Qualitative and quantitative methods in complex socio-economic systems

Abstract

Several phenomena of real world and many concrete problems, in different contexts, can be addressed through the concept of “complex system”, i.e. a multiplicity of agents interacting both among themselves and with the environment they are embedded in. This is confirmed by the continuously growing interest showed by the scientific community towards “complexity” and its applications to real issues.Moreover, in recent years the toolbox of both qualitative and quantitative models designed to describe and better understand complex systems has found various fields of application, particularly within the socio-economic area. Network models, stochastic and dynamics systems, social network analysis, multivariate statistics, inference and stochastic processes, fuzzy theory, relational calculus, partial order theory, multi-criteria decision methods... all of these tools find their place in the study of real complex systems. In this Special Issue, mainly dedicated to complex socio-economic, financial and environmental problems, you will find an application for most of them. Most papers in this issue were originally presented at the 6th International Workshop on Dynamics of Social and Economical Systems held in Ushuaia, Tierra del Fuego, Argentina organized by Asociacion Dyses (Argentina, http://www.dyses.org.ar), Group of Studies in Multifractal and Complexity (GEMC) of Universidad Nac. de Gral. Sarmiento, Laboratorio de Sistemas Complejos, Facultad de Ingenieria-UBA (Argentina)(http://laboratorios.fi.uba. ar/lsc/), and GECSI, Grupo de Estudio de la Complejidad en la Sociedad de la Informacion, Facultad de Ciencias Juridicas y Sociales Universidad Nacional de La Plata (Argentina). Many papers in this volume apply complex network theory to economic or financial fields. L. Catalano and A. Figliola in “Analysis of the nonlinear relationship between commodity prices in the last two decades” describe the construction of a network useful for the study of the correlations among the price indexes of different commodities, obtained by using the Multi-fractal Cross-Correlation method. They consider different networks, where nodes represent commodity groups and links represent cross-correlations, and study their evolution from 1991 to 2012.