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Previous research suggests that the presence of other gamblers in a gambling venue intensifies individual gambling behavior. To study such potential social facilitation among gamblers, we conduct a large-scale analysis of more than one million gambling sessions using player tracking data of the Norwegian gambling operator Norsk Tipping. In particular, we empirically assess the existence and strength of this facilitation, and how it manifests in differently utilized gambling venues. In our study, we control for (i) each individual’s co-gamblers (frequent vs. occasional co-gamblers) and for (ii) the magnitude of individual’s social participation (social seeking vs. social avoiding gamblers). We find that gamblers stake more money and play longer sessions the more crowded the venues get and that social avoiding gamblers gamble more when they play with their most-frequent co-gambler. Interestingly, our results strongly indicate that social avoiding gamblers are more susceptible to social facilitation than gamblers who are familiar with crowded gambling venues. Overall, our research is the first large-scale study of social facilitation among gamblers introducing a novel frame-work to empirically measure this effect. We believe that our work will have important practical implications for both gambling behavior researchers as well as the gambling industry in designing and evaluating responsible gambling tools.

Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved One of the major challenges in the field of ambulant stress detection lies in the model validation. Commonly, different types of questionnaires are used to record perceived stress levels. These only capture stress levels at discrete moments in time and are prone to subjective inaccuracies. Although, many studies have already reported such issues, a solution for these difficulties is still lacking. This paper explores the potential of unsupervised learning with Self-Organizing Maps (SOM) for stress detection. In unsupervised learning settings, the labels from perceived stress levels are not needed anymore. First, a controlled stress experiment was conducted during which relax and stress phases were alternated. The skin conductance (SC) and electrocardiogram (ECG) of test subjects were recorded. Then, the structure of the SOM was built based on a training set of SC and ECG features. A Gaussian Mixture Model was used to cluster regions of the SOM with similar characteristics. Finally, by comparison of features values within each cluster, two clusters could be associated to either relax phases or stress phases. A classification performance of 79.0% (±5.16) was reached with a sensitivity of 75.6% (±11.2). In the future, the goal is to transfer these first initial results from a controlled laboratory setting to an ambulant environment. no issn ispartof: pages:26-35 ispartof: Proc. of Biosignals 2018 vol:4 pages:26-35 ispartof: BIOSIGNALS 2017 location:Funchal, Madeira date:Jan - Jan 2018 status: published