Contributions in Merlin

In the past decade, research on measuring and assessing the environmental impact of software has gained significant momentum in science and industry. However, due to the large number of research groups, measurement setups, procedure models, tools, and general novelty of the research area, a comprehensive research framework has yet to be created. The literature documents several approaches from researchers and practitioners who have developed individual methods and models, along with more general ideas like the integration of software sustainability in the context of the UN Sustainable Development Goals, or science communication approaches to make the resource cost of software transparent to society. However, a reference measurement model for the energy and resource consumption of software is still missing. In this article, we jointly develop the Green Software Measurement Model (GSMM), in which we bring together the core ideas of the measurement models, setups, and methods of over 10 research groups in four countries who have done pioneering work in assessing the environmental impact of software. We briefly describe the different methods and models used by these research groups, derive the components of the GSMM from them, and then we discuss and evaluate the resulting reference model. By categorizing the existing measurement models and procedures and by providing guidelines for assimilating and tailoring existing methods, we expect this work to aid new researchers and practitioners who want to conduct measurements for their individual use cases.

Return event studies generally involve several firms but there are also cases when only one firm is involved. This makes the relevant testing problems, abnormal return and cumulative abnormal return, more difficult since one cannot exploit the multitude of firms (by using a relevant central limit theorem, say) to design hypothesis tests. We propose a permutation test which is of nonparametric nature and more generally valid than the tests that have previously been proposed in the literature in this context. We address the question of the power of the test via a brief simulation study and also illustrate the method with two applications to real data.

This article examines the impact of non-pharmaceutical interventions on the initial exponential growth of the infected population and the final exponential decay of the infected population. We employ a Bayesian dynamic model to test whether there is learning, a random walk pattern, or another type of learning with evolving epidemiological data over time across 168 countries and 41,706 country-date observations. Although we show that Bayesian learning is not taking place, most policy measures appear to assert some effect. In particular, we show that economic policy variables are of importance for the main epidemiological parameters derived from the policy learning model. In an empirical second-stage application, we further investigate the underlying dynamics between the epidemiological parameters and household debt repayments, a key economic variable, in the UK. Results show no Bayesian learning, although a higher transmission rate would increase household debt repayments, while the recovery rate would have a negative impact. Therefore, suboptimal learning is taking place.

From deciding which meal to prepare for our guests to trading-off the pro-environmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision makers facing conflicting preferences of others should choose the option that maximizes the sum of subjective value (utility) of the entire group. This notion requires comparing intensities of preferences across individuals. However, it remains unclear whether such comparisons are possible at all, and (if they are possible) how they might be implemented in the brain. Here, we show that female and male participants can both learn the preferences of others by observing their choices, and represent these preferences on a common scale to make utilitarian welfare decisions. On the neural level, multivariate support vector regressions revealed that a distributed activity pattern in the ventromedial prefrontal cortex (VMPFC), a brain region previously associated with reward processing, represented preference strength of others. Strikingly, also the utilitarian welfare of others was represented in the VMPFC and relied on the same neural code as the estimated preferences of others. Together, our findings reveal that humans can behave as if they maximized utilitarian welfare using a specific utility representation and that the brain enables such choices by repurposing neural machinery processing the reward others receive.Significance statementIn many situations politicians and civilians strive to maximize the welfare of social groups. If the preferences of group members are in conflict, identifying the utilitarian welfare-maximizing option requires that decision makers can compare the strengths of conflicting preferences on a common scale. Yet, there is a fundamental lack of understanding which brain mechanisms enable such comparisons of conflicting utilities. Here, we show that brain regions involved in reward processing compute welfare comparisons by representing the preferences of others with a common neural code. This provides a neurobiological mechanism to compute utilitarian welfare maximization as desired by moral philosophy in the Humean tradition.

In light of recent growth in privacy awareness and data ownership rights, differential privacy (DP) has emerged as a promising technique employed by several well-known data controller entities. This raises the question of how casual users, as the immediate recipients of privacy threats and risks, comprehend and perceive DP and its key parameter ε, as DP's provided protection depends on it. Existing studies show that ordinary users have the potential to understand the fundamental mechanism of DP and its implications for the privacy-utility trade-off when they are communicated clearly through textual and visual aids and, accordingly, make informed decisions about sharing their data under DP protection. However, these attempts either only implicitly mention a few possible values for ε, such as low, medium, and high, or altogether leave it out of the communication. In this paper, we conduct a between-subject user study (N=426) to investigate the effectiveness of nine interactive visual tools to communicate ε explicitly and on a continuous scale in a data-sharing scenario related to publishing positive COVID-19 test results. These interactive visual tools allow casual users to visualize DP's effects on data accuracy and/or privacy loss for various ε values. We found that visualizations incorporating the privacy loss component have a significant impact on assisting users in selecting values that are closer to the recommended values by experts. However, depending on the ratio between DP noise and underlying data, the accuracy loss component disparately affects users' ε decision; the bigger the relative error, the bigger the selected epsilon and vice versa. Thus, accuracy portrayals should be carried out with care. We contextualize our findings in the existing literature and conclude with insights and recommendations on effectively employing our findings to communicate DP to casual users.

Applications of drones in emergency response, like firefighting, have been promoted in the past decade. As the autonomy of drones continues to improve, the ways in which they are integrated into firefighting teams and their impact on crews are changing. This demands more understanding of how firefighters perceive and interact with autonomous drones. This paper presents a drone-based system for emergency operations with which firefighters can interact through sound, lights, and a graphical user interface. We use interviews with stakeholders collected in two field trials to explore their perceptions of the interaction and collaboration with drones. Our result shows that firefighters perceived visual interaction as adequate. However, for audio instructions and interfaces, information overload emerges as an essential problem. The potential impact of drones on current work configurations may involve shifting the position of humans closer to supervisory decision-makers and changing the training structure and content.

To call attention to and motivate action on ethical issues in business or society, messengers often criticize groups for wrongdoing and ask these groups to change their behavior. When criticizing target groups, messengers frequently identify and express concern about harm caused to a victim group, and in the process address a target group by criticizing them for causing this harm and imploring them to change. However, we find that when messengers criticize a target group for causing harm to a victim group in this way—expressing singular concern for the victim group—members of the target group infer, often incorrectly, that the messenger views the target group as less moral and unworthy of concern. This inferred lack of moral concern reduces criticism acceptance and prompts backlash from the target group. To address this problem, we introduce dual concern messaging—messages that simultaneously communicate that a target group causes harm to a victim group and express concern for the target group. A series of several experiments demonstrate that dual concern messages reduce inferences that a critical messenger lacks moral concern for the criticized target group, increase the persuasiveness of the criticism among members of the target group, and reduce backlash from consumers against a corporate messenger. When pursuing justice for victims of a target group, dual concern messages that communicate concern for the victim group as well as the target group are more effective in fostering openness toward criticism, rather than defensiveness, in a target group, thus setting the stage for change.

We contribute to the literature on statistical robustness of risk measures by computing the index of qualitative robustness for risk measures based on utility functions. This problem is intimately related to finding the natural domain of finiteness and continuity of such risk measures.

Search engines serve as information gatekeepers on a multitude of topics dealing with different aspects of society. However, the ways search engines filter and rank information are prone to biases related to gender, ethnicity, and race. In this article, we conduct a systematic algorithm audit to examine how one specific form of bias, namely, sexualization, is manifested in Google’s text search results about different national and gender groups. We find evidence of the sexualization of women, particularly those from the Global South and East, in search outputs in both organic and sponsored search results. Our findings contribute to research on the sexualization of people in different forms of media, bias in web search, and algorithm auditing as well as have important implications for the ongoing debates about the responsibility of transnational tech companies for preventing systems they design from amplifying discrimination.

The COVID-19 pandemic demonstrated the fragility of international, national, regional, and local risk management systems. It revealed an urgent need to improve risk planning, preparedness, and communication strategies. In parallel, it created an opportunity to drastically re-think and transform societal processes and policies to prevent future shocks originating not only from health, but also combined with those related to climate change and biodiversity loss. In this perspective, we examine how to improve integrated risk assessment and management (IRAM) capacities to address interconnected shocks. We present the results from a series of workshops within the framework of the University of Zurich and University of Geneva. Initiative "Shaping Resilient Societies: A Multi-Stakeholder Approach to Create a Responsive Society". This initiative gathered experts from multiple disciplines to discuss their perspectives on resilience; here we present the key messages of the "Pandemics, Climate and Sustainability” thinking group. We identify a roadmap and selected research areas concerning the improvement of IRAM analysis capacities, practices, policies. We recommend the development of robust data systems and science-policy advice systems to address combined shocks emerging from health, biodiversity loss and climate change. We posit that further developing the IRAM framework to include these recommendations will improve societal preparedness and response capacity and will provide more empirical evidence supporting decision-making and the selection of strategies and measures for integrated risk reduction.