Economic Theory最新文献

We propose a notion of core for school choice (i.e., priority-based allocation) problems. We say that a coalition of students is able to enforce a subassignment among them if, given their priorities at schools, other students together cannot exclude any student in the coalition from her assigned school. An assignment is in the core if no coalition of students can enforce any subassignment among them that Pareto improves their assignments. We show that all assignments in the core are Pareto efficient, and the assignment produced by Kesten’s efficiency-adjusted deferred acceptance algorithm is always in the core. We also propose a natural refinement of the core and show that it is equivalent to the concept of priority-efficiency proposed by Reny (Am Econ Rev 112:2025–2043, 2022).

In this manuscript, we study a model of human capital accumulation during the spread of disease following an agent-based approach, where agents behave maximising their intertemporal utility. We assume that the agent interaction is of mean field type, yielding a mean field game description of the problem. We discuss how the analysis of a model including both the mechanism of change of species from one epidemiological state to the other and an optimisation problem for each agent leads to an aggregate behaviour that is not easy to describe, and that sometimes exhibits structural issues. Therefore we eventually propose and study numerically a SEIRD model in which the rate of infection depends on the distribution of the population, given exogenously as the solution to the mean field game system arising as the macroscopic description of the discrete multi-agent economic model for the accumulation of human capital. Such a model arises in fact as a simplified but tractable version of the initial one.

We consider optimal group testing of individuals with heterogeneous risks for an infectious disease. Our algorithm significantly reduces the number of tests needed compared to Dorfman (Ann Math Stat 14(4):436-440, 1943). When both low-risk and high-risk samples have sufficiently low infection probabilities, it is optimal to form heterogeneous groups with exactly one high-risk sample per group. Otherwise, it is not optimal to form heterogeneous groups, but homogeneous group testing may still be optimal. For a range of parameters including the U.S. Covid-19 positivity rate for many weeks during the pandemic, the optimal size of a group test is four. We discuss the implications of our results for team design and task assignment.

The reconstruction of microbial genome sequences by bioinformatic pipelines and the consequent functional annotation of their genes' repertoire are fundamental activities aiming at unveiling their biological mechanisms, such as metabolism, virulence factors, and antimicrobial resistances. Here, we describe the development of the MEGAnnotator2 pipeline able to manage all next-generation sequencing methodologies producing short- and long-read DNA sequences. Starting from raw sequencing data, the updated pipeline can manage multiple analyses leading to the assembly of high-quality genome sequences and the functional classification of their genetic repertoire, providing the user with a useful report constituting features and statistics related to the microbial genome. The updated pipeline is fully automated from the installation to the delivery of the output, thus requiring minimal bioinformatics knowledge to be executed.

A large number of recent studies consider a compartmental SIR model to study optimal control policies aimed at containing the diffusion of COVID-19 while minimizing the economic costs of preventive measures. Such problems are non-convex and standard results need not to hold. We use a Dynamic Programming approach and prove some continuity properties of the value function of the associated optimization problem. We study the corresponding Hamilton-Jacobi-Bellman equation and show that the value function solves it in the viscosity sense. Finally, we discuss some optimality conditions. Our paper represents a first contribution towards a complete analysis of non-convex dynamic optimization problems, within a Dynamic Programming approach.