Contributions in Merlin

Search engines serve as information gatekeepers on a multitude of topics that are prone to gender, ethnicity, and race misrepresentations. In this paper, we specifically look at the image search representation of migrant population groups that are often subjected to discrimination and biased representation in mainstream media, increasingly so with the rise of right-wing populist actors in the Western countries. Using multiple (n = 200) virtual agents to simulate human browsing behavior in a controlled environment, we collect image search results related to various terms referring to migrants (e.g., expats, immigrants, and refugees, seven queries in English and German used in total) from the six most popular search engines. Then, with the aid of manual coding, we investigate which features are used to represent these groups and whether the representations are subjected to bias. Our findings indicate that search engines reproduce ethnic and gender biases common for mainstream media representations of different subgroups of migrant population. For instance, migrant representations tend to be highly racialized, and female migrants as well as migrants at work tend to be underrepresented in the results. Our findings highlight the need for further algorithmic impact auditing studies in the context of representation of potentially vulnerable groups in web search results.

Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Data and material: https://doi.org/10.5281/zenodo.5848794

Exploiting a quasi-experimental field setting, we examine whether people are outcome biased when self-evaluating their past decisions. Using data from Swiss driving license exams, we find that candidates who narrowly passed the theoretical driving exam are significantly less likely to pass the subsequent practical driving exam – which is taken several months after the theoretical exam – than those who narrowly failed. Those candidates who passed the theoretical exam on their first attempt receive more objections regarding their momentary, on-the-spot decisions in the practical exam, consistent with the idea that the underlying behavioral difference is worse preparation.

Delegation is an important part of organizational success and can be used to overcome personal shortcomings and draw upon the expertise and abilities of others. However, delegation comes with risks and uncertainties, as it entails a transfer of power and loss of control. Indeed, research has documented that people tend to under-delegate to other humans, often leading to poor decisions and ultimately negative economic consequences. Today, however, people are faced with a new delegation choice: Artificial Intelligence (AI). Fueled by Big Data, AI is rapidly becoming more intelligent and frequently outperforming human forecasters and decision-makers. Given this evolution of computational autonomy, researchers need to revisit the hows and whys of decision delegation and clarify not only whether people are willing to cede control to AI agents but also whether AI can reduce the under-delegation that is especially pronounced when people are faced with decisions that spur a high desire for control. By linking research on decision delegation, social risk, and control premium to the emerging field of trust in AI, we propose and find that people prefer to delegate decisions to AI as compared to human agents, especially when decisions entail losses (Studies 1–3). Results further illuminate the underlying psychological process involved (Study 1 and 2) and show that process transparency increases delegation to humans but not to AI (Study 3). These findings have important implications for research on trust in AI and the applicability of autonomous AI systems for managers and decision makers.

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This paper analyses the correlation between electoral results of green parties and corporate social responsibility (CSR) efforts of a predetermined set of banks. Using sustainability- and annual reports of banks over the years 2010 to 2020 the CSR efforts are quantified. Election data is, if possible, collected from official institutions, considering the same time period. Otherwise, different sources are consulted to obtain the required information. To reach a conclusion regarding the correlation, descriptive statistics are applied to relate the banks’ CSR efforts and the green parties’ electoral performance. This paper provides evidence that the efforts banks take with regards to CSR are independent of the performance of green parties. The result suggests that the political advancement of green parties does not have a direct effect on the banks’ commitment to CSR-related topics.

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In this study, I examine the validity of the small-cap premium (i.e., the size effect) from 2000 to 2021, using the Swiss Performance Index (SPI) to represent the Swiss stock market. I sort SPI stocks in terms of size (market capitalisation) and value (book-to-market ratio). Then, I construct six portfolios based on these categories to analyse the size effect in relation to performance, as well as statistically. I find that the small market capitalisation portfolio and low bookto-market ratio portfolio returns significantly outperform the benchmark (SPI) over the 21-year period, with this effect amplified during the COVID-19 pandemic. However, I find no significant statistical size effect across the six portfolios.

Central bank purchases under QE programs have the unintended consequence of making some collateral securities more scarce in the market. Such scarcity effects can usually be found in the bilateral repo market, where special repo rates are traded, but prolonged and excessive central bank asset purchases coupled with high a demand for safe assets can also cause a decrease in general collateral repo rates, which in turn reflects the scarcity of the whole class of government bonds. In this dissertation, I study scarcity effects of US Treasuries by investigating the Treasury GCF repo rate. Each $1 trillion purchase of Treasuries by the Fed is associated with a 40-46 basis point decline in the Treasury general collateral repo rate. This suggests moderate general Treasury collateral scarcity effects of quantitative easing in the US.

Multi-Asset Barrier Reverse Convertible (MBRC) are one of the most sold structured products in the world, especially in Switzerland where 200 billion CHF of assets are being sold per year. Some structured products can be evaluated with a close-end solution, which does not exist for MBRCs. There exist multiple methods to price MBRC and this thesis will focus on the implementation of the pricing algorithm of MBRC suggested by Lindauer and Seiz (2008). In this thesis also ceteris paribus effects of influencing parameters are measured and described. It is observed that volatility, maturity, type of the product and a barrier level have a big impact on the final price, whereas the dividend yield and risk-free rate are influencing each other in an unknown way.