Ricerc@Sapienza

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes.

Citation counts are increasingly used to create rankings of scholars or institutions: while social scientists are often skeptical of the resulting indexes, economists have mostly been supporters of this approach. Yet, citation metrics have raised two debates in the literature: empirical, regarding their technical use, and theoretical, regarding their meaning and, more generally, the meaning of “scientific quality.” I review this literature highlighting the consequences for the use of citations for research assessment.

This paper provides a unified view for defining a measure of the reasons behind migration
flows whose nature is of social and economic type. To this aim, worldwide migration
flows are here presented in the context of complex network and a related socio-economic indicator
is conceptualized. The ingredients of the indicator also include the economic strengths of the
countries and how they behave in terms of community structure, where community" has to
be intended in the sense of how countries interact in terms of immigration and emigration.

Networks are at the core of modeling many engineering contexts, mainly in the case of infrastructures and communication systems. The resilience of a network, which is the property of the system capable of absorbing external shocks, is then of paramount relevance in the applications. This paper deals with this topic by advancing a theoretical proposal for measuring the resilience of a network. The proposal is based on the study of the shocks propagation along the patterns of connections among nodes.

Service Systems, Networks, and Ecosystems: Connecting the Dots Concisely from a Systems Perspective

We discuss convergence results for a class of finite difference schemes approximating Mean Field Games systems either on the torus or a network. We also propose a quasi-Newton method for the computation of discrete solutions, based on a least squares formulation of the problem. Several numerical experiments are carried out including the case with two or more competing populations.

We consider a system of differential equations of Monge–Kantorovich type which describes the equilibrium configurations of granular material poured by a constant source on a network. Relying on the definition of viscosity solution for Hamilton–Jacobi equations on networks introduced in [P.-L. Lions and P. E. Souganidis, Atti Accad. Naz. Lincei Rend. Lincei Mat. Appl., 27 (2016), pp. 535–545], we prove existence and uniqueness of the solution of the system and we discuss its numerical approximation. Some numerical experiments are carried out.

We propose a numerical method for stationary Mean Field Games defined on a network.
In this framework a correct approximation of the transition conditions at the vertices plays a crucial
role. We prove existence, uniqueness and convergence of the scheme and we also propose a least squares
method for the solution of the discrete system. Numerical experiments are carried out.

In the latest years, there has been a growing interest in autonomous drone delivery. This is due to the increasing demand for efficient delivery services, and to the concurrent inability of existing ground based systems to provide guaranteed availability, and delivery time. However, the cost for implementing a centralized drone-based delivery service can only be afforded by large commercial organizations.