Contributions in Merlin

What is a good workday for a software developer? What is a typical workday? We seek to answer these two questions to learn how to make good days typical. Concretely, answering these questions will help to optimize development processes and select tools that increase job satisfaction and productivity. Our work adds to a large body of research on how software developers spend their time. We report the results from 5971 responses of professional developers at Microsoft, who reflected about what made their workdays good and typical, and self-reported about how they spent their time on various activities at work. We developed conceptual frameworks to help define and characterize developer workdays from two new perspectives: good and typical. Our analysis confirms some findings in previous work, including the fact that developers actually spend little time on development and developers' aversion for meetings and interruptions. It also discovered new findings, such as that only 1.7% of survey responses mentioned emails as a reason for a bad workday, and that meetings and interruptions are only unproductive during development phases; during phases of planning, specification and release, they are common and constructive. One key finding is the importance of agency, developers' control over their workday and whether it goes as planned or is disrupted by external factors. We present actionable recommendations for researchers and managers to prioritize process and tool improvements that make good workdays typical. For instance, in light of our finding on the importance of agency, we recommend that, where possible, managers empower developers to choose their tools and tasks.

To prevent the spread of an infection, an organization obeys social distancing restrictions and thus limits the number of its members physically present on a given day. We study rotation schemes in which mutually exclusive groups are active on different days. The frequency of rotation affects risk over the duration of diffusion prior to the time the organization is able to react to the infection. If this reaction time is speedy, then such risk is undesirable because prevalence is initially convex in time. In this case, frequent rotation acts as insurance against exposure-time risk and is optimal. Infrequent rotation becomes optimal if the organization reacts slowly. Cross-mixing of the rotating subpopulations is detrimental because it increases contacts between sick and healthy individuals. However, the effect of mixing is small if the terminal prevalence is low in the absence of mixing.

The European labor market allows for the border-free mobility of workers across 31 countries that cover most of the continent’s population. However, rates of migration across European countries remain considerably lower than interstate migration in the United States, and spatial variation in terms of unemployment or income levels is larger. We document patterns of migration in Europe, which include a sizable migration from east to west in the last twenty years. An analysis of worker-level microdata provides some evidence for an international convergence in wage rates and for modest static gains from migration. We conclude by discussing obstacles to migration that reduce the potential for further labor market integration in Europe.

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

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Verbraucher unterschätzen häufig die Folgen ihrer Entscheidungen – nicht nur beim Zinseszins. Wie Banken die Erkenntnisse der Verhaltensökonomie strategisch für sich nutzen können, erklärt Wirtschaftsprofessor Thorsten Hens im Gespräch mit FinanzBusiness.

Most existing personalization systems promote items that match a user’s previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on random walks with erasure is able to generate more ideologically diverse recommenda- tions. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

How does the complexity of the world around us affect the reliability of our opinions? Motivated by this question, we quantitatively study an opinion formation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust. We show numerically and analytically that the observer’s resulting opinions are highly inconsistent (they tend to be independent of the observer’s initial opinions) and unstable (they exhibit wide stochastic variations). Opinion inconsistency and instability increase with the world’s complexity, intended as the number of subjects and their interactions. This increase can be prevented by suitably expanding the observer’s initial amount of information. Our findings imply that an individual who initially trusts a few credible information sources may end up trusting the deceptive ones even if only a small number of trust relations exist between the credible and deceptive sources.

With the increasing importance of online information environments, researchers have started investigating direct measures of online media use, such as online tracking. Most existing studies using tracking data have so far relied on commercial solutions, but these have limitations in terms of their costliness, replicability, and applicability to certain research questions. Hence, different research groups are developing their own tracking solutions for academic purposes. In this paper, we provide a critical review and classification of the existing approaches, apt to guide research decisions on the appropriate tracking approach and tool. First, we develop criteria to distinguish different user-centric desktop and mobile tracking approaches and tools (types of information, technical complexity, privacy implementation, user experience, and availability). Second, we describe different tools and approaches – separately for desktop and mobile tracking – with concrete examples and evaluate them using the aforementioned criteria. Finally, we discuss how different mobile and desktop tracking solutions can complement each other and provide recommendations for future research.

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