Relationships between Social Capital and regional development in Europe: a close examination

The study of the Social Capital and its relationships with the development is a topical subject. The theme has not an exactly definition yet. Some proofs at national and regional levels in Europe show interactions between the Social Capital and the economic growth and the labour market. From them, the paper aims to analyze these results, trying to specify the significances. Applying the Principal Components Analysis to several interesting single variables (coming from the European Values Survey database), some macro-variables were created and inserted in regressions, producing partial results. These macro-components summarize the elements of the Social Capital and they are broken down as single variables. A benchmarking between subjective variables and quantitative ones is realized to explain the concept of the Social Capital, with the aim of consider the individual and collective insight and the concrete effects of this multi-dimensional idea. To fulfill the analysis, a remark is faced on the relationships between the Social Capital and the development, as the causality between them deserves further examinations.

A Multi-Agent Model of Tax Evasion with Public Expenditure

We develop a model where heterogeneous agents maximize their individual utility based on (after tax) income and on the level of public expenditure (as in Cowell, Gordon, 1988). Agents are different in risk aversion and in the relative preference for public expenditure with respect to personal income. In each period, an agent can optimally conceal some income based on conjectures on the perceived probability of being subject to audits, the perceived level of public expenditure and the perceived amount of tax paid by other individuals. As far as the agent-based model is concerned, we assume that the Government sets the tax rate and the penalties, uses all the revenue to finance public expenditure (with no inefficiency) and fights evasion by controlling a (random) fraction of agents. We show that, through computational experiments based on micro-simulations, stable configurations of tax rates and public expenditure endogenously form in this case as well. In such equilibrium-like situations we find: • a positive relationship between the tax rate and evasion still arises. • tax compliance mainly depends on the distribution of personal features like risk-aversion and the degree of preference for public expenditure. • an endogenous level of tax evasion that is almost not affected by reasonable rates of control. A proper choice of the tax rate results instead in voluntary partial compliance. • the enforcement of higher compliance rates requires unrealistic and costly large-scale audits.Tax evasion, public expenditure, agent-based models

Relationships between Social Capital and regional development in Europe: a close examination

The study of the Social Capital and its relationships with the development is a topical subject. The theme has not an exactly definition yet. Some proofs at national and regional levels in Europe show interactions between the Social Capital and the economic growth and the labour market. From them, the paper aims to analyze these results, trying to specify the significances. Applying the Principal Components Analysis to several interesting single variables (coming from the European Values Survey database), some macro-variables were created and inserted in regressions, producing partial results. These macro-components summarize the elements of the Social Capital and they are broken down as single variables. A benchmarking between subjective variables and quantitative ones is realized to explain the concept of the Social Capital, with the aim of consider the individual and collective insight and the concrete effects of this multi-dimensional idea. To fulfill the analysis, a remark is faced on the relationships between the Social Capital and the development, as the causality between them deserves further examinations

From Strategic Planning to City Branding: Some Empirical Evidence in Italy

En un mundo global en el que los sitios compiten entre ellos, la imagen de la ciudad juega un papel crucial para atraer turistas e inversores, y para conseguir que los ciudadanos se queden satisfechos y evitar su desplazamiento. A través de la exploración de conexiones e implicaciones entre la teoria y los resultados empíricos obtenidos en varias ciudades italianas en relación a su atractivo para turistas e inversores, este artículo pretende ofrecer una útil visión general para académicos y profesionales. El objetivo es no sólo el de revisar la extensa literatura de la planificación estratégica en marketing y el proceso de marca de las ciudades, sino también es el de concentrarse específicamente en algunos casos italianos (Turín, Génova, Venecia y Piacenza), donde la aplicación de los instrumentos mencionados ha proporcionado resultados interesantes para comparar

OpenKnowledge at work: exploring centralized and decentralized information gathering in emergency contexts

Real-world experience teaches us that to manage emergencies, efficient crisis response coordination is crucial; ICT infrastructures are effective in supporting the people involved in such contexts, by supporting effective ways of interaction. They also should provide innovative means of communication and information management. At present, centralized architectures are mostly used for this purpose; however, alternative infrastructures based on the use of distributed information sources, are currently being explored, studied and analyzed. This paper aims at investigating the capability of a novel approach (developed within the European project OpenKnowledge1) to support centralized as well as decentralized architectures for information gathering. For this purpose we developed an agent-based e-Response simulation environment fully integrated with the OpenKnowledge infrastructure and through which existing emergency plans are modelled and simulated. Preliminary results show the OpenKnowledge capability of supporting the two afore-mentioned architectures and, under ideal assumptions, a comparable performance in both cases

Multimap targeted free energy estimation

We present a new method to compute free energies at a quantum mechanical (QM) level of theory from molecular simulations using cheap reference potential energy functions, such as force fields. To overcome the poor overlap between the reference and target distributions, we generalize targeted free energy perturbation (TFEP) to employ multiple configuration maps. While TFEP maps have been obtained before from an expensive training of a normalizing flow neural network (NN), our multimap estimator allows us to use the same set of QM calculations to both optimize the maps and estimate the free energy, thus removing almost completely the overhead due to training. A multimap extension of the multistate Bennett acceptance ratio estimator is also derived for cases where samples from two or more states are available. Furthermore, we propose a one-epoch learning policy that can be used to efficiently avoid overfitting when computing the loss function is expensive compared to generating data. Finally, we show how our multimap approach can be combined with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. We test our method on the HiPen dataset of drug-like molecules and fragments, and we show that it can accelerate the calculation of the free energy difference of switching from a force field to a DFTB3 potential by about 3 orders of magnitude compared to standard FEP and by a factor of about 8 compared to previously published nonequilibrium calculations.Comment: Added Algorithm 1, wall-clock timings, additional uncertainty estimates, and other minor edits. Main Text: 12 pages, 5 figures, 7 equations. Supplemental Material: 17 pages, 5 figures, 22 equation

Effective Data-Driven Collective Variables for Free Energy Calculations from Metadynamics of Paths

A variety of enhanced sampling methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets -- ideally incorporating information about physical pathways and transition states -- which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of enhanced sampling simulations in trajectory space via the metadynamics of paths [arXiv:2002.09281] algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in enhanced sampling simulations. We demonstrate our approach with two numerical examples, a two-dimensional model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice