Classical economic theories prescribe specialization of countries industrial production. Inspection the country databases exported products shows that this is not case: successful are extremely diversified, in analogy with biosystems evolving a competitive dynamical environment. The challenge assessing quantitatively non-monetary advantage diversification which represents hidden potential for development and growth. Here we develop new statistical approach based on coupled non-linear maps,...

We investigate a recent methodology we have proposed to extract valuable information on the competitiveness of countries and complexity products from trade data. Standard economic theories predict high level specialization in specific industrial sectors. However, direct analysis official databases exported by all shows that actual situation is very different. Countries commonly considered as developed ones are extremely diversified, exporting large variety simple complex. At same time...

The renormalization group is the cornerstone of modern theory universality and phase transitions, a powerful tool to scrutinize symmetries organizational scales in dynamical systems. However, its network counterpart particularly challenging due correlations between intertwined scales. To date, explorations are based on hidden geometries hypotheses. Here, we propose Laplacian RG diffusion-based picture complex networks, defining both Kadanoff supernodes' concept, momentum space procedure,...

Heterogeneous and complex networks represent intertwined interactions between real-world elements or agents. Determining the multiscale mesoscopic organization of clusters structures is still a fundamental open problem network theory. By taking advantage recent Laplacian renormalization group (LRG), we scrutinize information diffusion pathways throughout to shed further light on this issue. Based internode communicability, our definition provides clear-cut framework for resolving mesh in...

In this paper we analyze the bipartite network of countries and products from UN data on country production. We define country-country product-product projected networks introduce a novel method filtering information based elements’ similarity. As result find that clustering reveals unexpected socio-geographic links among most competing countries. On same footings can be efficiently used for bottom-up classification produced goods. Furthermore mathematically reformulate “reflections method”...

We address a fundamental problem that is systematically encountered when modeling complex systems: the limitedness of information available. In case economic and financial networks, privacy issues severely limit can be accessed and, as consequence, possibility correctly estimating resilience these systems to events such shocks, crises cascade failures. Here we present an innovative method reconstruct structure partially-accessible systems, based on knowledge intrinsic node-specific...

Abstract Within the last fifteen years, network theory has been successfully applied both to natural sciences and socioeconomic disciplines. In particular, bipartite networks have recognized provide a particularly insightful representation of many systems, ranging from mutualistic in ecology trade economy, whence need pattern detection-oriented analysis order identify statistically-significant structural properties. Such an rests upon definition suitable null models, i.e. choice portion...

We use citation data of scientific articles produced by individual nations in different domains to determine the structure and efficiency national research systems. characterize fitness each nation (that is, competitiveness its system) complexity domain means a non-linear iterative algorithm able assess quantitatively advantage diversification. find that technological leading nations, beyond having largest production papers number citations, do not specialize few domains. Rather, they...

We show that the space in which scientific, technological and economic developments interplay with each other can be mathematically shaped using pioneering multilayer network complexity techniques. build tri-layered of human activities (scientific production, patenting, industrial production) study interactions among them, also taking into account possible time delays. Within this construction we identify capabilities prerequisites are needed to competitive a given activity, even measure how...

After reviewing the basic relevant properties of stationary stochastic processes (SSP), defining terms and quantities, we discuss so-called Harrison-Zeldovich like spectra. These correlations, usually characterized exclusively in k space [i.e., power spectra $P(k)],$ are a fundamental feature all current standard cosmological models. Examining them real note their characteristics to be negative law tail $\ensuremath{\xi}(r)\ensuremath{\sim}\ensuremath{-}{r}^{\ensuremath{-}4},$ sub-Poissonian...

Abstract Since 2007, several contributions have tried to identify early-warning signals of the financial crisis. However, vast majority analyses has focused on systems and little theoretical work been done economic counterpart. In present paper we fill this gap employ tools network theory shed light response world trade crisis 2007 recession 2008–2009. We explored evolution bipartite World Trade Web (WTW) across years 1995–2010, monitoring behavior system both before after 2007. Our analysis...

Systemic sclerosis (SSc) is characterised by a progressive microangiopathy that contributes significantly to the morbidity of patients with SSc. Besides insufficient angiogenesis, defective vasculogenesis altered numbers endothelial precursor cells (EPCs) might also contribute vascular pathogenesis However, different protocols for isolation, enrichment, culture and quantification EPCs are currently used, which complicate comparison interpretation results from studies. The aim European League...

Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling mechanisms driving interactions occurring between distinct groups nodes. One most important issues encountered when modeling bipartite is devising way to obtain (monopartite) projection on layer interest, which preserves much possible information encoded original structure. In present paper we propose an algorithm statistically-validated projections networks,...

A problem typically encountered when studying complex systems is the limitedness of information available on their topology, which hinders our understanding structure and dynamical processes taking place them. paramount example provided by financial networks, whose data are privacy protected: Banks publicly disclose only aggregate exposure towards other banks, keeping individual exposures each single bank secret. Yet, estimation systemic risk strongly depends detailed interbank network. The...

The aim of this project is to develop a stochastic simulation machine that generates individual claims histories non-life insurance claims. This based on neural networks incorporate feature information. We provide fully calibrated scenario generator real data. allows everyone simulate their own synthetic portfolio and back-test thier preferred reserving method.

Andrea Gabriellia, Ronald Richmanb & Mario V. Wüthricha*a Department of Mathematics, RiskLab, ETH Zurich, Switzerlandb QED Actuaries and Consultants, Johannesburg, South Africa

Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales reflecting structural insights that not manifest from analyses of the network topology. Moreover, small-world effects correlate with different hierarchies complicating identification coexisting mesoscopic structures and functional cores. We present communicability analysis effective information throughout complex based on diffusion shed...

Scale invariance profoundly influences the dynamics and structure of complex systems, spanning from critical phenomena to network architecture. Here, we propose a precise definition scale-invariant networks by leveraging concept constant entropy-loss rate across scales in renormalization-group coarse-graining setting. This framework enables us differentiate between scale-free networks, revealing distinct characteristics within each class. Furthermore, offer comprehensive inventory genuinely...