Testing prospect theory in students’ performance

This paper tests the existence of ‘reference dependence’ and ‘loss aversion’ in students’ academic performance. Accordingly, achieving a worse than expected academic performance would have a much stronger effect on students’ (dis)satisfaction than obtaining a better than expected grade. Although loss aversion is a well-established finding, some authors have demonstrated that it can be moderated – diminished, to be precise–. Within this line of research, we also examine whether the students’ emotional response (satisfaction/dissatisfaction) to their performance can be moderated by different musical stimuli. We design an experiment through which we test loss aversion in students’ performance with three conditions: ‘classical music’, ‘heavy music’ and ‘no music’. The empirical application supports the reference-dependence and loss aversion hypotheses (significant at p < 0.05), and the musical stimuli do have an influence on the students’ state of satisfaction with the grades (at p < 0.05). Analyzing students’ perceptions is vital to find the way they process information. Particularly, knowing the elements that can favour not only the academic performance of students but also their attitude towards certain results is fundamental. This study demonstrates that musical stimuli can modify the perceptions of a certain academic result: the effects of ‘positive’ and ‘negative’ surprises are higher or lower, not only in function of the size of these surprises, but also according to the musical stimulus received.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.

Ideological Complex Systems: Mathematical Theory

The goal of this article is to build an abstract mathematical theory rather than a computational one of the process of transmission of ideology. The basis of much of the argument is Patten's Environment Theory that characterizes a system with its double environment (input or stimulus and output or response) and the existing interactions among them. Ideological processes are semiotic processes, and if in Patten's theory, the two environments are physical, in this theory ideological processes are physical and semiotic, as are stimulus and response.