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The German Mittelstand is closely linked to the success of the German economy. Mittelstand firms, thereof numerous Hidden Champions, significantly contribute to Germany’s economic performance, innovation, and export strength. However, the advancing digitalization poses complex challenges for Mittelstand firms. To benefit from the manifold opportunities offered by digital technologies and to defend or even expand existing market positions, Mittelstand firms must transform themselves and their business models. This dissertation uses quantitative methods and contributes to a deeper understanding of the distinct needs and influencing factors of the digital transformation of Mittelstand firms. The results of the empirical analyses of a unique database of 525 mid-sized German manufacturing firms, comprising both firm-related information and survey data, show that organizational capabilities and characteristics significantly influence the digital transformation of Mittelstand firms. The results support the assumption that dynamic capabilities promote the digital transformation of such firms and underline the important role of ownership structure, especially regarding family influence, for the digital transformation of the business model and the pursuit of growth goals with digitalization. In addition to the digital transformation of German Mittelstand firms, this dissertation examines the economic success and regional impact of Hidden Champions and hence, contributes to a better understanding of the Hidden Champion phenomenon. Using quantitative methods, it can be empirically proven that Hidden Champions outperform other mid-sized firms in financial terms and promote regional development. Consequently, the results of this dissertation provide valuable research contributions and offer various practical implications for firm managers and owners as well as policy makers.
Every action we perform, no matter how simple or complex, has a cognitive representation. It is commonly assumed that these are organized hierarchically. Thus, the representation of a complex action consists of multiple simpler actions. The representation of a simple action, in turn, consists of stimulus, response, and effect features. These are integrated into one representation upon the execution of an action and can be retrieved if a feature is repeated. Depending on whether retrieved features match or only partially match the current action episode, this might benefit or impair the execution of a subsequent action. This pattern of costs and benefits results in binding effects that indicate the strength of common representation between features. Binding effects occur also in more complex actions: Multiple simple actions seem to form representations on a higher level through the integration and retrieval of sequentially given responses, resulting in so-called response-response binding effects. This dissertation aimed to investigate what factors determine whether simple actions form more complex representations. The first line of research (Articles 1-3) focused on dissecting the internal structure of simple actions. Specifically, I investigated whether the spatial relation of stimuli, responses, or effects, that are part of two different simple actions, influenced whether these simple actions are represented as one more complex action. The second line of research (Articles 2, 4, and 5) investigated the role of context on the formation and strength of more complex action representations. Results suggest that spatial separation of responses as well as context might affect the strength of more complex action representations. In sum, findings help to specify assumptions on the structure of complex action representations. However, it may be important to distinguish factors that influence the strength and structure of action representations from factors that terminate action representations.
There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.
Building Fortress Europe Economic realism, China, and Europe’s investment screening mechanisms
(2023)
This thesis deals with the construction of investment screening mechanisms across the major economic powers in Europe and at the supranational level during the post-2015 period. The core puzzle at the heart of this research is how, in a traditional bastion of economic liberalism such as Europe, could a protectionist tool such as investment screening be erected in such a rapid manner. Within a few years, Europe went from a position of being highly welcoming towards foreign investment to increasingly implementing controls on it, with the focus on China. How are we to understand this shift in Europe? I posit that Europe’s increasingly protectionist shift on inward investment can be fruitfully understood using an economic realist approach, where the introduction of investment screening can be seen as part of a process of ‘balancing’ China’s economic rise and reasserting European competitiveness. China has moved from being the ‘workshop of the world’ to becoming an innovation-driven economy at the global technological frontier. As China has become more competitive, Europe, still a global economic leader, broadly situated at the technological frontier, has begun to sense a threat to its position, especially in the context of the fourth industrial revolution. A ‘balancing’ process has been set in motion, in which Europe seeks to halt and even reverse the narrowing competitiveness gap between it and China. The introduction of investment screening measures is part of this process.
Some of the largest firms in the DACH region (Germany, Austria, Switzerland) are (partially) owned by a foundation and/or a family office, such as Aldi, Bosch, or Rolex. Despite their growing importance, prior research neglected to analyze the impact of these intermediaries on the firms they own. This dissertation closes this research gap by contributing to a deeper understanding of two increasingly used family firm succession vehicles, through four empirical quantitative studies. The first study focuses on the heterogeneity in foundation-owned firms (FOFs) by applying a descriptive analysis to a sample of 169 German FOFs. The results indicate that the family as a central stakeholder in a family foundation fosters governance that promotes performance and growth. The second study examines the firm growth of 204 FOFs compared to matched non-FOFs from the DACH region. The findings suggest that FOFs grow significantly less in terms of sales but not with regard to employees. In addition, it seems that this negative effect is stronger for the upper than for the middle or lower quantiles of the growth distribution. Study three adopts an agency perspective and investigates the acquisition behavior within the group of 164 FOFs. The results reveal that firms with charitable foundations as owners are more likely to undertake acquisitions and acquire targets that are geographically and culturally more distant than firms with a family foundation as owner. At the same time, they favor target companies from the same or related industries. Finally, the fourth study scrutinizes the capital structure of firms owned by single family-offices (SFOs). Drawing on a hand-collected sample of 173 SFO-owned firms in the DACH region, the results show that SFO-owned firms display a higher long-term debt ratio than family-owned firms, indicating that SFO-owned firms follow trade-off theory, similar to private equity-owned firms. Additional analyses show that this effect is stronger for SFOs that sold their original family firm. In conclusion, the outcomes of this dissertation furnish valuable research contributions and offer practical insights for families navigating such intermediaries or succession vehicles in the long term.
Family firms play a crucial role in the DACH region (Germany, Austria, Switzerland). They are characterized by a long tradition, a strong connection to the region, and a well-established network. However, family firms also face challenges, especially in finding a suitable successor. Wealthy entrepreneurial families are increasingly opting to establish Single Family Offices (SFOs) as a solution to this challenge. An SFO takes on the management and protection of family wealth. Its goal is to secure and grow the wealth over generations. In Germany alone, there are an estimated 350 to 450 SFOs, with 70% of them being established after the year 2000. However, research on SFOs is still in its early stages, particularly regarding the role of SFOs as firm owners. This dissertation delves into an exploration of SFOs through four quantitative empirical studies. The first study provides a descriptive overview of 216 SFOs from the DACH-region. Findings reveal that SFOs exhibit a preference for investing in established companies and real estate. Notably, only about a third of SFOs engage in investments in start-ups. Moreover, SFOs as a group are heterogeneous. Categorizing them into three groups based on their relationship with the entrepreneurial family and the original family firm reveals significant differences in their asset allocation strategies. Subsequent studies in this dissertation leverage a hand-collected sample of 173 SFO-owned firms from the DACH region, meticulously matched with 684 family-owned firms from the same region. The second study focusing on financial performance indicates that SFO-owned firms tend to exhibit comparatively poorer financial performance than family-owned firms. However, when members of the SFO-owning family hold positions on the supervisory or executive board of the firm, there's a notable improvement. The third study, concerning cash holdings, reveals that SFO-owned firms maintain a higher cash holding ratio compared to family-owned firms. Notably, this effect is magnified when the SFO has divested its initial family firms. Lastly, the fourth study regarding capital structure highlights that SFO-owned firms tend to display a higher long-term debt ratio than family-owned firms. This suggests that SFO-owned firms operate within a trade-off theory framework, like private equity-owned firms. Furthermore, this effect is stronger for SFOs that sold their original family firm. The outcomes of this research are poised to provide entrepreneurial families with a practical guide for effectively managing and leveraging SFOs as a strategic long-term instrument for succession and investment planning.
This thesis deals with REITs, their capital structure and the effects on leverage that regulatory requirements might have. The data used results from a combination of Thomson Reuters data with hand-collected data regarding the REIT status, regulatory information and law variables. Overall, leverage is analysed across 20 countries in the years 2007 to 2018. Country specific data, manually extracted from yearly EPRA reportings, is merged with company data in order to analyse the influence of different REIT restrictions on a firm's leverage.
Observing statistically significant differences in means across NON-REITs and REITs, causes motivation for further investigations. My results show that variables beyond traditional capital structure determinants impact the leverage of REITs. I find that explicit restrictions on leverage and the distribution of profits have a significant effect on leverage decisions. This supports the notion that the restrictions from EPRA reportings are mandatory. I test for various combinations of regulatory variables that show both in isolation as well as in combination significant effects on leverage.
My main result is the following: Firms that operate under regulation that specifies a maximum leverage ratio, in addition to mandatory high dividend distributions, have on average lower leverage ratios. Further the existence of sanctions has a negative effect on REITs' leverage ratios, indicating that regulation is binding. The analysis clearly shows that traditional capital structure determinants are of second order relevance. This relationship highlights the impact on leverage and financing decisions caused by regulation. These effects are supported by further analysis. Results based on an event study show that REITs have statistically lower leverage ratios compared to NON-REITs. Based on a structural break model, the following effect becomes apparent: REITs increase their leverage ratios in years prior REIT status. As a consequence, the ex ante time frame is characterised by a bunker and adaption process, followed by the transformation in the event. Using an event study and a structural break model, the analysis highlights the dominance of country-specific regulation.
The following dissertation contains three studies examining academic boredom development in five high-track German secondary schools (AVG-project data; Study 1: N = 1,432; Study 2: N = 1,861; Study 3: N = 1,428). The investigation period spanned 3.5 years, with four waves of measurement from grades 5 to 8 (T1: 5th grade, after transition to secondary school; T2: 5th grade, after mid-term evaluations; T3: 6th grade, after mid-term evaluations; T4: 8th grade, after mid-term evaluations). All three studies featured cross-sectional and longitudinal analyses, separating, and comparing the subject domains of mathematics and German.
Study 1 provided an investigation of academic boredom’s factorial structure alongside correlational and reciprocal relations of different forms of boredom and academic self-concept. Analyses included reciprocal effects models and latent correlation analyses. Results indicated separability of boredom intensity, boredom due to underchallenge and boredom due to overchallenge, as separate, correlated factors. Evidence for reciprocal relations between boredom and academic self-concept was limited.
Study 2 examined the effectiveness and efficacy of full-time ability grouping for as a boredom intervention directed at the intellectually gifted. Analyses included propensity score matching, and latent growth curve modelling. Results pointed to limited effectiveness and efficacy for full-time ability grouping regarding boredom reduction.
Study 3 explored gender differences in academic boredom development, mediated by academic interest, academic self-concept, and previous academic achievement. Analyses included measurement invariance testing, and multiple-indicator-multi-cause-models. Results showed one-sided gender differences, with boys reporting less favorable boredom development compared to girls, even beyond the inclusion of relevant mediators.
Findings from all three studies were embedded into the theoretical framework of control-value theory (Pekrun, 2006; 2019; Pekrun et al., 2023). Limitations, directions for future research, and practical implications were acknowledged and discussed.
Overall, this dissertation yielded important insights into boredom’s conceptual complexity. This concerned factorial structure, developmental trajectories, interrelations to other learning variables, individual differences, and domain specificities.
Keywords: Academic boredom, boredom intensity, boredom due to underchallenge, boredom due to overchallenge, ability grouping, gender differences, longitudinal data analysis, control-value theory
The publication of statistical databases is subject to legal regulations, e.g. national statistical offices are only allowed to publish data if the data cannot be attributed to individuals. Achieving this privacy standard requires anonymizing the data prior to publication. However, data anonymization inevitably leads to a loss of information, which should be kept minimal. In this thesis, we analyze the anonymization method SAFE used in the German census in 2011 and we propose a novel integer programming-based anonymization method for nominal data.
In the first part of this thesis, we prove that a fundamental variant of the underlying SAFE optimization problem is NP-hard. This justifies the use of heuristic approaches for large data sets. In the second part, we propose a new anonymization method belonging to microaggregation methods, specifically designed for nominal data. This microaggregation method replaces rows in a microdata set with representative values to achieve k-anonymity, ensuring each data row is identical to at least k − 1 other rows. In addition to the overall dissimilarities of the data rows, the method accounts for errors in resulting frequency tables, which are of high interest for nominal data in practice. The method employs a typical two-step structure: initially partitioning the data set into clusters and subsequently replacing all cluster elements with representative values to achieve k-anonymity. For the partitioning step, we propose a column generation scheme followed by a heuristic to obtain an integer solution, which is based on the dual information. For the aggregation step, we present a mixed-integer problem formulation to find cluster representatives. To this end, we take errors in a subset of frequency tables into account. Furthermore, we show a reformulation of the problem to a minimum edge-weighted maximal clique problem in a multipartite graph, which allows for a different perspective on the problem. Moreover, we formulate a mixed-integer program, which combines the partitioning and the aggregation step and aims to minimize the sum of chi-squared errors in frequency tables.
Finally, an experimental study comparing the methods covered or developed in this work shows particularly strong results for the proposed method with respect to relative criteria, while SAFE shows its strength with respect to the maximum absolute error in frequency tables. We conclude that the inclusion of integer programming in the context of data anonymization is a promising direction to reduce the inevitable information loss inherent in anonymization, particularly for nominal data.
Forest inventories provide significant monitoring information on forest health, biodiversity,
resilience against disturbance, as well as its biomass and timber harvesting potential. For this
purpose, modern inventories increasingly exploit the advantages of airborne laser scanning (ALS)
and terrestrial laser scanning (TLS).
Although tree crown detection and delineation using ALS can be seen as a mature discipline, the
identification of individual stems is a rarely addressed task. In particular, the informative value of
the stem attributes—especially the inclination characteristics—is hardly known. In addition, a lack
of tools for the processing and fusion of forest-related data sources can be identified. The given
thesis addresses these research gaps in four peer-reviewed papers, while a focus is set on the
suitability of ALS data for the detection and analysis of tree stems.
In addition to providing a novel post-processing strategy for geo-referencing forest inventory plots,
the thesis could show that ALS-based stem detections are very reliable and their positions are
accurate. In particular, the stems have shown to be suited to study prevailing trunk inclination
angles and orientations, while a species-specific down-slope inclination of the tree stems and a
leeward orientation of conifers could be observed.
Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural
practices have been shaping the face of the earth, and today around one third of the ice-free land
mass consists of cropland and pastures. While agriculture is necessary for our survival, the
intensity has caused many negative externalities, such as enormous freshwater consumption, the
loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation.
Some of these externalities can potentially be ameliorated by careful allocation of crops and
cropping practices, while at the same time the state of these crops has to be monitored in order
to assess food security. Modern day satellite-based earth observation can be an adequate tool to
quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to
do so, in terms of input data, reference data and classification algorithms have been constantly
improving over the past 60 years, and we live now in a time where fully operational satellites
produce freely available imagery with often less than monthly revisit times at high spatial
resolution. At the same time, classification models have been constantly evolving from
distribution based statistical algorithms, over machine learning to the now ubiquitous deep
learning.
In this environment, we used an explorative approach to advance the state of the art of crop
classification. We conducted regional case studies, focused on the study region of the Eifelkreis
Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique
role in the regional agricultural system and because of their specific phenologic characteristics
we focused solely on maize fields.
In the first case study, we generated reference data for the years 2009 and 2016 in the study
region by drawing polygons based on high resolution aerial imagery, and used these in
conjunction with RapidEye imagery to produce high resolution maize maps with a random forest
classifier and a gaussian blur filter. We were able to highlight the importance of careful residual
analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in
spite of the severe limitations introduced by the restricted acquisition windows due to cloud
coverage, high quality maps could be produced for two years, and the regional development of
maize cultivation could be quantified.
In the second case study, we used these spatially explicit datasets to link the expansion of biogas
producing units with the extended maize cultivation in the area. In a next step, we overlayed the
maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction
and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type
classification in environmental protection, by producing maps of potential soil hazards, which can
be used by local stakeholders to reallocate certain crop types to locations with less associated
risk.
In our third case study, we used Sentinel-1 data as input imagery, and official statistical records
as maize reference data, and were able to produce consistent modeling input data for four
consecutive years. Using these datasets, we could train and validate different models in spatially
iv
and temporally independent random subsets, with the goal of assessing model transferability. We
were able to show that state-of-the-art deep learning models such as UNET performed
significantly superior to conventional models like random forests, if the model was validated in a
different year or a different regional subset. We highlighted and discussed the implications on
modeling robustness, and the potential usefulness of deep learning models in building fully
operational global crop classification models.
We were able to conclude that the first major barrier for global classification models is the
reference data. Since most research in this area is still conducted with local field surveys, and only
few countries have access to official agricultural records, more global cooperation is necessary to
build harmonized and regionally stratified datasets. The second major barrier is the classification
algorithm. While a lot of progress has been made in this area, the current trend of many appearing
new types of deep learning models shows great promise, but has not yet consolidated. There is
still a lot of research necessary, to determine which models perform the best and most robust,
and are at the same time transparent and usable by non-experts such that they can be applied
and used effortlessly by local and global stakeholders.
The Second Language Acquisition of English Non-Finite Complement Clauses – A Usage-Based Perspective
(2022)
One of the most essential hypotheses of usage-based theories and many constructionist approaches to language is that language entails the piecemeal learning of constructions on the basis of general cognitive mechanisms and exposure to the target language in use (Ellis 2002; Tomasello 2003). However, there is still a considerable lack of empirical research on the emergence and mental representation of constructions in second language (L2) acquisition. One crucial question that arises, for instance, is whether L2 learners’ knowledge of a construction corresponds to a native-like mapping of form and meaning and, if so, to what extent this representation is shaped by usage. For instance, it is unclear how learners ‘build’ constructional knowledge, i.e. which pieces of frequency-, form- and meaning-related information become relevant for the entrenchment and schematisation of a L2 construction.
To address these issues, the English catenative verb construction was used as a testbed phenomenon. This idiosyncratic complex construction is comprised of a catenative verb and a non-finite complement clause (see Huddleston & Pullum 2002), which is prototypically a gerund-participial (henceforth referred to as ‘target-ing’ construction) or a to-infinitival complement (‘target-to’ construction):
(1) She refused to do her homework.
(2) Laura kept reading love stories.
(3) *He avoids to listen to loud music.
This construction is particularly interesting because learners often show choices of a complement type different from those of native speakers (e.g. Gries & Wulff 2009; Martinez‐Garcia & Wulff 2012) as illustrated in (3) and is commonly claimed to be difficult to be taught by explicit rules (see e.g. Petrovitz 2001).
By triangulating different types of usage data (corpus and elicited production data) and analysing these by multivariate statistical tests, the effects of different usage-related factors (e.g. frequency, proficiency level of the learner, semantic class of verb, etc.) on the representation and development of the catenative verb construction and its subschemas (i.e. target-to and target-ing construction) were examined. In particular, it was assessed whether they can predict a native-like form-meaning pairing of a catenative verb and non-finite complement.
First, all studies were able to show a robust effect of frequency on the complement choice. Frequency does not only lead to the entrenchment of high-frequency exemplars of the construction but is also found to motivate a taxonomic generalisation across related exemplars and the representation of a more abstract schema. Second, the results indicate that the target-to construction, due to its higher type and token frequency, has a high degree of schematicity and productivity than the target-ing construction for the learners, which allows for analogical comparisons and pattern extension with less entrenched exemplars. This schema is likely to be overgeneralised to (less frequent) target-ing verbs because the learners perceive formal and semantic compatibility between the unknown/infrequent verb and this pattern.
Furthermore, the findings present evidence that less advanced learners (A2-B2) make more coarse-grained generalisations, which are centred around high-frequency and prototypical exemplars/low-scope patterns. In the case of high-proficiency learners (C1-C2), not only does the number of native-like complement choices increase but relational information, such as the semantic subclasses of the verb, form-function contingency and other factors, becomes also relevant for a target-like choice. Thus, the results suggests that with increasing usage experience learners gradually develop a more fine-grained, interconnected representation of the catenative verb construction, which gains more resemblance to the form-meaning mappings of native speakers.
Taken together, these insights highlight the importance for language learning and teaching environments to acknowledge that L2 knowledge is represented in the form of highly interconnected form-meaning pairings, i.e. constructions, that can be found on different levels of abstraction and complexity.
This thesis is concerned with two classes of optimization problems which stem
mainly from statistics: clustering problems and cardinality-constrained optimization problems. We are particularly interested in the development of computational techniques to exactly or heuristically solve instances of these two classes
of optimization problems.
The minimum sum-of-squares clustering (MSSC) problem is widely used
to find clusters within a set of data points. The problem is also known as
the $k$-means problem, since the most prominent heuristic to compute a feasible
point of this optimization problem is the $k$-means method. In many modern
applications, however, the clustering suffers from uncertain input data due to,
e.g., unstructured measurement errors. The reason for this is that the clustering
result then represents a clustering of the erroneous measurements instead of
retrieving the true underlying clustering structure. We address this issue by
applying robust optimization techniques: we derive the strictly and $\Gamma$-robust
counterparts of the MSSC problem, which are as challenging to solve as the
original model. Moreover, we develop alternating direction methods to quickly
compute feasible points of good quality. Our experiments reveal that the more
conservative strictly robust model consistently provides better clustering solutions
than the nominal and the less conservative $\Gamma$-robust models.
In the context of clustering problems, however, using only a heuristic solution
comes with severe disadvantages regarding the interpretation of the clustering.
This motivates us to study globally optimal algorithms for the MSSC problem.
We note that although some algorithms have already been proposed for this
problem, it is still far from being “practically solved”. Therefore, we propose
mixed-integer programming techniques, which are mainly based on geometric
ideas and which can be incorporated in a
branch-and-cut based algorithm tailored
to the MSSC problem. Our numerical experiments show that these techniques
significantly improve the solution process of a
state-of-the-art MINLP solver
when applied to the problem.
We then turn to the study of cardinality-constrained optimization problems.
We consider two famous problem instances of this class: sparse portfolio optimization and sparse regression problems. In many modern applications, it is common
to consider problems with thousands of variables. Therefore, globally optimal
algorithms are not always computationally viable and the study of sophisticated
heuristics is very desirable. Since these problems have a discrete-continuous
structure, decomposition methods are particularly well suited. We then apply a
penalty alternating direction method that explores this structure and provides
very good feasible points in a reasonable amount of time. Our computational
study shows that our methods are competitive to
state-of-the-art solvers and heuristics.
Due to the transition towards climate neutrality, energy markets are rapidly evolving. New technologies are developed that allow electricity from renewable energy sources to be stored or to be converted into other energy commodities. As a consequence, new players enter the markets and existing players gain more importance. Market equilibrium problems are capable of capturing these changes and therefore enable us to answer contemporary research questions with regard to energy market design and climate policy.
This cumulative dissertation is devoted to the study of different market equilibrium problems that address such emerging aspects in liberalized energy markets. In the first part, we review a well-studied competitive equilibrium model for energy commodity markets and extend this model by sector coupling, by temporal coupling, and by a more detailed representation of physical laws and technical requirements. Moreover, we summarize our main contributions of the last years with respect to analyzing the market equilibria of the resulting equilibrium problems.
For the extension regarding sector coupling, we derive sufficient conditions for ensuring uniqueness of the short-run equilibrium a priori and for verifying uniqueness of the long-run equilibrium a posteriori. Furthermore, we present illustrative examples that each of the derived conditions is indeed necessary to guarantee uniqueness in general.
For the extension regarding temporal coupling, we provide sufficient conditions for ensuring uniqueness of demand and production a priori. These conditions also imply uniqueness of the short-run equilibrium in case of a single storage operator. However, in case of multiple storage operators, examples illustrate that charging and discharging decisions are not unique in general. We conclude the equilibrium analysis with an a posteriori criterion for verifying uniqueness of a given short-run equilibrium. Since the computation of equilibria is much more challenging due to the temporal coupling, we shortly review why a tailored parallel and distributed alternating direction method of multipliers enables to efficiently compute market equilibria.
For the extension regarding physical laws and technical requirements, we show that, in nonconvex settings, existence of an equilibrium is not guaranteed and that the fundamental welfare theorems therefore fail to hold. In addition, we argue that the welfare theorems can be re-established in a market design in which the system operator is committed to a welfare objective. For the case of a profit-maximizing system operator, we propose an algorithm that indicates existence of an equilibrium and that computes an equilibrium in the case of existence. Based on well-known instances from the literature on the gas and electricity sector, we demonstrate the broad applicability of our algorithm. Our computational results suggest that an equilibrium often exists for an application involving nonconvex but continuous stationary gas physics. In turn, integralities introduced due to the switchability of DC lines in DC electricity networks lead to many instances without an equilibrium. Finally, we state sufficient conditions under which the gas application has a unique equilibrium and the line switching application has finitely many.
In the second part, all preprints belonging to this cumulative dissertation are provided. These preprints, as well as two journal articles to which the author of this thesis contributed, are referenced within the extended summary in the first part and contain more details.
The main focus of this work is to study the computational complexity of generalizations of the synchronization problem for deterministic finite automata (DFA). This problem asks for a given DFA, whether there exists a word w that maps each state of the automaton to one state. We call such a word w a synchronizing word. A synchronizing word brings a system from an unknown configuration into a well defined configuration and thereby resets the system.
We generalize this problem in four different ways.
First, we restrict the set of potential synchronizing words to a fixed regular language associated with the synchronization under regular constraint problem.
The motivation here is to control the structure of a synchronizing word so that, for instance, it first brings the system from an operate mode to a reset mode and then finally again into the operate mode.
The next generalization concerns the order of states in which a synchronizing word transitions the automaton. Here, a DFA A and a partial order R is given as input and the question is whether there exists a word that synchronizes A and for which the induced state order is consistent with R. Thereby, we study different ways for a word to induce an order on the state set.
Then, we change our focus from DFAs to push-down automata and generalize the synchronization problem to push-down automata and in the following work, to visibly push-down automata. Here, a synchronizing word still needs to map each state of the automaton to one state but it further needs to fulfill some constraints on the stack. We study three different types of stack constraints where after reading the synchronizing word, the stacks associated to each run in the automaton must be (1) empty, (2) identical, or (3) can be arbitrary.
We observe that the synchronization problem for general push-down automata is undecidable and study restricted sub-classes of push-down automata where the problem becomes decidable. For visibly push-down automata we even obtain efficient algorithms for some settings.
The second part of this work studies the intersection non-emptiness problem for DFAs. This problem is related to the problem of whether a given DFA A can be synchronized into a state q as we can see the set of words synchronizing A into q as the intersection of languages accepted by automata obtained by copying A with different initial states and q as their final state.
For the intersection non-emptiness problem, we first study the complexity of the, in general PSPACE-complete, problem restricted to subclasses of DFAs associated with the two well known Straubing-Thérien and Cohen-Brzozowski dot-depth hierarchies.
Finally, we study the problem whether a given minimal DFA A can be represented as the intersection of a finite set of smaller DFAs such that the language L(A) accepted by A is equal to the intersection of the languages accepted by the smaller DFAs. There, we focus on the subclass of permutation and commutative permutation DFAs and improve known complexity bounds.
Algorithmen als Richter
(2022)
Die menschliche Entscheidungsgewalt wird durch algorithmische
Entscheidungssysteme herausgefordert. Verfassungsrechtlich besonders
problematisch ist dies in Bereichen, die das staatliche Handeln betreffen.
Eine herausgehobene Stellung nimmt durch den besonderen Schutz der
Art. 92 ff. GG die rechtsprechende Gewalt ein. Lydia Wolff fragt daher danach, welche Antworten das Grundgesetz auf digitale Veränderungen in diesem Bereich bereithält und wie sich ein Eigenwert menschlicher Entscheidungen in der Rechtsprechung angesichts technischen Wandels darstellen lässt.
Das Werk erörtert hierzu einen Beitrag zum verfassungsrechtlichen
Richterbegriff und stellt diesen etablierten Begriff in einen Kontext neuer digitaler Herausforderungen durch algorithmische Konkurrenz.
Surveys play a major role in studying social and behavioral phenomena that are difficult to
observe. Survey data provide insights into the determinants and consequences of human
behavior and social interactions. Many domains rely on high quality survey data for decision
making and policy implementation including politics, health, business, and the social
sciences. Given a certain research question in a specific context, finding the most appropriate
survey design to ensure data quality and keep fieldwork costs low at the same time is a
difficult task. The aim of examining survey research methodology is to provide the best
evidence to estimate the costs and errors of different survey design options. The goal of this
thesis is to support and optimize the accumulation and sustainable use of evidence in survey
methodology in four steps:
(1) Identifying the gaps in meta-analytic evidence in survey methodology by a systematic
review of the existing evidence along the dimensions of a central framework in the
field
(2) Filling in these gaps with two meta-analyses in the field of survey methodology, one
on response rates in psychological online surveys, the other on panel conditioning
effects for sensitive items
(3) Assessing the robustness and sufficiency of the results of the two meta-analyses
(4) Proposing a publication format for the accumulation and dissemination of metaanalytic
evidence
Der vorliegende Text ist als Mantelpapier im Rahmen einer kumulativen Dissertation an der Universität Trier angenommen worden. Er dient der Zusammenfassung, Reflexion und erweiterten theoretischen Betrachtung der empirischen Einzelbeiträge, die alle einen Einzelaspekt des Gesamtgeschehens „Innovationslabor zur Unterstützung unternehmerischen Lernens und der Entwicklung sozialer Dienstleistungsinnovationen“ behandeln. Dabei wird das Innovationslabor grundsätzlich als Personalentwicklungsmaßnahme aufgefasst. In einem gedanklichen Experiment werden die Ergebnisse auf Organisationen der Erwachsenen- und Weiterbildung übertragen.
Das Besondere dieses Rahmenpapiers ist die Verbindung eines relationalen Raumverständnisses mit der lerntheoretischen Untermauerung des Gegenstandes „Innovationslabor“ aus der Perspektive der Organisationspädagogik und Erwachsenenbildung. Die Ergebnisse zeigen den Lernraum Labor als abseits des Arbeitslebens, als semi-autonom angebundenen Raum, wo Lernprozesse auf unterschiedlichen Ebenen stattfinden und angestoßen werden. Das Labor wird als heterotoper (Lern-)Raum diskutiert. Neu ist auch der Einbezug einer kritischen Perspektive, die bislang im Diskurs um Innovationslabore fehlte: Das Labor wird als prekärer Lernraum charakterisiert. Somit liegt mit dieser Arbeit nun eine grundlegende Ausarbeitung des Labors als Lernraum vor, die zahlreiche weitere Anschlussmöglichkeiten für Forschung ermöglicht.
This socio-pragmatic study investigates organisational conflict talk between superiors and subordinates in three medical dramas from China, Germany and the United States. It explores what types of sociolinguistic realities the medical dramas construct by ascribing linguistic behaviour to different status groups. The study adopts an enhanced analytical framework based on John Gumperz’ discourse strategies and Spencer-Oatey’s rapport management theory. This framework detaches directness from politeness, defines directness based on preference and polarity and explains the use of direct and indirect opposition strategies in context.
The findings reveal that the three hospital series draw on 21 opposition strategies which can be categorised into mitigating, intermediate and intensifying strategies. While the status identity of superiors is commonly characterised by a higher frequency of direct strategies than that of subordinates, both status groups manage conflict in a primarily direct manner across all three hospital shows. The high percentage of direct conflict management is related to the medical context, which is characterised by a focus on transactional goals, complex role obligations and potentially severe consequences of medical mistakes and delays. While the results reveal unexpected similarities between the three series with regard to the linguistic directness level, cross-cultural differences between the Chinese and the two Western series are obvious from particular sociopragmatic conventions. These conventions particularly include the use of humour, imperatives, vulgar language and incorporated verbal and para-verbal/multimodal opposition. Noteworthy differences also appear in the underlying patterns of strategy use. They show that the Chinese series promotes a greater tolerance of hierarchical structures and a partially closer social distance in asymmetrical professional relationships. These disparities are related to different perceptions of power distance, role relationships, face and harmony.
The findings challenge existing stereotypes of Chinese, US American and German conflict management styles and emphasise the context-specific nature of verbal conflict management in every culture. Although cinematic aspects affect the conflict management in the fictional data, the results largely comply with recent research on conflict talk in real-life workplaces. As such, the study contributes to intercultural trainings in medical contexts and provides an enhanced analytical framework for further cross-cultural studies on linguistic strategies.
The ability to acquire knowledge helps humans to cope with the demands of the environment. Supporting knowledge acquisition processes is among the main goals of education. Empirical research in educational psychology has identified several processes mediated through that prior knowledge affects learning. However, the majority of studies investigated cognitive mechanisms mediating between prior knowledge and learning and neglected that motivational processes might also mediate the influence. In addition, the impact of successful knowledge acquisition on patients’ health has not been comprehensively studied. This dissertation aims at closing knowledge gaps on these topics with the use of three studies. The first study is a meta-analysis that examined motivation as a mediator of individual differences in knowledge before and after learning. The second study investigated in greater detail the extent to which motivation mediated the influence of prior knowledge on knowledge gains in a sample of university students. The third study is a second-order meta-analysis synthesizing the results of previous meta-analyses on the effects of patient education on several health outcomes. The findings of this dissertation show that (a) motivation mediates individual differences in knowledge before and after learning; (b) interest and academic self-concept stabilize individual differences in knowledge more than academic self-efficacy, intrinsic motivation, and extrinsic motivation; (c) test-oriented instruction closes knowledge gaps between students; (d) students’ motivation can be independent of prior knowledge in high aptitude students; (e) knowledge acquisition affects motivational and health-related outcomes; and (f) evidence on prior knowledge and motivation can help develop effective interventions in patient education. The results of the dissertation provide insights into prerequisites, processes, and outcomes of knowledge acquisition. Future research should address covariates of learning and environmental impacts for a better understanding of knowledge acquisition processes.