Preferences, actions and voting rules

In this paper we address several issues related to collective dichotomous decision-making by means of quaternary voting rules, i.e., when voters may choose between four actions: voting yes, voting no, abstaining and not turning up-which are aggregated by a voting rule into a dichotomous decision: acceptance or rejection of a proposal. In particular we study the links between the actions and preferences of the actors. We show that quaternary rules (unlike binary rules, where only two actions -yes or no- are possible) leave room for "manipulability" (i.e., strategic behaviour). Thus a preference profile does not in general determine an action profile. We also deal with the notions of success and decisiveness and their ex ante assessment for quaternary voting rules, and discuss the role of information and coordination in this context.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Gain Scheduling Control of an Islanded Microgrid Voltage

The aim of this research study has been to design a gain scheduling (GS) digital controller in order to control the voltage of an islanded microgrid in the presence of fast varying loads (FVLs), and to compare it to a robust controller. The inverter which feeds the microgrid is connected to it through an inductance-capacitor-inductance (LCL) filter. The oscillatory and nonlinear behaviour of the plant is analyzed in the whole operating zone. Afterwards, the design of the controllers which contain two loops in cascade are described. The first loop concerns the current control, while the second is linked to the voltage regulation. Two controllers, one defined as Robust and another one as GS controller, are designed for the two loops, emphasizing in their robustness and their ability to damp the oscillatory plant behaviour. To finish, some simulations are carried out to study and compare the two kinds of controllers in different operating points. The results show that both controllers damp the oscillatory behaviour of the plant in closed loop (CL), and that the GS controller ensures a better rejection of current disturbances from FVLs.

Impact of Cyberprogram 2.0 on Different Types of School Violence and Aggressiveness

Some antibullying interventions have shown positive outcomes with regard to reducing violence. The aim of the study was to experimentally assess the effects on school violence and aggressiveness of a program to prevent and reduce cyberbullying. The sample was comprised of a randomly selected sample of 176 adolescents (93 experimental, 83 control), aged 13-15 years. The study used a repeated measures pre-posttest design with a control group. Before and after the program, two assessment instruments were administered: the "Cuestionario de Violencia Escolar-Revisado" (CUVE-R [School Violence Questionnaire- Revised]; Alvarez Garcia et al., 2011) and the "Cuestionario de agresividad premeditada e impulsiva" (CAPI-A [Premeditated and Impulsive Aggressiveness Questionnaire]; Andreu, 2010). The intervention consisted of 19 one-hour sessions carried out during the school term. The program contains 25 activities with the following objectives: (1) to identify and conceptualize bullying/cyberbullying; (2) to analyze the consequences of bullying/cyberbullying, promoting participants' capacity to report such actions when they are discovered; (3) to develop coping strategies to prevent and reduce bullying/cyberbullying; and (4) to achieve other transversal goals, such as developing positive variables (empathy, active listening, social skills, constructive conflict resolution, etc.). The pre-posttest ANCOVAs confirmed that the program stimulated a decrease in: (1) diverse types of school violence teachers' violence toward students (ridiculing or publicly humiliating students in front of the class, etc.); students' physical violence (fights, blows, shoves... aimed at the victim, or at his or her property, etc.); students' verbal violence (using offensive language, cruel, embarrassing, or insulting words... toward classmates and teachers); social exclusion (rejection or exclusion of a person or group, etc.), and violence through Information and Communication Technologies (ICT; violent behaviors by means of electronic instruments such as mobile phones and the Internet); and (2) premeditated and impulsive aggressiveness. Pre-posttest MANCOVA revealed differences between conditions with a medium effect size. This work contributes an efficacious intervention tool for the prevention and reduction of peer violence. The conclusions drawn from this study have interesting implications for educational and clinical intervention.