Marisa FAGGINI | Projects

EXTREME EVENTS ON COMPLEX ECONOMIC NETWORKS

The structural characteristics of complex systems permits to be described as networks characterized as decentralized, non-hierarchical, dispersed, and distributed. To study network complex system imply manage increasing mass of data with appropriate empirical methods involving interdisciplinary development of a new science of complex systems. The field of complex systems cuts across many diverse disciplines including mathematics, engineering, computer science, chemistry, physics, philosophy, psychology, sociology, economics, management, medicine, molecular biology and anthropology.In particular the new approach to the networks brings a whole new perspective on the role of coevolving interdependency in large and complex economic systems.The economy is described composed by thousands or millions of individual entities that have different degrees of connectivity inside the network. There are nodes with a high degree of connectivity meaning that few economic agents concentrate the power in order to dominate the market and to influence the transactions, and the large majority of nodes with low connectivity that’s agents linked only with a small group of other agents. These interactions are also characterized by an evolutionary process that might occur at multiple and different levels. So, the agents change status, as influenced by neighboring nodes, new agents can come into the network while others eventually abandon it, creating or extinguishing links across vertices (Gomes 2014).Following Newman [7], there are three interrelated approaches to the modern study of complex networks. The first is find statistical properties that characterize the structure and dynamic behaviour of networked systems; Second is build models of networks that explain and help understand how they are created and how they evolve; and finally predict the behavior of networked systems. The extreme events on complex network can affect all the approches and can redesigned the network.Extreme events taking place on the networks is a fairly common place experience. Traffic jams in roads and other transportation networks, web servers not responding due to heavy load of web requests, floods in the network of rivers, power black outs due to tripping of power grids are some of the common examples of EE on networks.Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest.As Extreme Events lead to losses ranging from financial and productivity to even of life and property, it is important to estimate probabilities for their occurrence and, when they occur how to manage this occurrence and, finally if possible, incorporate them to design networks that can handle such phenomena and to develop effective control strategies” in order to understand, assessment, mitigation, and if possible, prediction of these events. In this project, we will introduce the idea of extreme events on complex networks and review some of the important results and applications in economics. The aim is to design such networks that can self- organize, self-adapt and optimize their interactions and functions in a continuous and robust manner in order to best reply to the occurrence of an extreme events with attention paid to the risk of cascading failure. In our case how the analysis will be devoted to the current global crisis and the its contagion effects.