Spreading of an infectious disease between different locations

The endogenous adaptation of agents, that may adjust their local contact network in response to the risk of being infected, can have the perverse effect of increasing the overall systemic infectiveness of a disease. We study a dynamical model over two geographically distinct but interacting locations, to better understand theoretically the mechanism at play. Moreover, we provide empirical motivation from the Italian National Bovine Database, for the period 2006-2013.Comment: 22 pages (and 10 pages for appendix); 11 figures (2 in appendix

Discrete Models of Information Diffusion in Networks

In this work we deal with models of diffusion in networks. Cascade and Threshold models are studied, then "influence aggregation" is defined by means of aggregation functions, t-conorms and co-copulas. Also diffusion maximization in networks is described. Since this is a NP-hard problem, a greedy algorithm and a Shapley-value based algorithm are described in order to approximate the solutions

No-vaxxers are different in public good games

In September 2021 we conducted a survey to 1482 people in Italy, when the vaccination campaign against Covid19 was going on. In the first part of the survey we run three simple tests on players’ behavior in standard tasks with monetary incentives to measure their risk attitudes, willingness to contribute to a public good in an experimental game, and their beliefs about others’ behavior. In the second part, we asked respondents if they were vaccinated and, if not, for what reason. We classified as no-vaxxers those (around 12% of the sample) who did not yet start the vaccination process and declared that they intended not to do it in the future. We find that no-vaxxers contribute less to the public good in the experimental game because they trust others less to do so. from the three tests we extrapolated a classification based on the benchmark of rationality and other-regarding preferences for each respondent, and we found that in this respect no-vaxxers do not differ from the rest of the population

Efficiency and Stability in a Process of Teams Formation

Motivated by data on coauthorships in scientific publications, we analyze a team formation process that generalizes matching models and network formation models, allowing for overlapping teams of heterogeneous size. We apply different notions of stability: myopic team-wise stability, which extends to our setup the concept of pair-wise stability, coalitional stability, where agents are perfectly rational and able to coordinate, and stochastic stability, where agents are myopic and errors occur with vanishing probability. We find that, in many cases, coalitional stability in no way refines myopic team-wise stability, while stochastically stable states are feasible states that maximize the overall number of activities performed by teams.Comment: 44 page

Covid19: unless one gets everyone to act, policies may be ineffective or even backfire

The diffusion of COVID19 is calling governments and public health authorities to interventions that limit new infections and contain the expected number of critical cases and deaths. Most of these measures rely on the compliance of people, who are asked to reduce their social contacts to a minimum. In this note we argue that individuals' adherence to prescriptions and reduction of social activity may not be efficacious if not implemented robustly on all social groups, especially on those characterized by intense mixing patterns. Actually, it is possible that, if those who have many contacts reduce them proportionally less than those who have few, then the effect of a policy could backfire: the disease would take more time to die out, up to the point that it could become endemic. In a nutshell, unless one gets everyone to act, and specifically those who have more contacts, a policy may even be counterproductive

Adolescents’ Opinions on COVID-19 Vaccine Hesitancy: Hints toward Enhancing Pandemic Preparedness in the Future

To understand and assess vaccine reluctance, it is necessary to evaluate people’s perceptions and grasp potential reasons for generic apprehension. In our analysis, we focus on adolescents’ impressions towards anti-vaxxer behavior. The aim of the study is to figure out students’ opinions about vaccine reluctance, connecting possible explanations that motivate anti-vaxxer decisions with common specific personality traits. We further investigate people’s forecasts concerning the evolution of the pandemic. Between 2021 and 2022, we conducted a randomized survey experiment on a sample of high school individuals (N=395 ) living in different Italian regions. At that time, the vaccination campaign had already been promoted for nearly one year. From the analysis, it emerges that vaccinated people (92%), especially males, tend to be more pessimistic and attribute a higher level of generic distrust in science to anti-vaxxers. The results show that family background (mother’s education) represents the most influential regressor: individuals coming from less educated families are less prone to attribute generic distrust and distrust of vaccines as principal reasons for vaccine reluctance. Similarly, those who rarely use social media develop a minor tendency to believe in a generic pessimism of anti-vaxxers. However, concerning the future of the pandemic, they are less likely to be optimistic toward vaccines. Overall, our findings shed light on adolescents’ perceptions regarding the factors that influence vaccine hesitancy and highlight the need for targeted communication strategies to improve vaccination rates

Endogenous Diffusion in Social Networks. Two Cases: Infectious Diseases and Sharing of Knowledge

Complex phenomena arising from the interaction of ``elemental'' pieces have been first studied in physics and biology, where such constitutive particles were given deterministic rules for their behavior. In that context it was already clear that even critical outcomes can result on the aggregate level in situations where agents' behaviors are ``mechanic'' and ``simple''. In recent years, inspired by real-world phenomena, economics and other social sciences have also started to play a role in this very wide strand of research. On the one hand, by introducing degrees of rationality in agents' behaviors and, on the other hand, by allowing heterogeneity in their interactions and responses to endogenous and exogenous stimuli. This kind of reasoning has proven itself of particular success when applied in the context of social networks. Research on such intrinsically complex objects blossomed naturally within the realm of sociology, however it was only with the advent of the Internet, with the availability of large databases and the application of mathematical techniques from statistical physics that the field has really started its golden period of prosperity. In this dissertation we contribute to this strand of literature by focusing on diffusive mechanisms that naturally emerge in the context of social networks. The first example is provided by the contagion of diseases channeled through social contacts, with possible straightforward applications to the cases of diffusion of opinions or of bad habits. The second example under study is that of knowledge diffusion (sharing?), which is not only typical of the academic world but also of innovation-seeking environments, such as that of research-and-development firms, where a collaboration network is constituted by the individuals. A common feature of these cases is the fact that economic agents can endogenously and dynamically adapt by changing their (local) network of contacts or their response. In both examples, though, the impact of a single agent's action can reverberate through the whole system via its contacts (and its contacts' contacts, and so on). In the context of social networks, then, it becomes particularly challenging to understand how local features (behaviors or inclinations) may propagate, amplify or dissolute when embedded in the whole environment. One crucial difference with other approaches lies exactly in the fact that ``local'' neighborhoods can indeed be very different from one another and, moreover, very different from the global situation, which is the outcome at an aggregate level. This dissertation is structured as follows. The first chapter describes a model of diffusion of a disease between two different locations, where the agents are able to respond and adapt to this menace. A peculiarity of our model is the possibility of agents of deciding where (i.e. with whom) to interact, in the attempt of avoiding contagion while still obtaining the benefits coming from the interactions with other healthy agents. The analytical results show that such individual-level behaviors have crucially different outcomes depending on the ``world'' these agents are living in: in particular, the two globally different systems considered (one, ``globalized'', where connections between the locations are allowed and the other, ``autarkic'', where they are forbidden) exhibit crucially different resistance to exogenous shocks in the infection rate. Further research in this field is still needed, as this model is one of the few attempts in the economics literature at trying to embed rational and responsive agents in a dynamical model of diffusion on networks. Applications to systemic risk and systemic resistance can benefit from this kind of research as well as analyses of mechanisms where is prevalent the interplay between local versus global forces. The second chapter deals with a classic dilemma in the economics and business literature, that of exploration versus exploitation, and links it to the achievement of results, i.e. to the notion of performance. Specifically, we follow individual scientists throughout their careers and use their co-authorship and citation networks to map their ``knowledge space'', in order to measure their propensity to explore, both in terms of new topics and of new collaborations. Econometric results shows that the relationship between exploration and performance tends to exhibit an inverted-U shape, hence supporting the theory that a ``sweet'' spot where performance is maximized might exist, at least at an individual level. Further research on this topic is still necessary, for example to understand in depth the relationship existing (if any) between forms of ``social exploration'' (i.e. exploration in terms of collaborations and social contacts) and ``scientific exploration'' (i.e. in terms of changes of the subjects studied or fields of expertise). Moreover, the results and techniques developed here can not only be directly applied to bibliometrics studies, but can also be fundamental to give the right incentives (and, possibly, funding) to encourage long-term innovation-seeking behaviors. The third chapter tackles the same research question, but from a different viewpoint: what is the outcome of that analysis when the production units are ``aggregated'' at the level of (departments of) universities? At this aggregate level, it turns out that, in contrast to what seen in the previous chapter, a U-shaped curve characterizes the relationship between performance and exploration. Moreover, this relationship is also complicated by the effects of resources and size of each university. This complication can be seen as evidence of how, at this level, the interplay between economies of scale and economies of scope can generate an overall complex behavior. In this case too, then, the individual-level and the aggregate-level analysis exhibit once again very different outcomes: this underlines even more the complexity that comes out from the interactions in systems composed by different layers and levels

A Note on Matricial Ways to Compute Burt’s Structural Holes

In this note, I derive simple formulas based on the adjacency matrix of a network to compute measures associated with Ronald S. Burt’s structural holes (effective size, redundancy, local constraint, and constraint), together with the measure called improved structural holes introduced in 2017. This can help to see these measures within a unified computation framework because they can all be expressed in matricial form. These formulas can also be used to define naïve algorithms based on matrix operations for their computation. Such naïve algorithms can be used for small- and medium-sized networks, where exploiting the sparsity of the matrices and efficient triangle listing techniques are not necessary

La percezione del rischio al tempo dell’Infodemia: La risposta dei cittadini alle misure di contenimento

Le misure di contenimento per l’epidemia di Coronavirus sono accettate e seguite in maniera differente dai cittadini. Mostriamo il perché ciò avviene con l’ausilio di un semplice modello di diffusione di percezioni ed opinioni in una rete sociale stilizzata. Infine, mostriamo che i dati del Ministero dell’Interno confermano che l’adeguarsi alle nuove normative e policy avviene, ma necessita di tempo