Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

Emergent behavior in multiplicative critical processes and applications to economy

Tese de doutoramento, Física, Universidade de Lisboa, Faculdade de Ciências, 2014The main objective of this thesis is to develop a theoretical foundation for the study of economic phenomena based on methods of statistical physics applied to a system composed by set of multiplicative processes. An equivalent of equilibrium is established for such system and proved to behave statistically as in thermal equilibrium. An equivalent to canonical and microcanonical ensembles is realized and the relation with the theory of scale-free complex networks is made. The statistics of more than one century of US economy is studied in the light of these findings and an explanation for inflation and the resilience of wealth inequalities is found. The equivalent of Markov stochastic process on the set of multiplicative processes is established and the corresponding Fokker-Plank equation is derived. Moreover, a relation with self-organized criticality (SOC) is made. The study of market fluctuations is done using SOC models and yielding the same result as the Fokker-Planck approach. Based on these findings, we will argue that the distribution on the fluctuations of prices in organized market cannot follow Levy-stable distributions as stated by Mandelbrot.89 Finally, some applications to market and credit risk are made.O objectivo principal desta tese é o desenvolvimento de um novo equadramento teórico para o estudo dos fenómenos económicos baseado em métodos da física estatística aplicada a sistema composto por um agregado de processos multiplicativos. Num tal sistema, um estado equivalente ao estado de equil1íbrio emerge e demonstra-se que o seu comportamento estatístico é semelhante a um sistema em equilíbrio térmico. É realizado o estudo dos correspondentes ensembles canónico e microcanónico e feita a ligação com a teoria das redes complexas livres de escala. A estatística de mais de um século de economia dos EUA é estudada à luz destes desenvolvimentos é.dada uma explicação para os fenómenos da inflacção e da resiliência das desiguladades sociais. O equivalente ao processos estocásticos Markovianos no agregado de processos multiplicativos é establecido com o desenvolvimento da correspondente equação de Fokker-Planck e é feita a relacção com o fenómeno da criticalidade auto-organizada(SOC). O estudo das flutuações nos preços de mercado usando modelos SOC é feito levando ao mesmo resultado esperado pela abordagem equação de Fokker-Planck, o que nos vai permitir no futuro fazer a ligação com conjunto de ferramentas desenvolvidas pela matemática financeira. Baseado nestes resultados, argumentamos que as flutuações dos preços de mercado não podem seguir distribuições Lévy estáveis como propunha Mandelbrot89.Finalmente, algumas aplicações do enquadramento teórico são apresentadas.Fundação para a Ciência e a Tecnologia (FCT

The random diffusivity approach for diffusion in heterogeneous systems.

164 p.The two hallmark features of Brownian motion are the linear growth of the meansquared displacement (MSD) with diffusion coefficient D in d spatial dimensions, andthe Gaussian distribution of displacements. With the increasing complexity of thestudied systems deviations from these two central properties have been unveiledover the years. Recently, a large variety of systems have been reported in which theMSD exhibits the linear growth in time of Brownian (Fickian) transport, however, thedistribution of displacements is pronouncedly non-Gaussian (Brownian yet non-Gaussian, BNG). A similar behaviour is also observed for viscoelastic-type motionwhere an anomalous trend of the MSD is combined with a priori unexpected non-Gaussian distributions (anomalous yet non-Gaussian, ANG). This kind of behaviourobserved in BNG and ANG diffusions has been related to the presence ofheterogeneities in the systems and a common approach has been established toaddress it, that is, the random diffusivity approach.This dissertation explores extensively the field of random diffusivity models. Startingfrom a chronological description of all the main approaches used as an attempt ofdescribing BNG and ANG diffusion, different mathematical methodologies aredefined for the resolution and study of these models.The processes that are reported in this work can be classified in threesubcategories, i) randomly-scaled Gaussian processes, ii) superstatistical modelsand iii) diffusing diffusivity models, all belonging to the more general class of randomdiffusivity models.Eventually, the study focuses more on BNG diffusion, which is by now wellestablishedand relatively well-understood. Nevertheless, many examples arediscussed for the description of ANG diffusion, in order to highlight the possiblescenarios which are known so far for the study of this class of processes.The second part of the dissertation deals with the statistical analysis of randomdiffusivity processes. A general description based on the concept of momentgeneratingfunction is initially provided to obtain standard statistical properties of themodels. Then, the discussion moves to the study of the power spectral analysis andthe first passage statistics for some particular random diffusivity models. Acomparison between the results coming from the random diffusivity approach andthe ones for standard Brownian motion is discussed. In this way, a deeper physicalunderstanding of the systems described by random diffusivity models is alsooutlined.To conclude, a discussion based on the possible origins of the heterogeneity issketched, with the main goal of inferring which kind of systems can actually bedescribed by the random diffusivity approach

The random diffusivity approach for diffusion in heterogeneous systems

The two hallmark features of Brownian motion are the linear growth $\langle x^2(t) \rangle = 2 D d t$ of the mean squared displacement (MSD) with diffusion coefficient $D$ in $d$ spatial dimensions, and the Gaussian distribution of displacements. With the increasing complexity of the studied systems deviations from these two central properties have been unveiled over the years. Recently, a large variety of systems have been reported in which the MSD exhibits the linear growth in time of Brownian (Fickian) transport, however, the distribution of displacements is pronouncedly non-Gaussian (Brownian yet non-Gaussian, BNG). A similar behaviour is also observed for viscoelastic-type motion where an anomalous trend of the MSD, i.e., $\langle x^2(t) \rangle \sim t^\alpha$, is combined with a priori unexpected non-Gaussian distributions (anomalous yet non-Gaussian, ANG). This kind of behaviour observed in BNG and ANG diffusions has been related to the presence of heterogeneities in the systems and a common approach has been established to address it, that is, the random diffusivity approach. This dissertation explores extensively the field of random diffusivity models. Starting from a chronological description of all the main approaches used as an attempt of describing BNG and ANG diffusion, different mathematical methodologies are defined for the resolution and study of these models. The processes that are reported in this work can be classified in three subcategories, i) randomly-scaled Gaussian processes, ii) superstatistical models and iii) diffusing diffusivity models, all belonging to the more general class of random diffusivity models. Eventually, the study focuses more on BNG diffusion, which is by now well-established and relatively well-understood. Nevertheless, many examples are discussed for the description of ANG diffusion, in order to highlight the possible scenarios which are known so far for the study of this class of processes. The second part of the dissertation deals with the statistical analysis of ran- dom diffusivity processes. A general description based on the concept of moment- generating function is initially provided to obtain standard statistical properties of the models. Then, the discussion moves to the study of the power spectral analysis and the first passage statistics for some particular random diffusivity models. A comparison between the results coming from the random diffusivity approach and the ones for standard Brownian motion is discussed. In this way, a deeper physical understanding of the systems described by random diffusivity models is also outlined. To conclude, a discussion based on the possible origins of the heterogeneity is sketched, with the main goal of inferring which kind of systems can actually be described by the random diffusivity approach.BERC.2018-202

Alternative explanations of hierarchical differentiation in urban systems

The hierarchical differentiation of urban systems has been noticed for a long time and various explanations have been suggested. Among them: an intentional functional organisation for controlling a territory; the application of a spatial economic equilibrium principle; a “purely” random growth process; the statistical addition of Pareto-like elementary phenomena; self-organisation or co-evolution of competing subsystems, without constraint or under space-time optimisation principle. We review these explanations and related methods of analysis, trying to assess their relevance and exploring the possible similarities between urban dynamics and other types of hierarchical complex systems

Recent Advances in Single-Particle Tracking: Experiment and Analysis

This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion