The diffusion of Covid-19 has called governments and public health authorities to interventions aiming at limiting new infections and containing 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 have reduced them proportionally less than those who have few, then the effect of a policy could have backfired: the disease has taken more time to die out, up to the point that it has become endemic. In a nutshell, unless one gets everyone to act, and specifically those who have more contacts, a policy may even be counterproductive.

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Motivated by data on co-authorships 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.

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.

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 $$12\%$$ 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.

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