1,721,019 research outputs found
Degree-correlations in a bursting dynamic network model
No full textWe propose a mathematical description of a dynamic network model in which the number of links fluctuates over time according to the degree-preferences of nodes. More specifically, we consider the minimal case of a bipartite directed network where we have two groups of nodes, each group has nodes with a given capability to bear links. One group is composed of nodes that create as many links as possible, the generators. The other group is composed of nodes which delete as many links as possible, i.e., the destroyers. We provide here a novel analytical formulation of the evolution of the system through coupled master equations for the two interacting populations, recovering the steady state degree distributions and a new analytic description of the transient dynamics to the equilibrium. Further, fluctuations are shown to be connected to a peak in degree correlation at a critical point of the system corresponding to equal-size populations of generators and destroyers. We investigate the nature of the neighbor connectivity and the temporal assortativity of the network, noticing that degree correlation are anomalously large at criticality and that they are not a pointwise characterization of the system in time but they emerge as an aggregate temporal property. Moreover, we examine the bursty dynamics of the network as a temporal property where the system heterogeneously evolves over time alternating between periods of low and high connectivity displaying a heavy-tailed distribution in the inter-event times distributions. Finally, we introduce a generalization of the model in which intermittent states can control the velocity of the network’s evolution. We will also provide examples of possible economic applications of the present network model
A phenomenological estimate of the true scale of CoViD-19 from primary data
No full textEstimation of the prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of CoViD-19, and for implementing effective public policy intervention strategies. We discuss a simple yet effective approach to estimate the true number of people infected by SARS-CoV-2, using raw epidemiological data reported by official health institutions in the largest EU countries and the USA
E pluribus, quaedam. Gross Domestic Product out of a Dashboard of Indicators
Full text linkIs aggregate income enough to summarize well-being? We address this long-standing question by exploiting a quantitative approach that studies the relationship between gross domestic product (GDP) and a set of economic, social and environmental indicators for nine developed economies. We introduce a mathematical approach to the analysis of economic indicators. By employing dimensionality reduction and time series reconstruction techniques, we quantify the share of variability stemming from a large set of different indicators that can be compressed into a univariate index. We also evaluate how well this variability can be explained if the univariate index is assumed to be respectively the gross domestic product, national income, household income, or household spending. Our results indicate that all the four univariate measures are doomed to fail in accounting for the variability of all the domains. Even if GDP emerges as the best option among the four economic variables, its quality in synthesizing the variability of indicators belonging to other domains is poor (about 35%). Our approach provides additional support for policy makers interested in measuring the trade offs between income and other relevant social, health and ecological dimensions. Finally, our work adds new quantitative evidence to the vast literature criticizing the usage of GDP as a measure of well-being
Non-Performing Loans and Systemic Risk in Financial Networks
No full textIn this paper we study the impact of non-performing loans (NPLs) on financial stability using a
network based approach. We start by combining loan-level data from DealScan and firm-level data from Orbis to reconstruct the global financial network in 1991-2016 and identify a series of stylized facts. We show that many regularities found at national level by the literature hold also at international level. Based on our empirical findings, we develop a simple network model in which banks and firms are linked by their reciprocal claims and study how an exogenous increase in NPLs affects the stability of the system. We investigate the model using Monte Carlo simulations and show that there exists a critical threshold of NPLs beyond which a systemic crisis occurs. This implies that small variations in the magnitude of the initial shock can have very different consequences at the aggregate level
Non-performing loans, systemic risk and resilience in financial networks
No full textAfter the outbreak of the financial crisis in 2007-2008 the level of non-performing loans (NPLs) in the economy has generally increased. However, while in some countries this has been a transitory phenomenon, in others it still represents a major threat for economic recovery and financial stability. The present work investigates the relationship between non-performing loans and systemic risk using a network-based approach. In particular, we analyze how an increase in NPLs at firm level propagates to the financial system through the network of credits and debits. To this end we develop a model with two types of agents, banks and firms, linked one another in a two-layers structure by their reciprocal credits and debits. The model is analyzed via numerical simulations and allows a) to define a synthetic measure of systemic risk and b) to quantify the resilience of the financial system to external shocks, making it particularly useful from a policy point of view. For illustrative purposes, in section 3 we present an application of the model to Italy, Germany, and United Kingdom, using empirically observed data for the three countries
Criticality and Transmission of Information in a Swarm of Cooperative Units
No full textWe show that the intelligence of a swarm of cooperative units (birds) emerges at criticality, as an effect of the joint action of frequent organizational collapses and of spatial correlation as extended as the flock size. The organizational collapses make the birds become independent of one another, thereby allowing the flock to follow the direction of the lookout birds. Long-range correlation violates the principle of locality, making the lookout birds transmit information on either danger or resources with a time delay determined by the time distance between two consecutive collapses
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