Case-Based Reasoning (CBR) is a symbolic Artificial Intelligence (AI) approach that has been successfully applied across various domains, including medical diagnosis, product configuration, and customer support, to solve problems based on experiential knowledge and analogy. A key aspect of CBR is its problem-solving procedure, where new solutions are created by referencing similar experiences, which makes CBR explainable and effective even with small amounts of data. However, one of the most significant challenges in CBR lies in defining and computing meaningful similarities between new and past problems, which heavily relies on domain-specific knowledge. This knowledge, typically only available through human experts, must be manually acquired, leading to what is commonly known as the knowledge-acquisition bottleneck. One way to mitigate the knowledge-acquisition bottleneck is through a hybrid approach that combines the symbolic reasoning strengths of CBR with the learning capabilities of Deep Learning (DL), a sub-symbolic AI method. DL, which utilizes deep neural networks, has gained immense popularity due to its ability to automatically learn from raw data to solve complex AI problems such as object detection, question answering, and machine translation. While DL minimizes manual knowledge acquisition by automatically training models from data, it comes with its own limitations, such as requiring large datasets, and being difficult to explain, often functioning as a "black box". By bringing together the symbolic nature of CBR and the data-driven learning abilities of DL, a neuro-symbolic, hybrid AI approach can potentially overcome the limitations of both methods, resulting in systems that are both explainable and capable of learning from data. The focus of this thesis is on integrating DL into the core task of similarity assessment within CBR, specifically in the domain of process management. Processes are fundamental to numerous industries and sectors, with process management techniques, particularly Business Process Management (BPM), being widely applied to optimize organizational workflows. Process-Oriented Case-Based Reasoning (POCBR) extends traditional CBR to handle procedural data, enabling applications such as adaptive manufacturing, where past processes are analyzed to find alternative solutions when problems arise. However, applying CBR to process management introduces additional complexity, as procedural cases are typically represented as semantically annotated graphs, increasing the knowledge-acquisition effort for both case modeling and similarity assessment. The key contributions of this thesis are as follows: It presents a method for preparing procedural cases, represented as semantic graphs, to be used as input for neural networks. Handling such complex, structured data represents a significant challenge, particularly given the scarcity of available process data in most organizations. To overcome the issue of data scarcity, the thesis proposes data augmentation techniques to artificially expand the process datasets, enabling more effective training of DL models. Moreover, it explores several deep learning architectures and training setups for learning similarity measures between procedural cases in POCBR applications. This includes the use of experience-based Hyperparameter Optimization (HPO) methods to fine-tune the deep learning models. Additionally, the thesis addresses the computational challenges posed by graph-based similarity assessments in CBR. The traditional method of determining similarity through subgraph isomorphism checks, which compare nodes and edges across graphs, is computationally expensive. To alleviate this issue, the hybrid approach seeks to use DL models to approximate these similarity calculations more efficiently, thus reducing the computational complexity involved in graph matching. The experimental evaluations of the corresponding contributions provide consistent results that indicate the benefits of using DL-based similarity measures and case retrieval methods in POCBR applications. The comparison with existing methods, e.g., based on subgraph isomorphism, shows several advantages but also some disadvantages of the compared methods. In summary, the methods and contributions outlined in this work enable more efficient and robust applications of hybrid CBR and DL in process management applications.

The goal of this work is to compare operators that are defined on probably varying Hilbert spaces. Distance concepts for operators as well as convergence concepts for such operators are explained and examined. For distance concepts we present three main notions. All have in common that they use space-linking operators that connect the spaces. At first, we look at unitary maps and compare the unitary orbits of the operators. Then, we consider isometric embeddings, which is based on a concept of Joachim Weidmann. Then we look at contractions but with more norm equations in comparison. The latter idea is based on a concept of Olaf Post called quasi-unitary equivalence. Our main result is that the unitary and isometric distances are equal provided the operators are both self-adjoint and have 0 in their essential spectra. In the third chapter, we focus specifically on the investigation of these distance terms for compact operators or operators in p-Schatten classes. In this case, the interpretation of the spectra as null sequences allows further distance investigation. Chapter four deals mainly with convergence terms of operators on varying Hilbert spaces. The analyses in this work deal exclusively with concepts of norm resolvent convergence. The main conclusion of the chapter is that the generalisation for norm resolvent convergence of Joachim Weidmann and the generalisation of Olaf Post, called quasi-unitary equivalence, are equivalent to each other. In addition, we specify error bounds and deal with the convergence speed of both concepts. Two important implications of these convergence notions are that the approximation is spectrally exact, i.e., the spectra converge suitably, and that the convergence is transferred to the functional calculus of the bounded functions vanishing at infinity.

In this dissertation, I analyze how large players in financial markets exert influence on smaller players and how this affects the decisions of the large ones. I focus on how the large players process information in an uncertain environment, form expectations and communicate these to smaller players through their actions. I examine these relationships empirically in the foreign exchange market and in the context of a game-theoretic model of an investment project. In Chapter 2, I investigate the relationship between the foreign exchange trading activity of large US-based market participants and the volatility of the nominal spot exchange rate. Using a novel dataset, I utilize the weekly growth rate of aggregate foreign currency positions of major market participants to proxy trading activity in the foreign exchange market. By estimating the heterogeneous autoregressive model of realized volatility (HAR-RV), I find evidence of a positive relationship between trading activity and volatility, which is mainly driven by unexpected changes in trading activity and is asymmetric for some of the currencies considered. My results contribute to the understanding of the drivers of exchange rate volatility and the role of large players in the flow of information in financial markets. In Chapters 3 and 4, I consider a sequential global game of an investment project to examine how a large creditor influences the decisions of small creditors with her lending decision. I pay particular attention to the timing of the large player’s decision, i.e. whether she makes her decision to roll over a credit before or after the small players. I show that she faces a trade-off between signaling to and learning from small creditors. By being a focal point for coordination, her actions have a substantial impact on the probability of coordination failure and the failure of the investment project. I investigate the sensitivity of the equilibrium by comparing settings with perfect and imperfect learning. The results highlight the importance of signaling and provide a new perspective on the idea of catalytic finance and the influence of a lender-of-last-resort in self-fulfilling debt crises.

Introduction: Apart from a few studies with limited sample sizes, we have little data on attitudes toward lesbian and gay (LG) people in Greece. Methods: This study examines this topic in 949 heterosexual Greek participants. Based on previous research in cultural contexts other than Greece, we hypothesized that four demographics (gender, age, education, area of residence) and religious and political orientation predict a substantial amount of variance in homophobia (i.e., anti-LG attitudes). Results: We verified all observed variables except area of residence as significant predictors. Regarding the “intergroup contact hypothesis,” we distinguished the direct effects of the predictor variables from indirect effects mediated by contact with lesbians and gay men. All variables except area of residence showed a direct effect and, except for education, also an indirect effect on homophobia. The strongest effects were found for religious and political orientation, followed by gender. Highly religious, right-wing oriented, and male participants reported the highest levels of homophobia, partially mediated by their low level of contact with LG people. Discussion/Conclusion: The results confirm and further explain the detrimental role the Greek Orthodox Church, right-wing political parties, and traditional gender roles play in the acceptance of sexual minorities.

Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text. Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model. Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language. Conclusions: Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.

Background: Psychoeducation positively influences the psychological components of chronic low back pain (CLBP) in conventional treatments. The digitalization of health care has led to the discussion of virtual reality (VR) interventions. However, CLBP treatments in VR have some limitations due to full immersion. In comparison, augmented reality (AR) supplements the real world with virtual elements involving one’s own body sensory perception and can combine conventional and VR approaches. Objective: The aim of this study was to review the state of research on the treatment of CLBP through psychoeducation, including immersive technologies, and to formulate suggestions for psychoeducation in AR for CLBP. Methods: A scoping review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed in August 2024 by using Livivo ZB MED, PubMed, Web of Science, American Psychological Association PsycINFO (PsycArticle), and PsyArXiv Preprints databases. A qualitative content analysis of the included studies was conducted based on 4 deductively extracted categories. Results: We included 12 studies published between 2019 and 2024 referring to conventional and VR-based psychoeducation for CLBP treatment, but no study referred to AR. In these studies, educational programs were combined with physiotherapy, encompassing content on pain biology, psychological education, coping strategies, and relaxation techniques. The key outcomes were pain intensity, kinesiophobia, pain catastrophizing, degree of disability, quality of life, well-being, self-efficacy, depression, attrition rate, and user experience. Passive, active, and gamified strategies were used to promote intrinsic motivation from a psychological point of view. Regarding user experience from a software development perspective, user friendliness, operational support, and application challenges were recommended.

Investment theory and related theoretical approaches suggest a dynamic interplay between crystallized intelligence, fluid intelligence, and investment traits like need for cognition. Although cross-sectional studies have found positive correlations between these constructs, longitudinal research testing all of their relations over time is scarce. In our pre-registered longitudinal study, we examined whether initial levels of crystallized intelligence, fluid intelligence, and need for cognition predicted changes in each other. We analyzed data from 341 German students in grades 7–9 who were assessed twice, one year apart. Using multi-process latent change score models, we found that changes in fluid intelligence were positively predicted by prior need for cognition, and changes in need for cognition were positively predicted by prior fluid intelligence. Changes in crystallized intelligence were not significantly predicted by prior Gf, prior NFC, or their interaction, contrary to theoretical assumptions. This pattern of results was largely replicated in a model including all constructs simultaneously. Our findings support the notion that intelligence and investment traits, particularly need for cognition, positively interact during cognitive development, but this interplay was unexpectedly limited to Gf.

Job crafting is the behavior that employees engage in to create personally better fitting work environments, for example, by increasing challenging job demands. To better understand the driving forces behind employees’ engagement in job crafting, we investigated implicit and explicit power motives. While implicit motives tend to operate at the unconscious, explicit motives operate at the unconscious level. We focused on power motives, as power is an agentic motive characterized by the need to influence your environment. Although power is relevant to job crafting in its entirety, in this study, we link it to increasing challenging job demands due to its relevance to job control, which falls under the umbrella of power. Using a cross-sectional design, we collected survey data from a sample of Lebanese nurses (N = 360) working in 18 different hospitals across the country. In both implicit and explicit power motive measures, we focused on integrative power that enable people to stay calm and integrate opposition. The results showed that explicit power predicted job crafting (H1) and that implicit power amplified this effect (H2). Furthermore, job crafting mediated the relationship between congruently high power motives and positive work-related outcomes (H3) that were interrelated (H4). Our findings unravel the driving forces behind one of the most important dimensions of job crafting and extend the benefits of motive congruence to work-related outcomes.

The turnover and stabilization of organic matter (OM) in soils depend on mass and energy fluxes. Understanding the energy content of soil organic matter (SOM) is therefore of crucial importance, but this has hardly been studied so far, especially in mineral soils. In this study, combustion calorimetry (bomb calorimetry) was applied to determine the energy content (combustion enthalpy, ΔCH) of various materials: litter inputs, forest floor layers (OL, OF, OH), and bulk soil and particulate organic matter (POM) from topsoils (0–5 cm). Samples were taken from 35-year-old monocultural stands of Douglas fir (Pseudotsuga menziesii), black pine (Pinus nigra), European beech (Fagus sylvatica), and red oak (Quercus rubra) grown under highly similar soil, landscape and boundary conditions. This allowed to investigate the influence of the degree of transformation and litter quality on the ΔCH of SOM. Tree species fuel the soil C cycle with high-energy litter (38.9 ± 1.1 kJ g−1C) and fine root biomass (35.9 ± 1.1 kJ g−1C). As plant material is transformed to SOM, ΔCH decreases in the order: OL (36.8 ± 1.6 kJ g−1C) ≥ OF (35.9 ± 3.7 kJ g−1C) > OH (30.6 ± 7.0 kJ g−1C) > 0–5 cm bulk soil (22.9 ± 8.2 kJ g−1C). It indicates that the energy content of OM decreases with transformation and stabilization, as microorganisms extract energy from organic compounds for growth and maintenance, resulting in lower-energy bulk SOM. The POM fraction has 1.6-fold higher ΔCH compared to the bulk SOM. Tree species significantly affect ΔCH of SOM in the mineral soil with the lowest values under beech (12.7 ± 3.4 kJ g−1C). The energy contents corresponded to stoichiometric and isotopic parameters as proxies for the degree of transformation. In conclusion, litter quality, in terms of elemental composition and energy content, defines the pathway and degree of the energy-driven microbially mediated transformation and stabilization of SOM.

The viviparous eelpout Zoarces viviparus is a common fish across the North Atlantic and has successfully colonized habitats across environmental gradients. Due to its wide distribution and predictable phenotypic responses to pollution, Z. viviparus is used as an ideal marine bioindicator organism and has been routinely sampled over decades by several countries to monitor marine environmental health. Additionally, this species is a promising model to study adaptive processes related to environmental change, specifically global warming. Here, we report the chromosome-level genome assembly of Z. viviparus, which has a size of 663 Mb and consists of 607 scaffolds (N50 = 26 Mb). The 24 largest represent the 24 chromosomes of the haploid Z. viviparus genome, which harbors 98% of the complete Benchmarking Universal Single-Copy Orthologues defined for ray-finned fish, indicating that the assembly is highly contiguous and complete. Comparative analyses between the Z. viviparus assembly and the chromosome-level genomes of two other eelpout species revealed a high synteny, but also an accumulation of repetitive elements in the Z. viviparus genome. Our reference genome will be an important resource enabling future in-depth genomic analyses of the effects of environmental change on this important bioindicator species.

Introduction: Conventional agricultural land-use may negatively impact biodiversity and the environment due to the increased disturbances to the soil ecosystem by tillage, for example. Cultivation of the perennial grain intermediate wheatgrass (Thinopyrum intermedium, IWG, Kernza®) is a nature-based solution for sustainable agriculture, improving nutrient retention mainly through its extensive root system. Nematodes serve as sensitive bioindicators, detecting early changes in the soil food web, reflecting in changes in their community structure. Materials and Methods:IWG and annual wheat sites in South France, Belgium and South Sweden were investigated in April 2022 for two depths (5–15 cm; 25–35 cm) to evaluate the difference in nematode community structure among the cropping systems. Results: Sites with IWG cultivation held an accumulation of structure indicators (c-p 3–5 nematodes) compared to sites with annual wheat cultivation. A generalised linear mixed model revealed significantly more root feeders, especially for the subsoil, under IWG as a result of the perennial cultivation. The maturity index, plant-parasitic index, channel index and structure index were greater for IWG sites. The enrichment index was greater for annual wheat sites due to the dominance of bacterivores and enrichment indicators (c-p 1 nematodes). The nematode community structure (weighted faunal profile analysis) indicates IWG sites as being a generally undisturbed system with efficient nutrient cycling and balanced distribution of feeding types, as well as higher metabolic footprint values for root feeders (including plant-parasitic nematodes) and fungivores. Annual wheat sites, on the other hand, held indicators of a disturbed system with increased occurrence of opportunistic species and a more bacterial driven pathway. The topsoil had an increased occurrence of structure indicators in both cropping systems. Conclusion: IWG creates favourable conditions for a diverse food web, including improved nutrient cycling and a heterogeneous resource environment, regardless of climatic conditions, establishing it as a stable and resilient agricultural management system.

Introduction: This study examined the sources and factors of resilience in Russian sexual and gender minorities. We hypothesized that, through their involvement in the lesbian, gay, bisexual, and transgender (LGBT) community (source of resilience), LGBT people establish friendships that provide them with social support (factor of resilience), which in turn should contribute to their mental health. Method: The study sample consisted of 1,127 young and middle-aged LGBT adults (18 to 50 years) from Russia. We collected the data online and anonymously. Results: Partial mediation could be confirmed. LGBT people who were involved in “their” community reported more social support from friends, which partially mediated the positive association between community involvement and mental health. The mediation remained significant when we controlled for demographics and outness as potential covariates. Additional analyses showed that the present sample reported lower mental health but not less social support than Russian nonminority samples recruited in previous research. Conclusion: Our study underlines the importance of the LGBT community in times of increasing stigmatization of sexual and gender minorities.

The cold pressor test (CPT) elicits strong cardiovascular reactions via activation of the sympathetic nervous system (SNS), yielding subsequent increases in heart rate (HR) and blood pressure (BP). However, little is known on how exposure to the CPT affects cardiac ventricular repolarization. Twenty-eight healthy males underwent both a bilateral feet CPT and a warm water (WW) control condition on two separate days, one week apart. During pre-stress baseline and stress induction cardiovascular signals (ECG lead II, Finometer BP) were monitored continuously. Salivary cortisol and subjective stress ratings were assessed intermittently. Corrected QT (QTc) interval length and T-wave amplitude (TWA) were assessed for each heartbeat and subsequently aggregated individually over baseline and stress phases, respectively. CPT increases QTc interval length and elevates the TWA. Stress-induced changes in cardiac repolarization are only in part and weakly correlated with cardiovascular and cortisol stress-reactivity. Besides its already well-established effects on cardiovascular, endocrine, and subjective responses, CPT also impacts on cardiac repolarization by elongation of QTc interval length and elevation of TWA. CPT effects on cardiac repolarization share little variance with the other indices of stress reactivity, suggesting a potentially incremental value of this parameter for understanding psychobiological adaptation to acute CPT stress.

Using validated stimulus material is crucial for ensuring research comparability and replicability. However, many databases rely solely on bidimensional valence ratings, ranging from negative to positive. While this material might be appropriate for certain studies, it does not reflect the complexity of attitudes and therefore might hamper the unambiguous interpretation of some study results. In fact, most databases cannot differentiate between neutral (i.e., neither positive nor negative) and ambivalent (i.e., simultaneously positive and negative) attitudes. Consequently, even presumably univalent (only positive or negative) stimuli cannot be clearly distinguished from ambivalent ones when selected via bipolar rating scales. In the present research, we introduce the Trier Univalence Neutrality Ambivalence (TUNA) database, a database containing 304,262 validation ratings from heterogeneous samples of 3,232 participants and at least 20 (M = 27.3, SD = 4.84) ratings per self-report scale per picture for a variety of attitude objects on split semantic differential scales. As these scales measure positive and negative evaluations independently, the TUNA database allows to distinguish univalence, neutrality, and ambivalence (i.e., potential ambivalence). TUNA also goes beyond previous databases by validating the stimulus materials on affective outcomes such as experiences of conflict (i.e., felt ambivalence), arousal, anger, disgust, and empathy. The TUNA database consists of 796 pictures and is compatible with other popular databases. It sets a focus on food pictures in various forms (e.g., raw vs. cooked, non-processed vs. highly processed), but includes pictures of other objects that are typically used in research to study univalent (e.g., flowers) and ambivalent (e.g., money, cars) attitudes for comparison. Furthermore, to facilitate the stimulus selection the TUNA database has an accompanying desktop app that allows easy stimulus selection via a ultitude of filter options.

The French Enlightenment is a pivotal period in European intellectual and literary history, which can be studied through this dataset of French novels first published between 1751 and 1800. This collection contains 200 French novels in TEI/XML, encoded according to the ‘level-1 schema’ of the European Literary Text Collection (ELTeC), and carefully compiled to reflect the known historical publication of French Novels in that period regarding publication year, gender of author and narrative form. The dataset is connected to a bigger knowledge graph of 331,671 Resource Description Framework triples (RDF) built within the project ‘Mining and Modeling Text’ at Trier University, Germany (2019–2023).

Globalization significantly transforms labor markets. Advances in production technologies, transportation, and political integration reshape how and where goods and services are produced. Local economic conditions and diverse policy responses create varying speeds of change, affecting regions' attractiveness for living and working -- and promoting mobility. Competition for talent necessitates a deep understanding of why individuals choose specific destinations, how to ensure their effective labor market integration, and what workplace factors affect workers' well-being. This thesis focuses on two crucial aspects of labor market change -- Migration and workplace technological change. It contributes to our understanding of the determinants of labor mobility, the factors facilitating migrant integration, and the role of workplace automation for worker well-being. Chapter 2 investigates the relationship between minimum wages (MWs) and regional worker mobility in the EU. EU citizens are free to work anywhere in the common market, which allows them to take advantage of the significant variation in MWs across the EU. However, although MWs are set at the national level, it is also their local relevance that varies substantially -- depending on factors such as the share of affected workers or the extent to which they shift local compensation levels. These variations may attract workers from elsewhere, from within a country or from abroad. Analyzing regional variations in the Kaitz index, a measure of local MW impact, reveals that higher MWs can significantly increase inflows of low-skilled EU workers, particularly in central Europe. Chapter 3 examines the inequality in returns to skills experienced by immigrants, focusing on the role of linguistic proximity between migrants' origin and destination countries. Harmonized individual-level data from nine linguistically diverse migrant-hosting economies allows for an analysis of the wage gaps faced by immigrants from various origins, implicitly indicating how well they and their skills are integrated into the local labor markets. The analysis reveals that greater linguistic distance is associated with a higher wage penalty for highly skilled immigrants and a lower position in the wage distribution for those without tertiary education. Chapter 4 investigates an institutional factor potentially relevant for the integration of immigrants -- the labor market impact of Confucius Institutes (CIs), Chinese government-sponsored institutions that promote Chinese language and culture abroad. CIs have been found to foster trade and cultural exchange, indicating their potential relevance in shaping attitudes and trust of natives towards China and Chinese individuals. Examining the relationship between local CI presence and the wages of Chinese immigrants in local labor markets of the United States, the analysis reveals that CIs associate with significantly reduced wages for nearby residing Chinese immigrants. An event study demonstrates that the mere announcement of a new CI negatively impacts local wages for Chinese immigrants, independent of the CI's actual opening. Chapter 5 explores how working in automatable jobs affects life satisfaction in Germany. Following earlier literature, we classify occupations by potential for automation, and define the top third of occupations in this metric as \textit{automatable jobs}. We find workers in highly automatable jobs reporting a lower life satisfaction. Moreover, we detect a non-linearity, where workers in moderately automatable jobs (the second third of the distribution) experience a positive association with life satisfaction. Overall, the negative relationship of automation is most pronounced among younger and blue-collar workers, irrespective of the non-linearity.

Der Trierer Wohnungsmarkt ist in den vergangenen Jahren durch steigende Mieten gekennzeichnet. Dies führt zu Herausforderungen für die Bevölkerung, insbesondere für einkommensschwächere Haushalte. In diesem Atlas zeigen wir diese Entwicklung der Angebotsmieten auf und verdeutlichen, dass der Mietwohnungsmarkt in Teilmärkte segmentiert ist. Angesichts der Preissteigerungen über alle Segmente hinweg ist es notwendig, dass die Diskussion um bezahlbaren Wohnraum und gezielte stadtpolitische Maßnahmen stärker in den Fokus rückt, um langfristig eine soziale Stadtentwicklung Triers zu gewährleisten. Mit diesem Atlas wollen wir daher stadtpolitisch Aktiven und anderen Interessierten Materialien an die Hand geben, um die Wohnungspolitik der Stadt einordnen zu können.

This dissertation addresses the measurement and evaluation of the energy and resource efficiency of software systems. Studies show that the environmental impact of Information and Communications Technologies (ICT) is steadily increasing and is already estimated to be responsible for 3 % of the total greenhouse gas (GHG) emissions. Although it is the hardware that consumes natural resources and energy through its production, use, and disposal, software controls the hardware and therefore has a considerable influence on the used capacities. Accordingly, it should also be attributed a share of the environmental impact. To address this softwareinduced impact, the focus is on the continued development of a measurement and assessment model for energy and resource-efficient software. Furthermore, measurement and assessment methods from international research and practitioner communities were compared in order to develop a generic reference model for software resource and energy measurements. The next step was to derive a methodology and to define and operationalize criteria for evaluating and improving the environmental impact of software products. In addition, a key objective is to transfer the developed methodology and models to software systems that cause high consumption or offer optimization potential through economies of scale. These include, e. g., Cyber-Physical Systems (CPS) and mobile apps, as well as applications with high demands on computing power or data volumes, such as distributed systems and especially Artificial Intelligence (AI) systems. In particular, factors influencing the consumption of software along its life cycle are considered. These factors include the location (cloud, edge, embedded) where the computing and storage services are provided, the role of the stakeholders, application scenarios, the configuration of the systems, the used data, its representation and transmission, or the design of the software architecture. Based on existing literature and previous experiments, distinct use cases were selected that address these factors. Comparative use cases include the implementation of a scenario in different programming languages, using varying algorithms, libraries, data structures, protocols, model topologies, hardware and software setups, etc. From the selection, experimental scenarios were devised for the use cases to compare the methods to be analyzed. During their execution, the energy and resource consumption was measured, and the results were assessed. Subtracting baseline measurements of the hardware setup without the software running from the scenario measurements makes the software-induced consumption measurable and thus transparent. Comparing the scenario measurements with each other allows the identification of the more energyefficient setup for the use case and, in turn, the improvement/optimization of the system as a whole. The calculated metrics were then also structured as indicators in a criteria catalog. These indicators represent empirically determinable variables that provide information about a matter that cannot be measured directly, such as the environmental impact of the software. Together with verification criteria that must be complied with and confirmed by the producers of the software, this creates a model with which the comparability of software systems can be established. The gained knowledge from the experiments and assessments can then be used to forecast and optimize the energy and resource efficiency of software products. This enables developers, but also students, scientists and all other stakeholders involved in the life cycleof software, to continuously monitor and optimize the impact of their software on energy and resource consumption. The developed models, methods, and criteria were evaluated and validated by the scientific community at conferences and workshops. The central outcomes of this thesis, including a measurement reference model and the criteria catalog, were disseminated in academic journals. Furthermore, the transfer to society has been driven forward, e. g., through the publication of two book chapters, the development and presentation of exemplary best practices at developer conferences, collaboration with industry, and the establishment of the eco-label “Blue Angel” for resource and energy-efficient software products. In the long term, the objective is to effect a change in societal attitudes and ultimately to achieve significant resource savings through economies of scale by applying the methods in the development of software in general and AI systems in particular.

Although universality has fascinated over the last decades, there are still numerous open questions in this field that require further investigation. In this work, we will mainly focus on classes of functions whose Fourier series are universal in the sense that they allow us to approximate uniformly any continuous function defined on a suitable subset of the unit circle. The structure of this thesis is as follows. In the first chapter, we will initially introduce the most important notation which is needed for our following discussion. Subsequently, after recalling the notion of universality in a general context, we will revisit significant results concerning universality of Taylor series. The focus here is particularly on universality with respect to uniform convergence and convergence in measure. By a result of Menshov, we will transition to universality of Fourier series which is the central object of study in this work. In the second chapter, we recall spaces of holomorphic functions which are characterized by the growth of their coefficients. In this context, we will derive a relationship to functions on the unit circle via an application of the Fourier transform. In the second part of the chapter, our attention is devoted to the $\mathcal{D}_{\textup{harm}}^p$ spaces which can be viewed as the set of harmonic functions contained in the $W^{1,p}(\D)$ Sobolev spaces. In this context, we will also recall the Bergman projection. Thanks to the intensive study of the latter in relation to Sobolev spaces, we can derive a decomposition of $\mathcal{D}_{\textup{harm}}^p$ spaces which may be seen as analogous to the Riesz projection for $L^p$ spaces. Owing to this result, we are able to provide a link between $\mathcal{D}_{\textup{harm}}^p$ spaces and spaces of holomorphic functions on $\mathbb{C}_\infty \setminus \s$ which turns out to be a crucial step in determining the dual of $\mathcal{D}_{\textup{harm}}^p$ spaces. The last section of this chapter deals with the Cauchy dual which has a close connection to the Fantappié transform. As an application, we will determine the Cauchy dual of the spaces $D_\alpha$ and $D_{\textup{harm}}^p$, two results that will prove to be very helpful later on. Finally, we will provide a useful criterion that establishes a connection between the density of a set in the direct sum $X \oplus Y$ and the Cauchy dual of the intersection of the respective spaces. The subsequent chapter will delve into the theory of capacities and, consequently, potential theory which will prove to be essential in formulating our universality results. In addition to introducing further necessary terminologies, we will define capacities in the first section following [16], however in the frame of separable metric spaces, and revisit the most important results about them. Simultaneously, we make preparations that allow us to define the $\mathrm{Li}_\alpha$-capacity which will turn out to be equivalent to the classical Riesz $\alpha$-capacity. The $\mathrm{Li}_\alpha$-capacity proves to be more adapted to the $D_\alpha$ spaces. It becomes apparent in the course of our discussion that the $\mathrm{Li}_\alpha$-capacity is essential to prove uniqueness results for the class $D_\alpha$. This leads to the centerpiece of this chapter which forms the energy formula for the $\mathrm{Li}_\alpha$-capacity on the unit circle. More precisely, this identity establishes a connection between the energy of a measure and its corresponding Fourier coefficients. We will briefly deal with the complement-equivalence of capacities before we revisit the concept of Bessel and Riesz capacities, this time, however, in a much more general context, where we will mainly rely on [1]. Since we defined capacities on separable metric spaces in the first section, we can draw a connection between Bessel capacities and $\mathrm{Li}_\alpha$-capacities. To conclude this chapter, we would like to take a closer look at the geometric meaning of capacities. Here, we will point out a connection between the Hausdorff dimension and the polarity of a set, and transfer it to the $\mathrm{Li}_\alpha$-capacity. Another aspect will be the comparison of Bessel capacities across different dimensions, in which the theory of Wolff potentials crystallizes as a crucial auxiliary tool. In the fourth chapter of this thesis, we will turn our focus to the theory of sets of uniqueness, a subject within the broader field of harmonic analysis. This theory has a close relationship with sets of universality, a connection that will be further elucidated in the upcoming chapter. The initial section of this chapter will be dedicated to the notion of sets of uniqueness that is specifically adapted to our current context. Building on this concept, we will recall some of the fundamental results of this theory. In the subsequent section, we will primarily rely on techniques from previous chapters to determine the closed sets of uniqueness for the class $\mathcal{D}_{\alpha}$. The proofs we will discuss are largely influenced by [16, p.\ 178] and [9, pp.\ 82]. One more time, it will become evident that the introduction of the $\mathrm{Li}_\alpha$-capacity in the third chapter and the closely associated energy formula on the unit circle, were the pivotal factors that enabled us to carry out these proofs. In the final chapter of our discourse, we will present our results on universality. To begin, we will recall a version of the universality criterion which traces back to the work of Grosse-Erdmann (see [26]). Coupled with an outcome from the second chapter, we will prove a result that allows us to obtain the universality of a class using the technique of simultaneous approximation. This tool will play a key role in the proof of our universality results which will follow hereafter. Our attention will first be directed toward the class $D_\alpha$ with $\alpha$ in the interval $(0,1]$. Here, we summarize that universality with respect to uniform convergence occurs on closed and $\alpha$-polar sets $E \subset \s$. Thanks to results of Carleson and further considerations, which particularly rely on the favorable behavior of the $\mathrm{Li}_\alpha$-kernel, we also find that this result is sharp. In particular, it may be seen as a generalization of the universality result for the harmonic Dirichlet space. Following this, we will investigate the same class, however, this time for $\alpha \in [-1,0)$. In this case, it turns out that universality with respect to uniform convergence occurs on closed and $(-\alpha)$-complement-polar sets $E \subset \s$. In particular, these sets of universality can have positive arc measure. In the final section, we will focus on the class $D_{\textup{harm}}^p$. Here, we manage to prove that universality occurs on closed and $(1,p)$-polar sets $E \subset \s$. Through results of Twomey [68] combined with an observation by Girela and Pélaez [23], as well as the decomposition of $D_{\textup{harm}}^p$, we can deduce that the closed sets of universality with respect to uniform convergence of the class $D_{\textup{harm}}^p$ are characterized by $(1,p)$-polarity. We conclude our work with an application of the latter result to the class $D^p$. We will show that the closed sets of divergence for the class $D^p$ are given by the $(1,p)$-polar sets.

Die Masterarbeit untersucht den Zusammenhang zwischen Libertarismus und Rechtsextremismus, wobei der Fokus auf der Entwicklung der libertären Szene in Deutschland liegt. Zunächst wird ein ausführlicher theoretischer Teil präsentiert, in dem gezeigt wird, dass zwischen einer radikal wirtschaftsliberalen und einer rechtsextremen Weltauffassung partiell gemeinsame Elemente bestehen. Insbesondere werden ein spezifischer Antiegalitarismus, eine Naturalisierung gesellschaftlicher Sachverhalte sowie eine gemeinsame Feindbildkonstruktion als verbindende Merkmale identifiziert, die beide Ideologien, die auf Ungleichwertigkeitsvorstellungen basieren, prägen. Im Anschluss folgt eine empirische Analyse des libertären Magazins eigentümlich frei, das eine zentrale Rolle in der deutschsprachigen libertären Bewegung spielt. Der soziologische Neo-Institutionalismus dient als theoretische Perspektive, um den institutionellen Wandel innerhalb der libertären Szene zu erfassen und zu analysieren. Die empirische Untersuchung bestätigt die theoretischen Annahmen und zeigt, dass sich im libertären Diskurs eine zunehmende Annäherung an rechtsextreme Ideologien vollzieht. Fünf Phasen des institutionellen Wandels werden identifiziert, die mit einer verstärkten Vernetzung der libertären Bewegung mit dem rechtsextremen Spektrum und der Veränderung von Diskursen einhergehen. Die Arbeit kommt zu dem Schluss, dass die libertäre Szene um eigentlich frei dem rechtsextremen Spektrum zuzuordnen ist. Die Untersuchung schlägt vor, den Libertarismus im Rahmen dieser Entwicklung als „Paläolibertarismus“ zu bezeichnen, was auf eine ideologische Nähe zur Alt-Right-Bewegung hinweist. Zentrale Merkmale dieser Ideologie sind neben einer radikal wirtschaftsliberalen Ausrichtung auch die Forderung nach einer Privatisierung gesellschaftlicher Institutionen und die Etablierung von sozialen Autoritäten wie Familie und Kirche zum Schutz des Individuums vor staatlicher Einflussnahme.

This thesis deals with consumption-investment allocation problems with Epstein-Zin recursive utility, building upon the dualization procedure introduced by [Matoussi and Xing, 2018]. While their work exclusively focuses on truly recursive utility, we extend their procedure to include time-additive utility using results from general convex analysis. The dual problem is expressed in terms of a backward stochastic differential equation (BSDE), for which existence and uniqueness results are established. In this regard, we close a gap left open in previous works, by extending results restricted to specific subsets of parameters to cover all parameter constellations within our duality setting. Using duality theory, we analyze the utility loss of an investor with recursive preferences, that is, her difference in utility between acting suboptimally in a given market, compared to her best possible (optimal) consumption-investment behaviour. In particular, we derive universal power utility bounds, presenting a novel and tractable approximation of the investors’ optimal utility and her welfare loss associated to specific investment-consumption choices. To address quantitative shortcomings of those power utility bounds, we additionally introduce one-sided variational bounds that offer a more effective approximation for recursive utilities. The theoretical value of our power utility bounds is demonstrated through their application in a new existence and uniqueness result for the BSDE characterizing the dual problem. Moreover, we propose two approximation approaches for consumption-investment optimization problems with Epstein-Zin recursive preferences. The first approach directly formalizes the classical concept of least favorable completion, providing an analytic approximation fully characterized by a system of ordinary differential equations. In the special case of power utility, this approach can be interpreted as a variation of the well-known Campbell-Shiller approximation, improving some of its qualitative shortcomings with respect to state dependence of the resulting approximate strategies. The second approach introduces a PDE-iteration scheme, by reinterpreting artificial completion as a dynamic game, where the investor and a dual opponent interact until reaching an equilibrium that corresponds to an approximate solution of the investors optimization problem. Despite the need for additional approximations within each iteration, this scheme is shown to be quantitatively and qualitatively accurate. Moreover, it is capable of approximating high dimensional optimization problems, essentially avoiding the curse of dimensionality and providing analytical results.

In bilevel optimization, some of the variables of an optimization problem have to be an optimal solution to another nested optimization problem. This specific structure renders bilevel optimization a powerful tool for modeling hierarchical decision-making processes, which arise in various real-world applications such as in critical infrastructure defense, transportation, or energy. Due to their nested structure, however, bilevel problems are also inherently hard to solve—both in theory and in practice. Further challenges arise if, e.g., bilevel problems under uncertainty are considered. In this dissertation, we address different types of uncertainties in bilevel optimization using techniques from robust optimization. We study mixed-integer linear bilevel problems with lower-level objective uncertainty, which we tackle using the notion of Gamma-robustness. We present two exact branch-and-cut approaches to solve these Gamma-robust bilevel problems, along with cuts tailored to the important class of monotone interdiction problems. Given the overall hardness of the considered problems, we additionally propose heuristic approaches for mixed-integer, linear, and Gamma-robust bilevel problems. The latter rely on solving a linear number of deterministic bilevel problems so that no problem-specific tailoring is required. We assess the performance of both the exact and the heuristic approaches through extensive computational studies. In addition, we study the problem of determining optimal tolls in a traffic network in which the network users hedge against uncertain travel costs in a robust way. The overall toll-setting problem can be seen as a single-leader multi-follower problem with multiple robustified followers. We model this setting as a mathematical problem with equilibrium constraints, for which we present a mixed-integer, nonlinear, and nonconvex reformulation that can be tackled using state-of-the-art general-purpose solvers. We further illustrate the impact of considering robustified followers on the toll-setting policies through a case study. Finally, we highlight that the sources of uncertainty in bilevel optimization are much richer compared to single-level optimization. To this end, we study two aspects related to so-called decision uncertainty. First, we propose a strictly robust approach in which the follower hedges against erroneous observations of the leader's decision. Second, we consider an exemplary bilevel problem with a continuous but nonconvex lower level in which algorithmic necessities prevent the follower from making a globally optimal decision in an exact sense. The example illustrates that even very small deviations in the follower's decision may lead to arbitrarily large discrepancies between exact and computationally obtained bilevel solutions.

Based on data collected from two surveys conducted in Germany and Taiwan, my first paper (Chapter 2) examines the impact of culture through language priming (Chinese vs. German or English) on individuals’ price fairness perception and attitudes towards government intervention and economic policy involving inequality. We document large cross-language differences: in both surveys, subjects who were asked and answered in Chinese demonstrated significantly higher perceived price fairness in a free market mechanism than their counterparts who completed the survey in German or English language. They were also more inclined to accept a Pareto improvement policy which increases social and economic inequality. In the second survey, Chinese language induced also a lower readiness to accept government intervention in markets with price limits compared to English language. Since language functions as a cultural mindset prime, our findings imply that culture plays an important role in fairness perception and preferences regarding social and economic inequality. Chapter 3 of this work deals with patriotism priming. By conducting two online experimental studies conducted in Germany and China, we tested three different kinds of priming methods for constructive and blind patriotism respectively. Subjects were randomly distributed to one of three treatments motivated by previous studies in different countries: a constructive patriotism priming treatment, a blind patriotism priming treatment and a non-priming baseline. While the first experiment had a between-subject design, the second one enabled both a between-subject and within-subject comparison, since the level of patriotism of individuals was measured before and after priming respectively. The design of the second survey also enabled a comparison among the three priming methods for constructive and blind patriotism. The results showed that the tested methods, especially the national achievements as a priming mechanism, functioned well overall for constructive patriotism. Surprisingly, the priming for blind patriotism did not work in either Germany or China and the opposite results were observed. Discussion and implications for future studies are provided at the end of the chapter. Using data from the same studies as in Chapter 3, Chapter 4 examines the impact of patriotism on individuals’ fairness perception and preferences regarding inequality and on their attitudes toward economic policy involving inequality. Across surveys and countries, a positive and significant effect of blind patriotism on economic individualism was found. For China, we also found a significant relationship between blind patriotism and the agreement to unequal economic policy. In contrast to blind patriotism, we did not find an association of constructive patriotism to economic individualism and to attitudes toward economic policy involving inequality. Political and economic implications based on the results are discussed. The last chapter (Chapter 5) studies the self-serving bias (when an individual’s perception about fairness is biased by self-interest) in the context of price setting and profit distribution. By analyzing data from four surveys conducted in six countries, we found that the stated appropriate product price and the fair allocation of profit was significantly higher, when the outcome was favorable to oneself. This self-serving bias in price fairness perception, however, differed across countries significantly and was significantly higher in Germany, Taiwan and China than in Vietnam, Estonia and Japan. Although economic individualism and masculinity were found to have a significant negative effect on self-interest bias in price fairness judgment, they did not sufficiently explain the differences in self-interest bias between countries. Furthermore, we also observed an increase of self-interest bias in profit allocation over time in time-series data for one country (Germany) with data from 2011 to 2023. The four papers are all co-authored with Prof. Marc Oliver Rieger, and the first paper has been accepted for publications in Review of Behavioral Economics.

　

　

　

　

　

　

　

　

　

This thesis contains three parts that are all connected by their contribution to research about the effects of trading apps on investment behavior. The primary motivation for this study is to investigate the previously undetermined consequences and effects of trading apps, which are a new phenomenon in the broker market, on the investment and risk behavior of Neobroker users. Chapter 2 addresses the characteristics of a typical Neobroker user and a former Neobroker user and the impact of trading apps on the investment and risk behavior of their users. The results show that Neobroker users are significantly more risk tolerant than the general German population and are influenced by trading apps regarding their investment and risk behavior. Low trading fees and the low minimum investment amount are the main reasons for the use of trading apps. Investors who stop using trading apps mostly stop investing altogether. Another worrying result is that financial literacy among all groups is low and most Neobroker users have wrong conceptions about how trading apps earn money. In general, the financial literacy of all groups considered in this chapter is surprisingly low. The third chapter investigates the effects of trading apps on investment behavior over time and compares the investment and risk behavior of Neobroker users and general investors. By using representative data of German Neobroker users, who were surveyed repeatedly over a 8-month time interval, it becomes possible to determine causal effects of the use of trading apps over time. In total, the financial literacy of Neobroker users increases with the longer use of a trading app. A worrying result is that the risk tolerance of Neobroker users rises significantly over time. Male Neobroker users gain a higher annual return (non-risk-adjusted) than female Neobroker users. In comparison to general investors, Neobroker users are significantly younger, more risk tolerant, more likely to buy derivatives and gain a higher annual return (non-risk-adjusted). The fourth chapter analyses the impact of personality traits on the investment and risk behavior of Neobroker users. The results show that the BIG-5 personality traits have an impact on the investment behavior of Neobroker users. Two personality traits, openness and conscientiousness, stand out the most, as these two have explanatory power over various aspects of the behavior of Neobroker users. In particular, whether they buy different financial products than planned, the time they inform themselves about financial markets, the variety of financial products owned, and the reasons to use a Neobroker. Surprisingly, the risk tolerance of Neobroker users and the reasons to invest are not connected to any personal dimension. Whether a participant uses a trading app or a traditional broker to invest is respectively influenced by different personality traits.

Die Hauptzielsetzung der vorliegenden Arbeit besteht in der Erarbeitung von Möglichkeiten zur Optimierung der Bewirtschaftung der Riveristalsperre. Dazu werden zunächst alle relevanten Einflussgrößen und Gefahrenpotentiale des Systems aus dem Einzugsgebiet und der Talsperre analysiert und bewertet. Letztlich wird die Konzeption eines integrierten Bewirtschaftungsplanes für die Riveristalsperre auf der Basis einer neuen Pilotierungsanlage im SWT-Wasserwerk in Trier-Irsch dargestellt, diskutiert und auf Funktionsfähigkeit geprüft. Mit einer aus ca. 90% des Einzugsgebiets bestehenden Waldfläche ist die Hauptsperre der Riveristalsperre durchschnittlich als eindeutig oligotroph eingestuft und das Rohwasser der Riveristalsperre von ausgezeichneter Qualität mit nur wenigen und beherrschbaren Gefahrenpotentialen. Unter Berücksichtigung der Pilotierungsergebnisse war die In/Out, PES, UF- geeigneter als die Out/In, PVDF-Membran. Die Anordnung der UF-Anlage auf der Rohwasserseite nach der Flockung für die Abtrennung der partikulären Wasserinhaltsstoffe mit einer nachgeschalteten Wasseraufhärtung, pH-Wert-Anhebung und Entmanganung in einer CaCO3-Filterstufe und abschließenden Desinfektion durch eine UV-Bestrahlung stellte sich als ideal für die Aufbereitung des Rohwassers der Riveristalsperre heraus. Die Ergebnisse der Pilotanlage sind in einer großtechnischen Trinkwasseraufbereitung im Wasserwerk in Trier-Irsch umgesetzt und seit 2013 offiziell in Betrieb genommen. Abschließend werden Maßnahmen gegen eventuelle Minderwassermengen bei z.B. langanhaltenden Trockenwetterperioden (Klimawandel !) und für die allgemeine Erhöhung der Versorgungssicherheit diskutiert, wobei in Trier und in der Region schon seit langem sehr stark in die Verbundnetzsysteme investiert wird.

Wie gestaltet sich die politische Kultur in Rheinland-Pfalz aus? Vor dem Hintergrund verschiedener Prägungen der Bundesländer in Deutschland nimmt das Monitoring eine spezielle Vermessung der in Rheinland-Pfalz verbreiteten politischen Einstellungen und Haltungen vor.

The positive consequences of performance pay on the wages and productivity have been well documented in the last decades. Yet, the increased pressure and work commitment associated with performance pay suggest that performance pay may have unintended negative consequences on worker’s health and well-being. As firms increasingly use performance pay worldwide, it becomes crucial to evaluate positive and negative consequences of performance pay. Thus, Chapters 2 – 4 of this doctoral thesis investigate the unintended adverse consequences of performance pay on stress, alcohol consumption, and loneliness, respectively. Chapter 5 investigates the positive role of performance pay on mitigating the overeducation wage penalty and enhancing labor market position of overeducated workers. In Chapter 2, together with John S. Heywood and Uwe Jirjahn, I examine the hypothesis that performance pay is positively associated with employee stress. Using unique survey data from the German Socio-Economic Panel, I find performance pay consistently and importantly associates with greater stress even controlling for a long list of economic, social, and personality characteristics. The finding also holds in instrumental variable estimations accounting for the potential endogeneity of performance pay. Moreover, I show that risk tolerance and locus of control moderate the relationship between performance pay and stress. Among workers receiving performance pay, the risk tolerant and those believing they can control their environment suffer to a lesser degree from stress. Chapter 3 examines the relationship between performance pay and alcohol use. Together with John S. Heywood and Uwe Jirjahn, I examine the hypothesis that alcohol use as “self-medication” is a natural response to the stress and uncertainty associated with performance pay. Using data from the German Socio-Economic Panel, I find that the likelihood of consuming each of four types of alcohol (beer, wine, spirits, and mixed drinks) is higher for those receiving performance pay even controlling for a long list of economic, social, and personality characteristics and in sensible instrumental variable estimates. I also show that the number of types of alcohol consumed is larger for those receiving performance pay and that the intensity of consumption increases. Moreover, I find that risk tolerance and gender moderate the relationship between performance pay and alcohol use. In Chapter 4, I examine the hypothesis that performance pay increases the risk of employee loneliness due to increased stress, job commitment, and uncooperativeness associated with performance pay. Using the German Socio-Economic Panel, I find that performance pay is positively associated with both the incidence and intensity of loneliness. Correspondingly, performance pay decreases the social life satisfaction of workers. The findings also hold in instrumental variable estimations addressing the potential endogeneity of performance pay and in various robustness checks. Interestingly, investigating the potential role of moderating factors reveals that the association between performance pay and loneliness is particularly large for private sector employees. Finally, in Chapter 5, I study the association between overeducation, performance pay, and wages. Overeducated workers are more productive and have higher wages in comparison to their adequately educated coworkers in the same jobs. However, they face a series of challenges in the labor market, including lower wages in comparison to their similarly educated peers who are in correctly matched jobs. Yet, less consensus exists over the adjustment mechanisms to overcome the negative consequences of overeducation. In this study, I examine the hypotheses that overeducated workers sort into performance pay jobs as an adjustment mechanism and that performance pay enhances their wages. Using the German Socio-Economic Panel, I show that overeducation associates with a higher likelihood of sorting into performance pay jobs and that performance pay moderates the wages of overeducated workers positively. It also holds in endogenous switching regressions accounting for the potential endogeneity of performance pay. Importantly, I show that the positive role of performance pay is particularly larger for the wages of overeducated women.

Au Luxembourg, des médiateur·trice·s scolaires externes apportent leur aide quand des conflits surgissent en milieu scolaire. Le Service de médiation scolaire fournit un soutien en cas de risque de décrochage scolaire et de conflits en lien avec l’inclusion et l’intégration d’élèves à besoins éducatifs spécifiques ou issu·e·s de l’immigration. Michèle Schilt s’est entretenue avec la directrice du service, Lis De Pina, sur le travail des médiateur·trice·s scolaires.

Un débat politique passionnant a lieu en classe. La plupart de vos élèves sont attentif·ve·s, intéressé·e·s et engagé·e·s. Tout se passe à merveille, jusqu’à ce qu’un·e élève lance – délibérément ou sans réfléchir – des propos extrémistes ou complotistes. Comment réagir ?

Les conflits sont inévitables. Ils surviennent même au sein d’établissements scolaires empreints d’une culture démocratique. L’instauration d’une culture d’échange constructif sur les questions conflictuelles permet toutefois d’utiliser les litiges comme des opportunités d’apprentissage et de développement personnel et organisationnel.

Internet-Memes sind ein globales, populäres Medium, oft und zumeist unproblematisiert rezipiert und in ihrer Komik meist nur fragmentarisch, auf bestimmte Aspekte fokussiert analysiert. Die vorliegende Arbeit bemüht sich um eine möglichst umfassende Darstellung der Komik in Memes basierend auf klassischen und modernen Komikkategorien. Auf Grundlage einer umfassend-kritischen Synthese der vorliegenden Fachliteratur und eines präzisen Analysemodells kann so eine begründete Diskussion über memetische Komik, ihre Funktionen und ihre positiven wie problematischen Aspekte geführt werden.

Income is one of the key indicators to measure regional differences, individual opportunities, and inequalities in society. In Germany, the regional distribution of income is a central concern, especially regarding persistent East-West, North-South, or urban-rural inequalities. Effective local policies and institutions require reliable data and indicators on regional inequality. However, its measurement faces severe data limitations: Inconsistencies in the existing microdata sources yield an inconclusive picture of regional inequality. While survey data provide a wide range of individual and household information but lack top incomes, tax data contain the most reliable income records but offer a limited range of socio-demographic variables essential for income analysis. In addition, information on the long-term evolution of the income distribution at the small-scale level is scarce. In this context, this thesis evaluates regional income inequality in Germany from various perspectives and embeds three self-contained studies in Chapters 3, 4, and 5, which present different data integration approaches. The first chapter motivates this thesis, while the second chapter provides a brief overview of the theoretical and empirical concepts as well as the datasets, highlighting the need to combine data from different sources. Chapter 3 tackles the issue of poor coverage of top incomes in surveys, also referred to as the ’missing rich’ problem, which leads to severe underestimation of income inequality. At the regional level this shortcoming is even more eminent due to small regional sample sizes. Based on reconciled tax and survey data, this chapter therefore proposes a new multiple imputation top income correction approach that, unlike previous research, focuses on the regional rather than the national level. The findings indicate that inequality between and within the regions is much larger than previously understood with the magnitude of the adjustment depending on the federal states’ level of inequality in the tail. To increase the potential of the tax data for income analysis and to overcome the lack of socio-demographic characteristics, Chapter 4 enriches the tax data with information on education and working time from survey data. For that purpose, a simulation study evaluates missing data methods and performant prediction models, finding that Multinomial Regression and Random Forest are the most suitable methods for the specific data fusion scenario. The results indicate that data fusion approaches broaden the scope for regional inequality analysis from cross-sectional enhanced tax data. Shifting from a cross-sectional to a longitudinal perspective on regional income inequality, Chapter 5 contributes to the currently relatively small body of literature dealing with the potential development of regional income disparities over time. Regionalized dynamic microsimulations provide a powerful tool for the study of long-term income developments. Therefore, this chapter extends the microsimulation model MikroSim with an income module that accounts for the individual, household, and regional context. On this basis, the potential dynamics in gender and migrant income gaps across the districts in Germany are simulated under scenarios of increased full-time employment rates and higher levels of tertiary education. The results show that the scenarios have regionally differing effects on inequality dynamics, highlighting the considerable potential of dynamic microsimulations for regional evidence-based policies. For the German case, the MikroSim model is well suited to analyze future regional developments and can be flexibly adapted for further specific research questions.

This thesis consists of four highly related chapters examining China’s rise in the aluminium industry. The first chapter addresses the conditions that allowed China, which first entered the market in the 1950s, to rise to world leadership in aluminium production. Although China was a latecomer, its re-entry into the market after the oil crises in the 1970s was a success and led to its ascent as the world’s largest aluminium producer by 2001. With an estimated production of 40.4 million tonnes in 2022, China represented almost 60% of the global output. Chapter 1 examines the factors underlying this success, such as the decline of international aluminium cartels, the introduction of innovative technology, the US granting China the MFN tariff status, Chinese-specific factors, and supportive government policies. Chapter 2 develops a mathematical model to analyze firms’ decisions in the short term. It examines how an incumbent with outdated technology and a new entrant with access to a new type of technology make strategic decisions, including the incumbent’s decision whether to deter entry, the production choice of firms, the optimal technology adoption rate of the newcomer, and cartel formation. Chapter 3 focuses on the adoption of new technology by firms upon market entry in four scenarios: firstly, a free market Cournot competition; secondly, a situation in which the government determines technology adoption rates; thirdly, a scenario in which the government controls both technology and production; and finally, a scenario where the government dictates technology adoption rates, production levels, and also the number of market participants. Chapter 4 applies the Spencer and Brander (1983) framework to examine strategic industrial policy. The model assumes that there are two exporting firms in two different countries that sell a product to a third country. We examine how the domestic firm is influenced by government intervention, such as the provision of a fixed-cost subsidy to improve its competitiveness relative to the foreign company. Chapter 4 initially investigates a scenario where only one government offers a fixed-cost subsidy, followed by an analysis of the case when both governments simultaneously provide financial help. Taken together, these chapters provide a comprehensive analysis of the strategic, technological, and political factors contributing to China’s leadership in the global aluminium industry. Chapter 1: The Rise of China as a Latecomer in the Global Aluminium Industry This chapter examines China’s remarkable transformation into a global leader in the aluminium industry, a sector in which the country accounted for approximately 58.9% of worldwide production in 2022. We examine how China, a latecomer to the aluminium industry that started off with labor-intensive technology in 1953, grew into the largest aluminium producer with some of the most advanced smelters in the world. This analysis identifies and discusses several opportunities that Chinese aluminium producers took advantage of. The first set of opportunities happened during the 1970s oil crises, which softened international competition and allowed China to acquire innovative smelting technology from Japan. The second set of opportunities started at about the same time when China opened its economy in 1978. The substantial demand for aluminium in China is influenced by both external and internal factors. Externally, the US granted China’s MFN tariff status in 1980 and China entered the World Trade Organization (WTO) in 2001. Both events contributed to a surge in Chinese aluminium consumption. Internally, China’s investment-led growth model boosted further its aluminium demand. Additional factors specific to China, such as low labor costs and the abundance of coal as an energy source, offer Chinese firms competitive advantages against international players. Furthermore, another window of opportunity is due to Chinese governmental policies, including phasing out old technology, providing subsidies, and gradually opening the economy to enhance domestic competition before expanding globally. By describing these elements, the study provides insights into the dynamic interplay of external circumstances and internal strategies that contributed to the success of the Chinese aluminium industry. Chapter 2: Technological Change and Strategic Choices for Incumbent and New Entrant This chapter introduces an oligopoly model that includes two actors: an incumbent and a potential entrant, that compete in the same market. We assume that two participants are located in different parts of the market: the incumbent is situated in area 1, whereas the potential entrant may venture into the other region, area 2. The incumbent exists in stage zero, where it can decide whether to deter the newcomer’s entry. A new type of technology exists in period one, when the newcomer may enter the market. In the short term, the incumbent is trapped with the outdated technology, while the new entrant may choose to partially or completely adopt the latest technology. Our results suggest the following: Firstly, the incumbent only tries to deter the new entrant if a condition for entry cost is met. Secondly, the new entrant is only interested in forming a cartel with the incumbent if a function of the ratio of the variable to new technology’s fixed-cost parameters is sufficiently high. Thirdly, if the newcomer asks to form a cartel, the incumbent will always accept this request. Finally, we can obtain the optimal new technology adoption rate for the newcomer. Chapter 3: Technological Adoption and Welfare in Cournot Oligopoly This study examines the difference between the optimal technology adoption rates chosen by firms in a homogeneous Cournot oligopoly and that preferred by a benevolent government upon firms’ market entry. To address the question of whether the technology choices of firms and government are similar, we analyze several different scenarios, which differ in the extent of government intervention in the market. Our results suggest a relationship between the number of firms in the market and the impact of government intervention on technology adoption rates. Especially in situations with a low number of firms that are interested in entering the market, greater government influence tends to lead to higher technology adoption rates of firms. Conversely, in scenarios with a higher number of firms and a government that lacks control over the number of market players, the technology adoption rate of firms will be highest when the government plays no role. Chapter 4: International Technological Innovation and Industrial Strategies Supporting domestic firms when they first enter the market may be seen as a favorable policy choice by governments around the world thanks to their ability to enhance the competitive advantage of domestic firms in non-cooperative competition against foreign enterprises (infant industry protection argument). This advantage may allow domestic firms to increase their market share and generate higher profits, thereby improving domestic welfare. This chapter utilizes the Spencer and Brander (1983) framework as a theoretical foundation to elucidate the effects of fixed-cost subsidies on firms’ production levels, technological innovations, and social welfare. The analysis examines two firms in different countries, each producing a homogeneous product that is sold in a third, separate country. We first examine the Cournot-Nash equilibrium in the absence of government intervention, followed by analyzing a scenario where just one government provides a financial subsidy for its domestic firm, and finally, we consider a situation where both governments simultaneously provide financial assistance for their respective firms. Our results suggest that governments aim to maximize social welfare by providing fixed-cost subsidies to their respective firms, finding themselves in a Chicken game scenario. Regarding technology innovation, subsidies lead to an increased technological adoption rate for recipient firms, regardless of whether one or both firms in a market receive support, compared to the situation without subsidies. The technology adoption rate of the recipient firm is higher than of its rival when only the recipient firm benefits from the fixed-cost subsidy. The lowest technology adoption rate of a firm occurs when the firm does not receive a fixed-cost subsidy, but its competitor does. Furthermore, global welfare will benefit the most in case when both exporting countries grant fixed-cost subsidies, and this welfare level is higher when only one country subsidizes than when no subsidies are provided by any country.

This meta-scientific dissertation comprises three research articles that investigated the reproducibility of psychological research. Specifically, they focused on the reproducibility of eye-tracking research on the one hand, and studying preregistration (i.e., the practice of publishing a study protocol before data collection or analysis) as one method to increase reproducibility on the other hand. In Article I, it was demonstrated that eye-tracking data quality is influenced by both the utilized eye-tracker and the specific task it is measuring. That is, distinct strengths and weaknesses were identified in three devices (Tobii Pro X3-120, GP3 HD, EyeLink 1000+) in an extensive test battery. Consequently, both the device and specific task should be considered when designing new studies. Meanwhile, Article II focused on the current perception of preregistration in the psychological research community and future directions for improving this practice. The survey showed that many researchers intended to preregister their research in the future and had overall positive attitudes toward preregistration. However, various obstacles were identified currently hindering preregistration, which should be addressed to increase its adoption. These findings were supplemented by Article III, which took a closer look at one preregistration-specific tool: the PRP-QUANT Template. In a simulation trial and a survey, the template demonstrated high usability and emerged as a valuable resource to support researchers in using preregistration. Future revisions of the template could help to further facilitate this open science practice. In this dissertation, the findings of the three articles are summarized and discussed regarding their implications and potential future steps that could be implemented to improve the reproducibility of psychological research.

Representation Learning techniques play a crucial role in a wide variety of Deep Learning applications. From Language Generation to Link Prediction on Graphs, learned numerical vector representations often build the foundation for numerous downstream tasks. In Natural Language Processing, word embeddings are contextualized and depend on their current context. This useful property reflects how words can have different meanings based on their neighboring words. In Knowledge Graph Embedding (KGE) approaches, static vector representations are still the dominant approach. While this is sufficient for applications where the underlying Knowledge Graph (KG) mainly stores static information, it becomes a disadvantage when dynamic entity behavior needs to be modelled. To address this issue, KGE approaches would need to model dynamic entities by incorporating situational and sequential context into the vector representations of entities. Analogous to contextualised word embeddings, this would allow entity embeddings to change depending on their history and current situational factors. Therefore, this thesis provides a description of how to transform static KGE approaches to contextualised dynamic approaches and how the specific characteristics of different dynamic scenarios are need to be taken into consideration. As a starting point, we conduct empirical studies that attempt to integrate sequential and situational context into static KG embeddings and investigate the limitations of the different approaches. In a second step, the identified limitations serve as guidance for developing a framework that enables KG embeddings to become truly dynamic, taking into account both the current situation and the past interactions of an entity. The two main contributions in this step are the introduction of the temporally contextualized Knowledge Graph formalism and the corresponding RETRA framework which realizes the contextualisation of entity embeddings. Finally, we demonstrate how situational contextualisation can be realized even in static environments, where all object entities are passive at all times. For this, we introduce a novel task that requires the combination of multiple context modalities and their integration with a KG based view on entity behavior.

Differential equations yield solutions that necessarily contain a certain amount of regularity and are based on local interactions. There are various natural phenomena that are not well described by local models. An important class of models that describe long-range interactions are the so-called nonlocal models, which are the subject of this work. The nonlocal operators considered here are integral operators with a finite range of interaction and the resulting models can be applied to anomalous diffusion, mechanics and multiscale problems. While the range of applications is vast, the applicability of nonlocal models can face problems such as the high computational and algorithmic complexity of fundamental tasks. One of them is the assembly of finite element discretizations of truncated, nonlocal operators. The first contribution of this thesis is therefore an openly accessible, documented Python code which allows to compute finite element approximations for nonlocal convection-diffusion problems with truncated interaction horizon. Another difficulty in the solution of nonlocal problems is that the discrete systems may be ill-conditioned which complicates the application of iterative solvers. Thus, the second contribution of this work is the construction and study of a domain decomposition type solver that is inspired by substructuring methods for differential equations. The numerical results are based on the abstract framework of nonlocal subdivisions which is introduced here and which can serve as a guideline for general nonlocal domain decomposition methods.

Die chinesische und westliche Forschung, die sich mit der Beziehung zwischen chinesischer Kultur und katholischer Kirche befasst, konzentriert sich in der Regel auf die katholische Kirche in China vor dem Verbot des Christentums. Die einzigartige Perspektive dieser Arbeit besteht darin, die Veränderungen in der Beziehung zwischen den beiden vom Ende der Ming-Dynastie bis zur ersten Hälfte des 20. Jahrhunderts zu untersuchen. Vor dem Verbot nährten die katholischen Missionare den konfuzianischen Gelehrten und verbanden die katholische Lehre mit dem Konfuzianismus, um ihren Einfluss in der Oberschicht der chinesischen Gesellschaft auszuüben. Nach dem Verbot achteten die katholischen Missionare nicht so sehr auf ihre Beziehung zur chinesischen Kultur wie ihre Vorgänger im 17. und 18. Jahrhundert. Einige Missionare sowie chinesische Katholiken wollten die Situation ändern und förderten gemeinsam die Gründung der Fu-Jen-Universität, die großen Wert auf die chinesische Kultur legte und die Beziehung zwischen der Katholischen Kirche und der chinesischen Kultur Anfang des 20. Jahrhunderts widerspiegeln konnte. Die Professoren der Abteilung Chinesisch und Geschichte leisteten den größten Beitrag zur Forschung der chinesischen Kultur an der Universität. Im Vergleich zu anderen wichtigen Universitäten in Peking, wo die chinesische Literatur im Fachbereich Chinesisch eine zentrale Stellung einnahm, legte die Fu-Jen-Universität mehr Wert auf die chinesische Sprache und Schriftzeichen. Anfang des 20. Jahrhunderts erlangten Frauen unter dem Einfluss der globalen feministischen Bewegung das Recht auf Hochschulbildung. Bis 1920 waren jedoch die katholischen Universitäten in Bezug auf die Hochschulbildung von Frauen Jahrzehnte hinter den protestantischen und nichtkirchlichen Universitäten zurückgefallen. Die Fu-Jen-Universität verbesserte diese Situation, indem sie nicht nur eine große Anzahl von Studentinnen annahm, sondern ihnen eine Vielzahl von Fächern einschließlich Chinesisch und Geschichte anbot. Im Allgemeinen konnte die Universität als Verbindung zwischen dem Katholizismus und der chinesischen Kultur in der ersten Hälfte des 20. Jahrhunderts angesehen werden. Sie spielte eine wichtige Rolle nicht nur bei der Erforschung und Verbreitung der chinesischen Kultur, sondern auch bei der Ausweitung des Einflusses der katholischen Kirche zu dieser Zeit.

Sozialunternehmen haben mindestens zwei Ziele: die Erfüllung ihrer sozialen bzw. ökologischen Mission und finanzielle Ziele. Zwischen diesen Zielen können Spannungen entstehen. Wenn sie sich in diesem Spannungsfeld wiederholt zugunsten der finanziellen Ziele entscheiden, kommt es zum Mission Drift. Die Priorisierung der finanziellen Ziele überlagert dabei die soziale Mission. Auch wenn das Phänomen in der Praxis mehrfach beobachtet und in Einzelfallanalysen beschrieben wurde, gibt es bislang wenig Forschung zu Mission Drift. Der Fokus der vorliegenden Arbeit liegt darauf, diese Forschungslücke zu schließen und eigene Erkenntnisse für die Auslöser und Treiber des Mission Drifts von Sozialunternehmen zu ermitteln. Ein Augenmerk liegt auf den verhaltensökonomischen Theorien und der Mixed-Gamble-Logik. Dieser Logik zufolge liegt bei Entscheidungen immer eine Gleichzeitigkeit von Gewinnen und Verlusten vor, sodass Entscheidungsträger die Furcht vor Verlusten gegenüber der Aussicht auf Gewinne abwägen müssen. Das Modell wird genutzt, um eine neue theoretische Betrachtungsweise auf die Abwägung zwischen sozialen und finanziellen Zielen bzw. Mission Drift zu erhalten. Mit einem Conjoint Experiment werden Daten über das Entscheidungsverhalten von Sozialunternehmern generiert. Im Zentrum steht die Abwägung zwischen sozialen und finanziellen Zielen in verschiedenen Szenarien (Krisen- und Wachstumssituationen). Mithilfe einer eigens erstellten Stichprobe von 1.222 Sozialunternehmen aus Deutschland, Österreich und der Schweiz wurden 187 Teilnehmende für die Studie gewonnen. Die Ergebnisse dieser Arbeit zeigen, dass eine Krisensituation Auslöser für Mission Drift von Sozialunternehmen sein kann, weil in diesem Szenario den finanziellen Zielen die größte Bedeutung zugemessen wird. Für eine Wachstumssituation konnten hingegen keine solche Belege gefunden werden. Hinzu kommen weitere Einflussfaktoren, welche die finanzielle Orientierung verstärken können, nämlich die Gründeridentitäten der Sozialunternehmer, eine hohe Innovativität der Unternehmen und bestimmte Stakeholder. Die Arbeit schließt mit einer ausführlichen Diskussion der Ergebnisse. Es werden Empfehlungen gegeben, wie Sozialunternehmen ihren Zielen bestmöglich treu bleiben können. Außerdem werden die Limitationen der Studie und Wege für zukünftige Forschung im Bereich Mission Drift aufgezeigt.

Information in der vorvertraglichen Phase – das heißt, Informationspflichten sowie Rechtsfolgen von Informationserteilung und -nichterteilung – in Bezug auf Kaufvertrag und Wahl des optionalen Instruments hat im Vorschlag der Europäischen Kommission für ein Gemeinsames Europäisches Kaufrecht (GEK; KOM(2011) 635) vielfältige Regelungen erfahren. Die vorliegende Arbeit betrachtet diese Regelungen auch in ihrem Verhältnis zu den Textstufen des Europäischen Privatrechts – Modellregeln und verbraucherschützende EU-Richtlinien – und misst sie an ökonomischen Rahmenbedingungen, die die Effizienz von Transaktionen gebieten und Grenzen des Nutzens von (Pflicht-)Informationen aufzeigen. Vom Grundsatz der Vertragsfreiheit ausgehend ist jeder Partei das Risiko zugewiesen, unzureichend informiert zu sein, während die Gegenseite nur punktuell zur Information verpflichtet ist. Zwischen Unternehmern bleibt es auch nach dem GEK hierbei, doch zwischen Unternehmer und Verbraucher wird dieses Verhältnis umgekehrt. Dort gelten, mit Differenzierung nach Vertragsschlusssituationen, umfassende Kataloge von Informationspflichten hinsichtlich des Kaufvertrags. Als Konzept ist dies grundsätzlich sinnvoll; die Pflichten dienen dem Verbraucherschutz, insbesondere der Informiertheit und Transparenz vor der Entscheidung über den Vertragsschluss. Teilweise gehen die Pflichten aber zu weit. Die Beeinträchtigung der Vertragsfreiheit des Unternehmers durch die Pflichten und die Folgen ihrer Verletzung lässt sich nicht vollständig mit dem Ziel des Verbraucherschutzes rechtfertigen. Durch das Übermaß an Information fördern die angeordneten Pflichten den Verbraucherschutz nur eingeschränkt; sie genügen nicht verhaltensökonomischen Maßstäben. Es empfiehlt sich daher, zwischen Unternehmern und Verbrauchern bestimmte verpflichtende Informationsinhalte ganz zu streichen, auf im konkreten Fall nicht erforderliche Information zu verzichten, erst nach Vertragsschluss relevante Informationen auf diese Zeit zu verschieben und die verbleibenden vorvertraglichen Pflichtinformationen in einer für den Verbraucher besser zu verarbeitenden Weise zu präsentieren. Von den einem Verbraucher zu erteilenden Informationen sollte stets verlangt werden, dass sie klar und verständlich sind; die Beweislast für ihre ordnungsgemäße Erteilung sollte generell dem Unternehmer obliegen. Neben die ausdrücklich angeordneten Informationspflichten treten ungeachtet der Verbraucher- oder Unternehmereigenschaft sowie der Käufer- oder Verkäuferrolle stark einzelfallabhängige Informationspflichten nach Treu und Glauben, die im Recht der Willensmängel niedergelegt sind. Hier ist der Grundsatz verwirklicht, dass mangelnde Information zunächst das eigene Risiko jeder Partei ist; berechtigtes Vertrauen und freie Willensbildung werden geschützt. Diese Pflichten berücksichtigen auch das Ziel der Effizienz und achten die Vertragsfreiheit. Das Vertrauen auf jegliche erteilten Informationen wird zudem dadurch geschützt, dass sie den Vertragsinhalt – allerdings in Verbraucherverträgen nicht umfassend genug – mitbestimmen können und dass ihre Unrichtigkeit sanktioniert wird. Die Verletzung jeglicher Arten von Informationspflichten kann insbesondere einen Schadensersatzanspruch sowie – über das Recht der Willensmängel – die Möglichkeit zur Lösung vom Vertrag nach sich ziehen. Das Zusammenspiel der unterschiedlichen Mechanismen führt allerdings zu Friktionen sowie zu Lücken in den Rechtsfolgen von Informationspflichtverletzungen. Daher empfiehlt sich die Schaffung eines Schadensersatzanspruchs für jede treuwidrig unterlassene Informationserteilung; hierdurch wird das Gebot von Treu und Glauben auch außerhalb des Rechts der Willensmängel zu einer eigentlichen einzelfallabhängigen Informationspflicht aufgewertet.

Bericht über die Ereignisse und Entwicklungen der UB Trier 2022.

Data fusions are becoming increasingly relevant in official statistics. The aim of a data fusion is to combine two or more data sources using statistical methods in order to be able to analyse different characteristics that were not jointly observed in one data source. Record linkage of official data sources using unique identifiers is often not possible due to methodological and legal restrictions. Appropriate data fusion methods are therefore of central importance in order to use the diverse data sources of official statistics more effectively and to be able to jointly analyse different characteristics. However, the literature lacks comprehensive evaluations of which fusion approaches provide promising results for which data constellations. Therefore, the central aim of this thesis is to evaluate a concrete plethora of possible fusion algorithms, which includes classical imputation approaches as well as statistical and machine learning methods, in selected data constellations. To specify and identify these data contexts, data and imputation-related scenario types of a data fusion are introduced: Explicit scenarios, implicit scenarios and imputation scenarios. From these three scenario types, fusion scenarios that are particularly relevant for official statistics are selected as the basis for the simulations and evaluations. The explicit scenarios are the fulfilment or violation of the Conditional Independence Assumption (CIA) and varying sample sizes of the data to be matched. Both aspects are likely to have a direct, that is, explicit, effect on the performance of different fusion methods. The summed sample size of the data sources to be fused and the scale level of the variable to be imputed are considered as implicit scenarios. Both aspects suggest or exclude the applicability of certain fusion methods due to the nature of the data. The univariate or simultaneous, multivariate imputation solution and the imputation of artificially generated or previously observed values in the case of metric characteristics serve as imputation scenarios. With regard to the concrete plethora of possible fusion algorithms, three classical imputation approaches are considered: Distance Hot Deck (DHD), the Regression Model (RM) and Predictive Mean Matching (PMM). With Decision Trees (DT) and Random Forest (RF), two prominent tree-based methods from the field of statistical learning are discussed in the context of data fusion. However, such prediction methods aim to predict individual values as accurately as possible, which can clash with the primary objective of data fusion, namely the reproduction of joint distributions. In addition, DT and RF only comprise univariate imputation solutions and, in the case of metric variables, artificially generated values are imputed instead of real observed values. Therefore, Predictive Value Matching (PVM) is introduced as a new, statistical learning-based nearest neighbour method, which could overcome the distributional disadvantages of DT and RF, offers a univariate and multivariate imputation solution and, in addition, imputes real and previously observed values for metric characteristics. All prediction methods can form the basis of the new PVM approach. In this thesis, PVM based on Decision Trees (PVM-DT) and Random Forest (PVM-RF) is considered. The underlying fusion methods are investigated in comprehensive simulations and evaluations. The evaluation of the various data fusion techniques focusses on the selected fusion scenarios. The basis for this is formed by two concrete and current use cases of data fusion in official statistics, the fusion of EU-SILC and the Household Budget Survey on the one hand and of the Tax Statistics and the Microcensus on the other. Both use cases show significant differences with regard to different fusion scenarios and thus serve the purpose of covering a variety of data constellations. Simulation designs are developed from both use cases, whereby the explicit scenarios in particular are incorporated into the simulations. The results show that PVM-RF in particular is a promising and universal fusion approach under compliance with the CIA. This is because PVM-RF provides satisfactory results for both categorical and metric variables to be imputed and also offers a univariate and multivariate imputation solution, regardless of the scale level. PMM also represents an adequate fusion method, but only in relation to metric characteristics. The results also imply that the application of statistical learning methods is both an opportunity and a risk. In the case of CIA violation, potential correlation-related exaggeration effects of DT and RF, and in some cases also of RM, can be useful. In contrast, the other methods induce poor results if the CIA is violated. However, if the CIA is fulfilled, there is a risk that the prediction methods RM, DT and RF will overestimate correlations. The size ratios of the studies to be fused in turn have a rather minor influence on the performance of fusion methods. This is an important indication that the larger dataset does not necessarily have to serve as a donor study, as was previously the case. The results of the simulations and evaluations provide concrete implications as to which data fusion methods should be used and considered under the selected data and imputation constellations. Science in general and official statistics in particular benefit from these implications. This is because they provide important indications for future data fusion projects in order to assess which specific data fusion method could provide adequate results along the data constellations analysed in this thesis. Furthermore, with PVM this thesis offers a promising methodological innovation for future data fusions and for imputation problems in general.