Luisa Stracqualursi

Predicting the evolution of a constrained network: a beta regression model

Social network analysis allows to map and measure relationships and flows (links) between people, groups, computers, URLs, or other connected knowledge entities (nodes). In this context, a relevant issue is the treatment of constrained scale-free networks such as the network of student transfers between degree courses offered by an University, that are strongly influenced by a number of institutional decisions. In the analysis of such a system, special attention has to be paid to identify current or future “critical points”, that is nodes characterized by a high number of outcoming or incoming links, on which to act in order to optimize the network. To predict the evolution of a constrained system over time in dependence of constraint modifications, a beta regression model is proposed, that fits links represented by quantities varying between 0 and 1. The algorithm was successfully applied to the network of student transfers within the University of Bologna: the link was defined by the out-transfer rate of the degree course (computed as the ratio of the number of out-transfers to the number of students enrolled) and the critical points of the system were defined by the courses characterized by a high out-transfer rate.

A Propensity Score Matching Method to Study the Achievement of Students in Upper Secondary Schools

In the paper, we investigate the effects of family characteristics on the achievement of students in the first year of the upper secondary schools of the province of Bologna. In particular, we focus our attention on the number of siblings as potential causal factor influencing the outcome. We employ a matching strategy based on propensity score to create treatment groups, corresponding to the values of the factor under study, with the same distribution of observed covariates. As a result, students are stratified in blocks according to the propensity score to obtain estimates of the average treatment effect using nearest neighbour matching. In order to further compare the achievements of students of upper secondary schools in the city of Bologna with those in the other towns of the province, we show that valid inference is assured by controlling for family characteristics whose influence on the outcome has been previously assessed.

Sequential Combining of Expert Information Using Mathematica

In every real-world domain where reasoning under uncertainty is required, combining information from different sources (‘experts’) can be really a powerful tool to enhance accuracy and precision of the ‘final’ estimate of the unknown quantity. Bayesian paradigm offers a coherent perspective which can be used to address the problem, but an issue strictly related to information combining is how to perform an efficient process of sequential consulting: at each stage, the investigator can select the ‘best’ expert to be consulted and choose whether to stop or continue the consulting. The aim of this paper is to rephrase the Bayesian combining algorithm in a sequential context and use Mathematica to implement suitable selecting and stopping rules.

The Role of Classification Trees and Expert Knowledge in Building Bayesian Networks: A Case Study in Medicine

In clinical research an early and prompt detection of the risk class of a new patient may really play a crucial role in determining the effectiveness of the treatment and, consequently, achieving a satisfying prognosis of the patients chances. There exists a number of popular rule-based algorithms for classification, whose performances are very attractive whenever data of large number of patients are available. However, when datasets only include data of a few hundred patients, the most common approaches give unstable results and developing effective decision-support systems become scientifically challenging. Since rules can be derived from different models as well as expert knowledge resources, each of them having its advantages and weaknesses, this article suggests a “hybrid” approach to address the classification problem when the number of patients is too small to effectively use a single technique only. The hybrid strategy was applied to a case study and its predictive performance was compared with performances of each single approach: due to the seriousness of a misclassification of high-risk patients, special attention was paid on the specificity. The results show that the hybrid strategy outperforms each single strategy involved.

A Forecast Model to Assess the Critical Points in University Systems

The network of student transfers within the system of the University of Bologna adapts a constraint scale-free topology. Despite the presence of “hubs,” their role is strongly influenced by different institutional decisions and choices applied to courses. Therefore, the macro model of this network is not useful for previewing its evolution over time, particularly in the creation of critical points, which are courses with high out transfer rates. The idea is to introduce a probability of transfer function for each course, in order to preview the creation of critical points. The proposed model is fundamentally a binary regression logistic model. This rough model allows us to identify the possible creation of critical points in our complex system, these being “escape” courses, and to preview the impact of institutional decisions. At the same time, it may suggest how to remove the existing critical points in order to optimize the academic courses on offer.