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University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules. [ABSTRACT FROM AUTHOR]

The paper introduces structural diversity as a new approach to quantify the complexity of technologies. By modeling technologies as combinatorial networks, a measure of technological complexity is derived that represents the diversity of (sub-)network topologies in these networks. It is further argued that this measure can be empirically approximated with the Network Diversity Score (NDS). The paper also presents an application of this approach to European patent data from 1980 to 2015. On this basis, the measure of structural diversity is shown to replicate a number of stylized facts commonly associated with technological complexity: Complexity increases over time and younger technologies are more complex than older technologies. Complex technologies are also associated to larger R&D efforts and require more collaborative R&D activities. Lastly, when controlling for technologies’ size, technologies scoring high on structural diversity are also shown to concentrate in space. [ABSTRACT FROM AUTHOR]

The rapid development and diffusion of new technologies such as automation and artificial intelligence makes life more convenient. At the same time, people may develop overdependence on technology to simplify everyday tasks or to reduce the level of effort required to accomplish them. We conduct a two-phase real-effort laboratory experiment to assess how external assistance affects subsequent revealed preferences for the convenience of a lower level of effort versus monetary rewards requiring greater effort. The results suggest that men treated with external help in the first phase tend to choose more difficult options with potentially higher monetary rewards. In contrast, after being treated with external help, women exhibit a stronger propensity to utilize the convenience of an easier task and are less likely to choose a more difficult option that carries higher potential earnings. [ABSTRACT FROM AUTHOR]