold. The methods described by Roli et al [9] also detect criticality; that is, they distinguish between different phases separated by a critical regime. The approach is centered on the relevance index-an information-theoretic ratio relating the multi-information (or integration) measure to the mutual information between a subsystem and the rest of the system.…”

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confidence: 99%

“…The next four papers [8][9][10][11] are placed in the complexity-criticality-computation overlap which is central to our issue. The study of Erten et al [8] continues the information-theoretic theme by applying the information dynamics framework to studies of critical thresholds during epidemics.…”

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Complexity, Criticality and Computation

Prokopenko

2017

Entropy

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What makes a system "complex"? Is it merely the number of components it integrates, a nonlinear nature of the dependencies and feedbacks among its parts, or an unpredictable behavior it exhibits over time? The term "complexity" was initially applied generically to express the lack of predictability, reflecting on the self-organization of a synergistic macroscopic behavior out of interactions between the constituent microscopic parts, and the emergence of global patterns. Without a doubt, by now the concept has acquired a fairly definitive meaning, describing a distinct field of research and education and a new approach to science and engineering. There are abundant examples showing that the enterprise of Complex Systems, having achieved a substantial level of maturity, reaches back into our everyday lives, revealing patterns of complexity that should be considered without employing a reductionist logic [1].Similarly, the idea of criticality was originally motivated by studies of various crises and disruptive events, as well as sensitivities to initial conditions, but over time has developed into a precise field: critical dynamics. Research into critical dynamics is typically focused on the behavior of dynamical spatiotemporal systems during phase transitions where scale invariance prevails and symmetries break. Crucially, such behavior can be understood in terms of the control and order parameters. For instance, a second-order phase transition in a ferromagnetic system, separating two qualitatively different behaviors, can be reached by controlling the temperature parameter: the "disordered" and isotropic (symmetric) high-temperature phase is characterized by the absence of net magnetization, while the "ordered" and anisotropic (less symmetric) low-temperature phase can be described by an order parameter, the net magnetization vector defining a preferred direction in space. Critical phenomena have become associated with the physics of critical points, such as fractal behavior, the divergence of the correlation length, power-law divergences (e.g., the divergence of the magnetic susceptibility in the ferromagnetic phase transition), universality of relevant critical exponents, and so on. Now, these precise theoretical notions begin to reconnect with their motivating applied studies of crisis modeling, forecasting, and response. There is a growing awareness that complexity is strongly related to criticality, and many examples of self-organizing complex systems can be found in applications managing complexity specifically at critical regimes.A similar loop originating in practical studies, maturing to an exact science with precise but narrow definitions, and then reaching back to applied scenarios, can be seen in the realm of distributed computation. These days, complex systems can be viewed as distributed information-processing systems, in the domains ranging from systems biology and artificial life to computational neuroscience, digital circuitry, and transport networks [2]. Consciousness emerging from neuronal ac...

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Identifying Critical States through the Relevance Index

Cited by 15 publications

References 21 publications

A Relevance Index Method to Infer Global Properties of Biological Networks

A Relevance Index Method to Infer Global Properties of Biological Networks

An Iterative Information‐Theoretic Approach to the Detection of Structures in Complex Systems

Complexity, Criticality and Computation

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