Social Welfare and Behavioral Public Policies

In this work I discuss several key aspects of welfare economics and policy analysis and I propose two original contributions to the growing field of behavioral public policymaking. After providing a historical perspective of welfare economics and an overview of policy analysis processes in the introductory chapter, in chapter 2 I discuss a debated issue of policymaking, the choice of the social welfare function. I contribute to this debate by proposing an original methodological contribution based on the analysis of the quantitative relationship among different social welfare functional forms commonly used by policy analysts. In chapter 3 I then discuss a behavioral policy to contrast indirect tax evasion based on the use of lotteries. I show that the predictions of my model based on non-expected utility are consistent with observed, and so far unexplained, empirical evidence of the policy success. Finally, in chapter 4 I investigate by mean of a laboratory experiment the effects of social influence on the individual likelihood to engage in altruistic punishment. I show that bystanders’ decision to engage in punishment is influenced by the punishment behavior of their peers and I suggest ways to enact behavioral policies that exploit this finding.

A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

An optimal estimator for the correlation of CMB anisotropies with Large Scale Structures and its application to WMAP-7year and NVSS

In the thesis we present the implementation of the quadratic maximum likelihood (QML) method, ideal to estimate the angular power spectrum of the cross-correlation between cosmic microwave background (CMB) and large scale structure (LSS) maps as well as their individual auto-spectra. Such a tool is an optimal method (unbiased and with minimum variance) in pixel space and goes beyond all the previous harmonic analysis present in the literature. We describe the implementation of the QML method in the {\it BolISW} code and demonstrate its accuracy on simulated maps throughout a Monte Carlo. We apply this optimal estimator to WMAP 7-year and NRAO VLA Sky Survey (NVSS) data and explore the robustness of the angular power spectrum estimates obtained by the QML method. Taking into account the shot noise and one of the systematics (declination correction) in NVSS, we can safely use most of the information contained in this survey. On the contrary we neglect the noise in temperature since WMAP is already cosmic variance dominated on the large scales. Because of a discrepancy in the galaxy auto spectrum between the estimates and the theoretical model, we use two different galaxy distributions: the first one with a constant bias $b$ and the second one with a redshift dependent bias $b(z)$. Finally, we make use of the angular power spectrum estimates obtained by the QML method to derive constraints on the dark energy critical density in a flat $\Lambda$CDM model by different likelihood prescriptions. When using just the cross-correlation between WMAP7 and NVSS maps with 1.8° resolution, we show that $\Omega_\Lambda$ is about the 70\% of the total energy density, disfavouring an Einstein-de Sitter Universe at more than 2 $\sigma$ CL (confidence level).

The porcine animal model goes through the 3Rs: development of in vitro and ex vivo system to study vascular biology

Since the publication of the book of Russell and Burch in 1959, scientific research has never stopped improving itself with regard to the important issue of animal experimentation. The European Directive 2010/63/EU “On the protection of animals used for scientific purposes” focuses mainly on the animal welfare, fixing the Russell and Burch’s 3Rs principles as the foundations of the document. In particular, the legislator clearly states the responsibility of the scientific community to improve the number of alternative methods to animal experimentation. The swine is considered a species of relevant interest for translational research and medicine due to its biological similarities with humans. The surgical community has, in fact, recognized the swine as an excellent model replicating the human cardiovascular system. There have been several wild-type and transgenic porcine models which were produced for biomedicine and translational research. Among these, the cardiovascular ones are the most represented. The continuous involvement of the porcine animal model in the biomedical research, as the continuous advances achieved using swine in translational medicine, support the need for alternative methods to animal experimentation involving pigs. The main purpose of the present work was to develop and characterize novel porcine alternative methods for cardiovascular translational biology/medicine. The work was mainly based on two different models: the first consisted in an ex vivo culture of porcine aortic cylinders and the second consisted in an in vitro culture of porcine aortic derived progenitor cells. Both the models were properly characterized and results indicated that they could be useful to the study of vascular biology. Nevertheless, both the models aim to reduce the use of experimental animals and to refine animal based-trials. In conclusion, the present research aims to be a small, but significant, contribution to the important and necessary field of study of alternative methods to animal experimentation.