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.

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.

A big part of software developers’ time is spent finding answers to their coding-task-related questions. To answer their questions, developers usually perform web searches, ask questions on Q&A websites, or, more recently, in chat communities. Yet, many of these questions have frequently already been answered in previous chat conversations or other online communities. Automatically identifying and then suggesting these previous answers to the askers could, thus, save time and effort. In an empirical analysis, we first explored the frequency of repeating questions on the Discord chat platform and assessed our approach to identify them automatically. The approach was then evaluated with real-world developers in a field experiment, through which we received 142 ratings on the helpfulness of the suggestions we provided to help answer 277 questions that developers posted in four Discord communities. We further collected qualitative feedback through 53 surveys and 10 follow-up interviews. We found that the suggestions were considered helpful in 40% of the cases, that suggesting Stack Overflow posts is more often considered helpful than past Discord conversations, and that developers have difficulties describing their problems as search queries and, thus, prefer describing them as natural language questions in online communities.

Reduced water availability poses risks for many economic activities. This paper studies how water risks affect hydroelectricity generation in Europe and the US and whether these risks are priced in by financial markets. To this end, we build a novel dataset for the period 2015–2022, which combines plant-specific hydroelectricity generation with geo-specific water physical risks and equity returns. We find that water risks, measured using model-based aggregate water risk metrics as well as precipitation anomalies, are significantly associated with reduced electricity generation, although the effect disap- pears after two months. We then link the power plants in our sample to the equity returns of their owners to investigate whether financial markets adequately price water risks. Using a portfolio sorts approach, we find weak evidence of a negative risk pre- mium. Given the real negative effect of water risks on generation, we conclude that the lack of a positive risk premium amounts to mispricing of water risks by financial markets.

Scientific and technological progress is largely driven by firms in many domains, including artificial intelligence and vaccine development. The early identification of the future performance of innovation players is a relevant goal for policymakers and practitioners. In this work, we investigate how the future trajectory of a firm can be predicted by the economic or technological value of its early patents. By inspecting the patenting life cycles of 7440 publicly listed firms, we find that the economic value of a firm’s early patents is an accurate predictor of various dimensions of a firm’s future research success. At the same time, a smaller set of future top-performers do not generate early patents of high economic value, but they are detectable via the technological value of their early patents. Importantly, the observed heterogeneity of the firms’ temporal success patterns markedly differs from the patterns previously observed for individuals’ research careers.

How financial investors may react to policy events related to sustainability and climate change mitigation in particular, is a key question with implications for sustainable finance and financial stability. We address this question by carrying out a multi-period difference-in-difference approach on a confidential database of securities holdings of the European Central Bank, and we provide evidence of several effects related to the Paris Agreement. In aggregate, investors reduced their participation in the equities of high-carbon firms in response to the agreement, and the trend reverted after the US’s announcement of withdrawal from the agreement. However, the reaction varies across categories and geographies of the securities holders, their ownership size, and the emissions of owned firms. In particular, transition risk has been taken up by less regulated financial institutions and the BRIC countries. Our results highlight that the redirection of global financial flows towards climate action requires clear and unanimous signals from the global community of policy makers.

In this cover paper, we introduce a Special Issue (SI) published after the fourth edition of a series of financial stability conferences organized by Bank of Mexico, CEMLA, Bank of Canada, Zurich University and the Journal of Financial Stability in November 2021. Before providing our perspective on why the research papers included into the SI are of great relevance, we give a brief and personal overview of recent directions in financial stability research in general, esp., related to topics accentuated by the COVID-19 pandemic or post-pandemic economic and financial conditions and their complexity. Papers published in the SI cover four topics of research in the financial stability field, featuring some outstanding and innovative projects presented during the conference. The first topic is on interconnectedness and shock transmission in the financial system, diving deep into asset fire sales, interconnectedness of various segments of the financial system, in addition to banks, on the optimality of systemic risk capital buffers, and on how risks are priced in the interbank market network. The second one touches upon climate change risks looking at investors’ reactions to international climate policy developments, in particular on the Paris Agreement front and how to jointly model physical and transition risk in the banking system, including the important concept of double materiality. The third topic is represented by projects focused on policy analysis for systemic risk mitigation, specifically dealing with macroprudential policy instruments and crisis mitigation policies. Finally, research papers in the last topic on big data and market data focus on the innovative ways to explore the growing body of data sources, such as data collected by regulators, including credit register data, supervisory data and market data on financial transactions, to better understand sources and implications of systemic risk.

Identifying the rank of species in a complex ecosystem is a difficult task, since the rank of each species invariably depends on the interactions stipulated with other species through the adjacency matrix of the network. A common ranking method in economic and ecological networks is to sort the nodes such that the layout of the reordered adjacency matrix looks maximally nested with all nonzero entries packed in the upper left corner, called Nestedness Maximization Problem (NMP). Here we solve this problem by defining a suitable cost-energy function for the NMP which reveals the equivalence between the NMP and the Quadratic Assignment Problem, one of the most important combinatorial optimization problems, and use statistical physics techniques to derive a set of self-consistent equationswhose fixed point represents the optimal nodes’ rankings in an arbitrary bipartite mutualistic network. Concurrently, we present an efficient algorithm to solve the NMP that outperforms state-ofthe- art network-based metrics and genetic algorithms. Eventually, our theoretical framework may be easily generalized to study the relationship between ranking and network structure beyond pairwise interactions, e.g. in higher-order networks.

We show that engagement on environmental, social, and governance issues can benefit shareholders by reducing firms’ downside risks. We find that the risk reductions (measured using value at risk and lower partial moments) vary across engagement types and success rates. Engagement is most effective in lowering downside risk when addressing environmental topics (primarily climate change). Further, targets with large downside risk reductions exhibit a decrease in environmental incidents after the engagement. We estimate that the value at risk of engagement targets decreases by 9% of the standard deviation after successful engagements, relative to control firms.

Temperament is claimed to be the basis for personality; therefore, discovering the temperamental underpinnings of borderline personality disorder and its facets is crucial for understanding this personality disorder. In this article, we explore these underpinnings by using a new model of temperament, based on the Regulative Theory of Temperament, the Big Two of temperament, and the Circumplex of Personality Metatraits. Two studies were conducted on adults—the first was in a general population sample (N = 315) and the second was in a clinical sample (N = 113) in people with a diagnosis of borderline personality disorder. The following measurements were used: The Screening Instrument for Borderline Personality Disorder (SI‐Bord), the Five‐Factor Borderline Inventory‐Short Form (FFBI‐SF), and the Temperament Metadimensions Questionnaire (TMQ). General borderline was explained by Reactivity (high Sensitivity) and Activity (high Dynamism). At the facet level, the Borderline Internalizing Facet was mainly explained by Reactivity (high Sensitivity), while the Borderline Externalizing Facet was explained by Activity (high Dynamism) in addition to Reactivity (high Sensitivity). The results of our study revealed specific temperamental underpinnings of borderline and its facets. Reactivity underlies all borderline facets, while Activity differentiates between the Borderline Externalizing Facet and Borderline Internalizing Facet.