Contributions in Merlin

Research suggests that firms with family CEOs differ from other types of businesses, yet surprisingly little is known about how employees in these firms feel and behave compared to those working in other firms. We draw from family science and management research to suggest that family CEOs, because of their emotion-evoking double role as family members and business leaders, are, on average, more likely to infuse employees with positive emotions, such as enthusiasm and excitement, than hired professional CEOs. We suggest that these emotions spread through firms by way of emotional contagion during interactions with employees, thereby setting the organizational affective tone. In turn, we hypothesize that in firms with family CEOs the voluntary turnover rate is lower. In considering structural features as boundary conditions, we propose that family CEOs have stronger effects in smaller and centralized firms, and weaker effects in formalized firms. Multilevel data from 41,200 employees and 2,246 direct reports of CEOs from 497 firms with and without family CEOs provide support for our model. This research suggests that firms managed by family CEOs, despite often being criticized as nepotistic relics of the past, tend to offer pleasant work environments.

We consider the design of contests when the principal can choose both the prize profile and how the prizes are allocated as a function of a possibly noisy signal about the agents' efforts. We provide sufficient conditions that guarantee optimality of a contest. Optimal contests have a minimally competitive prize profile and an intermediate degree of competitiveness in the contest success function. Whenever observation is not too noisy, the optimum can be achieved by an all-pay contest with a cap. When observation is perfect, the optimum can also be achieved by a nested Tullock contest. We relate our results to a recent literature which has asked similar questions but has typically focused on the design of either the prize profile or the contest success function.

The Creator Economy faces concerning levels of unfairness. Content creators (CCs) publicly accuse platforms of purposefully reducing the visibility of their content based on protected attributes, while platforms place the blame on viewer biases. Meanwhile, prior work warns about the “rich-get-richer” effect perpetuated by existing popularity biases in recommender systems: Any initial advantage in visibility will likely be exacerbated over time. What remains unclear is how the biases based on protected attributes from platforms and viewers interact and contribute to the observed inequality in the context of popularity-biased recommender systems. The difficulty of the question lies in the complexity and opacity of the system. To overcome this challenge, we design a simple agent-based model (ABM) that unifies the platform systems which allocate the visibility of CCs (e.g., recommender systems, moderation) into a single popularity-based function, which we call the visibility allocation system (VAS). Through simulations, we find that although viewer homophilic biases do alone create inequalities, small levels of additional biases in VAS are more harmful. From the perspective of interventions, our results suggest that (a) attempts to reduce attribute-biases in moderation and recommendations should precede those reducing viewers’ homophilic tendencies, (b) decreasing the popularity-biases in VAS decreases but not eliminates inequalities, (c) boosting the visibility of protected CCs to overcome viewers’ homophily with respect to one fairness metric is unlikely to produce fair outcomes with respect to all metrics, and (d) the process is also unfair for viewers and this unfairness could be overcome through the same interventions. More generally, this work demonstrates the potential of using ABMs to better understand the causes and effects of biases and interventions within complex sociotechnical systems.

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Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is violated. The causes of endogeneity are manifold and include response bias in surveys, omission of important explanatory variables, or simultaneity between explanatory and response variables. Instrumental variable estimation provides a possible solution. However, valid and strong external instruments are difficult to find. Consequently, internal instrumental variable approaches have been proposed to correct for endogeneity without relying on external instruments. The R package REndo implements various internal instrumental variable approaches, i.e., latent instrumental variables estimation (Ebbes, Wedel, Boeckenholt, and Steerneman 2005), higher moments estimation (Lewbel 1997), heteroscedastic error estimation (Lewbel 2012), joint estimation using copula (Park and Gupta 2012) and multilevel generalized method of moments estimation (Kim and Frees 2007). Package usage is illustrated on simulated and real-world data.

Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions.

This thesis aims to analyze the impact of occlusion on the quality of semantic segmentation methods for point cloud data. Occlusion is a prevalent phenomenon in 3D scenes, where objects often overlap or obstruct each other. This can significantly compromise the quality and integrity of data, leading to inaccuracies in semantic segmentation. While the issue of occlusion has garnered attention in 3D data processing, current research on how different occlusion levels impact the quality of semantic segmentation is rare. Specifically, there is a palpable gap in understanding how to quantify occlusion in the scene and how this characteristic influence the performance of advanced semantic segmentation software like the Minkowski Engine. To bridge the research gap, we proposed a novel metric to quantify the occlusion level of a scene. We then applied this metric to analyze the impact of occlusion on the quality of semantic segmentation methods for point cloud data. Our results show that the occlusion level of a scene has limited impact to the quality of semantic segmentation.

The rise of Blockchain technology and cryptocurrencies has enabled the creation of decentralized alternative payment systems, without the need for third party financial institutions. However, this decentralization and pseudo-anonymity have also facilitated the emergence of darknet markets (DNMs) offering illicit products. This study introduces the"grayscale diffusion framework", with the aim of modeling and understanding the propagation of dark assets in the Bitcoin network. Formulated and implemented with a combination of on-chain and off-chain data, this approach utilizes address clustering, haircut tainting, and community partitioning to offer a unique analysis perspective on dark asset proliferation across the Bitcoin blockchain. The framework uncovers interesting patterns in the assortative nature of Bitcoin transactions based on the darkness level of assets, pointing to the existence of non-random clusters and communities that facilitate dark asset diffusion. Our research not only addresses key questions related to the effective modeling and tracking of tainted asset flow, but also provides valuable insights. Keywords: Bitcoin, Address Clustering, Darknet Marketplaces, Gray-scale Diffusion, User Transaction Networks.

In recent years, small and midsize enterprises (SMEs) have become increasingly reliant on technology, but lag in terms of investment into cybersecurity. This renders them vulnerable to malware attacks, which are increasingly targeting companies rather than individuals, with great economic impact. This project proposes and implements a prototype tool, which allows for machine learning models to be trained, stored, and tested within the SecBox sandbox environment. Both classification and anomaly detection models are implemented through Scikit-learn, in order to provide predictions about known malware types (binary and multiclass classification), as well as detecting the presence of unseen malware in real-time during the SecBox execution. The models are trained using the system call and resource usage file execution logs available from the SecBox, which are transformed into suitable formats using frequency-based and sequence-based data preprocessing. Model reproducibility is ensured by generating configuration files with references to the random seeds, the datasets used in training, as well as other model parameters, which can be used to re-train the same model. To evaluate and compare model performance, each model type is tested in a realistic scenario of the execution of Monti ransomware within the SecBox, creating a confusion matrix as well as calculating the accuracy, precision, recall and F1-score metrics based on the model predictions. The system call classifier models are shown to have the best performance when classifying Monti malware samples, and the project is concluded by specifying several relevant research areas to be investigated further.