Contributions in Merlin

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We test the realization effect, i.e., that risk-taking is greater after paper outcomes than after realized outcomes, using gambling data from a real casino. During a particular casino visit, customers likely perceive that gains and losses are paper outcomes, whereas leaving the casino realizes the final account balance. Using individual-level slot machine gambling records, we find that risk-taking after both paper losses and paper gains increases within a visit and that this effect is more pronounced for larger outcomes. Conversely, realized losses from earlier visits significantly decrease risk-taking if losses are comparatively large, whereas comparatively small realized losses and realized gains do not alter risk-taking. These results provide important field validation of the realization effect in an environment with positively skewed lotteries.

Von Mitte April bis Mitte Mai wurde die Credit Suisse in der Schweiz durchschnittlich mehr als 15’500-mal online gesucht. Im gleichen Zeitraum kam die Konkurrentin UBS auf nur etwas mehr als 3’700 Suchanfragen. Hinter der CS auf dem 2. Platz rangiert die PostFinance mit etwa 8’500 Search-Anfragen. Die digitale Markt-Analyse wurde von ArgYou.com in Baar exklusiv im Auftrag der Schweizer Finanzplatz-Plattform theonliner.ch durchgeführt. Sie beinhaltete alle relevanten Suchmaschinen, Social-Media-Plattformen und E-Shops. Generell haben die Banken mit den meisten Suchanfragen ein grösseres digitales Potenzial. Die validierten und zuverlässigen Resultate der Onliner-Marktanalyse zeigen, wie viele Nutzerinnen und Nutzer sich potenziell neu für eine Bank interessieren. Anders als Kundinnen und Kunden, welche bereits feste Links zu ihrer Bank verwenden. Thorsten Hens, Professor of Financial Economics an der Universität Zürich, vermutet, dass der Spitzenplatz der Credit Suisse auf eine Kombination der Grösse und Häufigkeit von Skandalen (Greensill, Archegos, etc.) zurückzuführen ist. CS, PostFinance und UBS verfügen alle drei über einen sehr hohen Bekanntheitsgrad. Nach Bilanzsumme 2020 wird die Top3 von der zweitgrössten (CS), der fünftgrössten (PostFinance) und der grössten (UBS) Schweizer Bank gebildet. Abgesehen von Raiffeisen und den Kantonalbanken haben diese Finanzdienstleister auch einen hohen Marktanteil im Retail Bereich.

Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.

In this paper, we examine if a slight behavioural modification has an influence on discrimination. Specifically, we use a field experiment based on emails to observe if persistence increases the chances of joining a social group. We find that native- or foreign-sounding names are equally successful when being persistent. However, non-persistent individuals with foreign-sounding names suffer from discrimination. The results show that persistent individuals can be especially interesting in the context of field experiments.

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The pandemic has been a wake-up call for a lot of people, causing us to reevaluate our lives and our careers. It’s natural to think: “If I’m going to spend so much time at work, I might as well do something I’m passionate about.” But there are also benefits to thinking about it differently: Instead of pursuing a career based on your passion, how can you career be a conduit to your passion? Pursuing a passion outside of work can be less risky. And some research suggests transforming hobbies into work can actually undermine your enjoyment of these activities. Instead, look for a job that will give you the resources - time, money, and energy - to pursue your passion. If time is your scarcest resource, look for a job that offers schedule flexibility so that you can structure your work around your passions. If money is the issue, look for a job that allows you to pay for the life you want to lead. When it comes to energy, don’t think of your passion as something that provides fuel to energize you for work. Instead, look at your job as giving you the security and income to pursue your passion.

This study aims to analyse the tenure of head coaches in college women’s soccer in the USA and puts the focus on the influence of race and gender. The analysis includes individual characteristics of coaches (educational and professional background), team performance, institutional characteristics, and geographical differences (at the state level) from 1977 until 2015. The main results show that African American coaches and women have a significantly shorter tenure in these college institutions. Other factors related to the background of coaches and the characteristics of institutions also play a significant role. The findings are relevant for coaches from minority groups and athletic departments, who are interested in equity in sport. Future research may use an alternative approach to investigate the causes of tenure disparity in college women’s soccer.

There have been many works on interpreting Transformer, the state-of-the-art model architecture in natural language processing (NLP). Recent researches reveal that the embedding space of Transformer models is highly anisotropic, i.e., the embeddings occupy only a narrow cone. Previous works (Mu et al., 2017; Liu et al., 2019) show that improving the isotropy of static embeddings (e.g., Word2Vec or GloVe) improves their performance on down-stream tasks. Based on this, different studies propose calibration methods to address the anisotropic problem of the contextualized embeddings of Transformers. However, Cai et al. (2021) show that the embedding space has many clusters, and these clusters are locally isotropic. Luo (2020) reports that embedding vectors of some Transformer models have ‘spikes’ at consistent indices, and this distorts our understanding of the embedding space. Overall, we believe that additional isotropy calibration does not help in improving the performance of Transformers. To better understand Transformers, we conduct an empirical evaluation and find that in most cases, calibration improves the isotropy of the model but decreases the scores on down-stream tasks. In other words, better isotropy does not provide consistent improvements across models and tasks. Reference Jiaqi Mu, Suma Bhat, and Pramod Viswanath. All-but-the-top: Simple and effective postprocessing for word representations. arXiv preprint arXiv:1702.01417, 2017. Tianlin Liu, Lyle Ungar, and Joao Sedoc. Unsupervised post-processing of word vectors via conceptor negation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6778–6785, 2019a. Xingyu Cai, Jiaji Huang, Yuchen Bian, and Kenneth Church. Isotropy in the contextual embedding space: Clusters and manifolds. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=xYGNO86OWDH. Ziyang Luo. Catch the" tails" of bert. arXiv preprint arXiv:2011.04393, 2020.