New Paths for the Application of DCI in Social Sciences: Theoretical Issues Regarding an Empirical Analysis

Righi, Riccardo; Roli, Andrea; Russo, Margherita; Serra, Roberto; Villani, Marco

doi:10.1007/978-3-319-57711-1_4

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…We can therefore use the same framework for both situations. Indeed, we analyzed several systems with strong dynamical organization, by juxtaposing the states belonging to different asymptotic behaviors of the same system (different attractors of a genetic regulatory network [ 20 , 31 , 32 ] and patients affected by the same kind of disease [ 33 ]) or by observing the trajectory of a single system (a socio-economic system [ 34 ]), sometimes perturbing it (metabolic networks [ 24 ] and autocatalytic systems [ 19 , 35 ]). The performed RI analyses show some common characteristics, so in this paper we choose to expose them by commenting in detail a particular system: an autocatalytic reaction network introduced in [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we presented a methodology for the identification of mesolevel objects, which we call relevant subsets, based on entropic measures, which may involve dynamical aspects [ 19 , 24 , 27 , 34 ] or juxtapose different realizations within a population of individuals sharing the same common organization [ 20 , 31 , 33 ]. We identified an entropic measure useful for the detection of relevant subsets and studied its theoretical distribution, a fact that helps in the interpretation of the results and allows to avoid the excessively onerous bootstrap calculations from a homogeneous system that are needed to compare groups of different size.…”

Section: Discussionmentioning

confidence: 99%

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Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

D’Addese

Sani²,

Rocca³

et al. 2021

Entropy

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The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

D’Addese

Sani²,

Rocca³

et al. 2021

Entropy

Self Cite

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“…The second example is a deterministic simulation of a chemical system (a Catalytic Reaction Network, CRN) 6 Complexity described by 22 variables. Given its limited dimension, this system has been analysed using an exhaustive search over all possible variable subsets.…”

Section: Resultsmentioning

confidence: 99%

“…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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“…We have applied our method to some artificial models, in order to test its capability to actually detect RSs, and to some interesting cases from natural and also social sciences, including the interpretation of metabolic pathways [22] and of autocatalytic systems in chemistry [14,15], the identification of communities in socio-economic systems [13,23], the detection of specific groups of genes ruling the dynamics of genetic networks [14,22,24] and of specific groups of mutations in cancer progressions [20].…”

Section: Introductionmentioning

confidence: 99%

A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems

et al. 2021

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In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case.

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New Paths for the Application of DCI in Social Sciences: Theoretical Issues Regarding an Empirical Analysis

Cited by 6 publications

References 12 publications

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

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

A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems

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