Minority Games, Local Interactions, and Endogenous Networks

In this paper we study a local version of the Minority Game where agents are placed on the nodes of a directed graph. Agents care about beingin the minority of the group of agents they are currently linked to and employ myopic best-reply rules to choose their next-period state. We show that, in this benchmark case, the smaller the size of local networks, the larger long-run population-average payoffs. We then explore the collective behavior of the system when agents can: (i) assign weights to each link they hold and modify them over time in response to payoff signals; (ii) delete badly-performing links (i.e. opponents) and replace them with randomly chosen ones. Simulations suggest that, when agents are allowed to weight links but cannot delete/replace them, the system self-organizes into networked clusters which attain very high payoff values. These clustered configurations are not stable and can be easily disrupted, generating huge subsequent payoff drops. If however agents can (and are sufficiently willing to) discard badly performing connections, the system quickly converges to stable states where all agents get the highest payoff, independently of the size of the networks initially in placeMinority Games, Local Interactions, Non-Directed Graphs, Endogenous Networks, Adaptive Systems.

Are Output Growth-Rate Distributions Fat-Tailed? Some Evidence from OECD Countries

This work explores some distributional properties of aggregate output growth-rate time series. We show that, in the majority of OECD countries, output growth-rate distributions are well approximated by symmetric exponential power densities with tails much fatter than those of a Gaussian (but with finite moments of any order). Fat tails robustly emerge in output growth rates independently of: (i) the way we measure aggregate output; (ii) the family of densities employed in the estimation; (iii) the length of time lags used to compute growth rates. We also show that fat tails still characterize output growth-rate distributions even after one washes away outliers, autocorrelation and heteroscedasticity

Spatial Localization in Manufacturing: A Cross-Country Analysis

This paper employs a homogeneous-firm database to investigate industry localization in European countries. More specifically, it compares, across industries and countries, the predictions of two of the most popular localization indexes, that is, the Ellison and Glaeser index of 1997 and the Duranton and Overman index of 2005. Independently from the index used, it is found that localization is a pervasive phenomenon in all countries studied; and the degree of localization is very unevenly distributed across industries in each country. Furthermore, it is shown that in all countries localized sectors are mainly ‘traditional’ sectors or, if one controls for country industrial structures, science-based sectors. Moreover, it is found that the two indexes significantly diverge in predicting the intensity of localization of the same industry both across and within countries. In turn, these differences point to the different role played by pecuniary versus non-pecuniary externalities in driving firms' location decisions

Segregation in Networks

International audienc

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table

Country centrality in the international multiplex network

Abstract In this work we introduce and analyze a new and comprehensive multilayer dataset covering a wide spectrum of international relationships between coutries. We select two cross sections of the dataset corresponding to years 2003 and 2010 with 19 layers and 112 nodes to study the structure and evolution of the network. Country centrality is measured by the multiplex PageRank (MultiRank) and the multiplex hub and authority scores (MultiHub and MultiAuth). We find that the MultiHub measure has the highest correlation to GDP per capita, with respect to the other multilayer measures and to their single layer analogues. Finally we analyze the differences in the ranking between GDP per capita and the multilayer centrality measures to evaluate them as measures of development

CONTEST : a Controllable Test Matrix Toolbox for MATLAB

Large, sparse networks that describe complex interactions are a common feature across a number of disciplines, giving rise to many challenging matrix computational tasks. Several random graph models have been proposed that capture key properties of real-life networks. These models provide realistic, parametrized matrices for testing linear system and eigenvalue solvers. CONTEST (CONtrollable TEST matrices) is a random network toolbox for MATLAB that implements nine models. The models produce unweighted directed or undirected graphs; that is, symmetric or unsymmetric matrices with elements equal to zero or one. They have one or more parameters that affect features such as sparsity and characteristic pathlength and all can be of arbitrary dimension. Utility functions are supplied for rewiring, adding extra shortcuts and subsampling in order to create further classes of networks. Other utilities convert the adjacency matrices into real-valued coefficient matrices for naturally arising computational tasks that reduce to sparse linear system and eigenvalue problems