Contributions in Merlin

Insightful visualization of multidimensional scalar fields, in particular parameter spaces, is key to many computational science and engineering disciplines. We propose a principal component-based approach to visualize such fields that accurately reflects their sensitivity to their input parameters. The method performs dimensionality reduction on the space formed by all possible partial functions (i.e., those defined by fixing one or more input parameters to specific values), which are projected to low-dimensional parameterized manifolds such as 3D curves, surfaces, and ensembles thereof. Our mapping provides a direct geometrical and visual interpretation in terms of Sobol’s celebrated method for variance-based sensitivity analysis. We furthermore contribute a practical realization of the proposed method by means of tensor decomposition, which enables accurate yet interactive integration and multilinear principal component analysis of high-dimensional models.

Digitization increases business opportunities and the risk of companies being victims of devastating cyberattacks. Therefore, managing risk exposure and cybersecurity strategies is essential for digitized companies that aim to survive in competitive markets. However, understanding company-specific risks and quantifying their associated costs is not trivial. Current approaches fail to approximate the individualized financial impact of cyber incidents with a monetary estimation. Additionally, due to limited resources and technical expertise, SMEs, but also large companies, struggle to quantify their cyberattack exposure. Therefore, novel approaches must be built to contribute to a better understanding of the financial loss associated with cyberattacks. This article introduces the Real Cyber Value at Risk (RCVaR), an economical approach for estimating cybersecurity costs using real-world information from public cybersecurity reports. RCVaR identifies the most significant cyber risk factors from various sources and combines their quantitative results to estimate specific cyberattack costs for companies. Furthermore, RCVaR extends current methods to achieve cost and risk estimations based on historical real-world data instead of only probability-based simulations. The evaluation of the approach on unseen data shows the high accuracy and efficiency of the RCVaR in predicting and managing cyber risks. Thus, we argue that the RCVaR is a valuable addition to cybersecurity planning and risk management processes.

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.

Conference calls provide opportunities for CEOs to inform market participants (i.e., financial analysts and investors) about their companies’ prospects. Much research has focused on how CEOs speak about business-related topics in these calls, yet surprisingly the literature has not considered how statements that go beyond financial information affect market participants. When we explored archival data of how CEOs of publicly traded U.S.-based companies from the Russell 3000 Index spoke about COVID-19 in conference calls as the pandemic began in 2020, we noticed that about half of CEOs made human care statements that expressed a concern for people, with seemingly little direct financial relevance. However, although these statements were largely generic, vague expressions rather than clear plans, we discovered that the more such statements CEOs made, the better their companies fared on the stock market when stock prices tumbled globally. Follow-up explorations unveiled a negative association between CEO human care statements and stock volatility, meaning that market participants discounted these companies’ future earnings less. Our explorations suggest that it pays off for CEOs to go beyond mere financial information and show some humanity, with implications for downstream theorizing about CEO impression management.

A random variable is difference-form decomposable (DFD) if it may be written as the difference of two i.i.d. random terms. We show that densities of such variables exhibit a remarkable degree of structure. Specifically, a DFD density can be neither approximately uniform, nor quasiconvex, nor strictly concave. On the other hand, a DFD density need, in general, be neither unimodal nor logconcave. Regarding smoothness, we show that a compactly supported DFD density cannot be analytic and will often exhibit a kink even if its components are smooth. The analysis highlights the risks for model consistency resulting from the strategy widely adopted in the economics literature of imposing assumptions directly on a difference of noise terms rather than on its components.

This study addresses a class of linear mixed-integer programming (MILP) problems that involve uncertainty in the objective function parameters. The parameters are assumed to form a random vector, whose probability distribution can only be observed through a finite training data set. Unlike most of the related studies in the literature, we also consider uncertainty in the underlying data set. The data uncertainty is described by a set of linear constraints for each random sample, and the uncertainty in the distribution (for a fixed realization of data) is defined using a type-1 Wasserstein ball centered at the empirical distribution of the data. The overall problem is formulated as a three-level distributionally robust optimization (DRO) problem. First, we prove that the three-level problem admits a single-level MILP reformulation, if the class of loss functions is restricted to biaffine functions. Secondly, it turns out that for several particular forms of data uncertainty, the outlined problem can be solved reasonably fast by leveraging the nominal MILP problem. Finally, we conduct a computational study, where the out-of-sample performance of our model and computational complexity of the proposed MILP reformulation are explored numerically for several application domains.

We study how banks use "regulatory adjustments" to inflate their regulatory capital ratios and whether this depends on forbearance on the part of national authorities. Using the 2011 EBA capital exercise as a quasi-natural experiment, we find that banks substantially inflated their levels of regulatory capital via a reduction in regulatory adjustments — without a commensurate increase in book equity and without a reduction in bank risk. We document substantial heterogeneity in regulatory capital inflation across countries, suggesting that national authorities forbear their domestic banks to meet supranational requirements, with a focus on short-term economic considerations.

This paper discusses how digitization and the associated emergence of distribution platforms have affected product discovery, as well as new opportunities, in the content industries. First, we describe the traditional ways in which content creators reached consumers, as well as how platforms have transformed the product discovery process. Second, we present the promise and challenges of the information aggregation role that platforms perform, with discussions of both the positive effects of platform‐collected product ratings, as well as the prevalence and implications of misleading information. Third, we describe the promise and challenges arising from platform curation and product recommendations, with a focus on the measurements of platform power as well as possible biases in platform product recommendations. Finally, we discuss how platforms may affect which sorts of products are produced in the first place.

Decentralized Finance (‘DeFi’) has gained tremendous momentum over the past three years by using novel approaches to disintermediating financial institutions in the provision of financial services. However, empirical research in this field is still rare, and a more comprehensive understanding of the domain is a missing component in academic research. This paper develops a taxonomy based on a comprehensive literature analysis to structure this emerging field systematically. The taxonomy includes three perspectives (strategy, organization, technology) and seven dimensions (blockchain, value proposition, token type, business process, price mechanism, protocol type, integration type) as well as thirty-six characteristics. The application of the taxonomy to 278 DeFi start-ups reveals that most of the DeFi start-ups focus on Ethereum (36.3%) and have a focus on analytics and automation (52%), while, surprisingly only a few incorporate decentralized governance approaches (3.3%), provide decentralized exchanges (14%) or integrate off-chain data.