1,689 research outputs found
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
Estimating poverty maps from aggregated mobile communication networks
Governments and other organisations often rely on data collected by household surveys and censuses to provide estimates of household poverty and identify areas in most need of regeneration and development investment. However, due to the high cost associated with manual data collection and processing, many developing countries conduct such surveys very infrequently, if at all, and only at a coarse level of spatial granularity. Consequently, it becomes difficult for governments and NGOs to determine where and when to intervene. This thesis addresses this problem by examining the feasibility of deriving up to date and high resolution proxy measurements of poverty from an alternative source of data, namely, Call Detail Records (CDRs), which can be used by organisations to help in decision making. Specifically, we contribute the following: 1. A detailed spatial analysis of economic wealth in two sub-Saharan countries, Senegal and Cote dāIvoire from which we derive two baseline poverty esti- Ė mators grounded on concrete usage scenarios. 2. We establish a link between communication patterns and wealth through a simulation-based analysis of information diffusion. We further examine the influence of contextual factors, including data quality issues and economic volatility, on the strength of this relationship. 3. An approach to building wealth prediction models based on features of aggregated CDRs. Features include static and simulation based measures of information access, activity based metrics and econometric inspired metrics. We further perform a comparative analysis of the results of several models in relation to the baseline predictors. We conclude that it is possible to produce proxy poverty or wealth indicators from aggregated CDRs that provide a good level of accuracy, particularly where geographical coverage of the mobile phone network is sufficient. The final outcome of this thesis is a method for developing aggregated CDR-based poverty or wealth models that can be readily implemented anywhere in which there is a need for more up to date and/or finer resolution poverty estimates
A calibrated measure to compare fluctuations of different entities across timescales
Ā© 2020 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisherās website: https://doi.org/10.1038/s41598-020-77660-4A common way to learn about a systemās properties is to analyze temporal fluctuations in associated variables. However, conclusions based on fluctuations from a single entity can be misleading when used without proper reference to other comparable entities or when examined only on one timescale. Here we introduce a method that uses predictions from a fluctuation scaling law as a benchmark for the observed standard deviations. Differences from the benchmark (residuals) are aggregated across multiple timescales using Principal Component Analysis to reduce data dimensionality. The first component score is a calibrated measure of fluctuationsāthe reactivityRA of a given entity. We apply our method to activity records from the media industry using data from the Event Registry news aggregatorāover 32M articles on selected topics published by over 8000 news outlets. Our approach distinguishes between different news outlet reporting styles: high reactivity points to activity fluctuations larger than expected, reflecting a bursty reporting style, whereas low reactivity suggests a relatively stable reporting style. Combining our method with the political bias detector Media Bias/Fact Check we quantify the relative reporting styles for different topics of mainly US media sources grouped by political orientation. The results suggest that news outlets with a liberal bias tended to be the least reactive while conservative news outlets were the most reactive.The work was partially supported as RENOIR Project by the European Union Horizon 2020 research and innovation programme under the Marie SkÅodowskaāCurie Grant Agreement No. 691152 and by Ministry of Science and Higher Education (Poland), Grant Nos. 34/H2020/2016, 329025/PnH/2016 and by National Science Centre, Poland Grant No. 2015/19/B/ST6/02612. J.A.H. was partially supported by the Russian Scientific Foundation, Agreement #17-71-30029 with co-financing of Bank Saint Petersburg and by POB Research Centre Cybersecurity and Data Science of Warsaw University of Technology within the Excellence Initiative ProgramāResearch University (IDUB).Published onlin
An Initial Framework Assessing the Safety of Complex Systems
Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Unionās Horizon 2020 research and innovation programme under the Marie SkÅodowska-Curie grant agreement No. 813844
Contagion aĢ effet de seuil dans les reĢseaux complexes
Networks arise frequently in the study of complex systems, since interactions among the components of such systems are critical. Networks can act as a substrate for dynamical process, such as the diffusion of information or disease throughout populations. Network structure can determine the temporal evolution of a dynamical process, including the characteristics of the steady state.The simplest representation of a complex system is an undirected, unweighted, single layer graph. In contrast, real systems exhibit heterogeneity of interaction strength and type. Such systems are frequently represented as weighted multiplex networks, and in this work we incorporate these heterogeneities into a master equation formalism in order to study their effects on spreading processes. We also carry out simulations on synthetic and empirical networks, and show that spreading dynamics, in particular the speed at which contagion spreads via threshold mechanisms, depend non-trivially on these heterogeneities. Further, we show that an important family of networks undergo reentrant phase transitions in the size and frequency of global cascades as a result of these interactions.A challenging feature of real systems is their tendency to evolve over time, since the changing structure of the underlying network is critical to the behaviour of overlying dynamical processes. We show that one aspect of temporality, the observed āburstinessā in interaction patterns, leads to non-monotic changes in the spreading time of threshold driven contagion processes.The above results shed light on the effects of various network heterogeneities, with respect to dynamical processes that evolve on these networks.Les interactions entre les composants des systeĢmes complexes font eĢmerger diffeĢrents types de reĢseaux. Ces reĢseaux peuvent jouer le roĢle dāun substrat pour des processus dynamiques tels que la diffusion dāinformations ou de maladies dans des populations. Les structures de ces reĢseaux deĢterminent lāeĢvolution dāun processus dynamique, en particulier son reĢgime transitoire, mais aussi les caracteĢristiques du reĢgime permanent.Les systeĢmes complexes reĢels manifestent des inteĢractions heĢteĢrogeĢnes en type et en intensiteĢ. Ces systeĢmes sont repreĢseteĢs comme des reĢseaux pondeĢreĢs aĢ plusieurs couches. Dans cette theĢse, nous deĢveloppons une eĢquation maiĢtresse afin dāinteĢgrer ces heĢteĢrogeĢneĢiteĢs et dāeĢtudier leurs effets sur les processus de diffusion. AĢ lāaide de simulations mettant en jeu des reĢseaux reĢels et geĢneĢreĢs, nous montrons que les dynamiques de diffusion sont lieĢes de manieĢre non triviale aĢ lāheĢteĢrogeĢneĢiteĢ de ces reĢseaux, en particulier la vitesse de propagation dāune contagion baseĢe sur un effet de seuil. De plus, nous montrons que certaines classes de reĢseaux sont soumises aĢ des transitions de phase reĢentrantes fonctions de la taille des āglobal cascadesā.La tendance des reĢseaux reĢels aĢ eĢvoluer dans le temps rend difficile la modeĢlisation des processus de diffusion. Nous montrons enfin que la dureĢe de diffusion dāun processus de contagion baseĢ sur un effet de seuil change de manieĢre non-monotone du fait de la preĢsence deārafalesā dans les motifs dāinteĢractions. Lāensemble de ces reĢsultats mettent en lumieĢre les effets de lāheĢteĢrogeĢneĢiteĢ des reĢseaux vis-aĢ-vis des processus dynamiques y eĢvoluant
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)
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