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Institut
- Psychologie (64)
- Fachbereich 4 (50)
- Raum- und Umweltwissenschaften (45)
- Mathematik (44)
- Wirtschaftswissenschaften (26)
- Fachbereich 1 (19)
- Informatik (16)
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- Fachbereich 2 (7)
- Fachbereich 6 (7)
Left ventricular assist devices (LVADs) have become a valuable treatment for patients with advanced heart failure. Women appear to be disadvantaged in the usage of LVADs and concerning clinical outcomes such as death and adverse events after LVAD implant. Contrary to typical clinical characteristics (e.g., disease severity), device-related factors such as the intended device strategy, bridge to a heart transplantation or destination therapy, are often not considered in research on gender differences. In addition, the relevance of pre-implant psychosocial risk factors, such as substance abuse and limited social support, for LVAD outcomes is currently unclear. Thus, the aim of this dissertation is to explore the role of pre-implant psychosocial risk factors for gender differences in clinical outcomes, accounting for clinical and device-related risk factors.
In the first article, gender differences in pre-implant characteristics of patients registered in The European Registry for Patients with Mechanical Circulatory Support (EUROMACS) were investigated. It was found that women and men differed in multiple pre-implant characteristics depending on device strategy. In the second article, gender differences in major clinical outcomes (i.e., death, heart transplant, device explant due to cardiac recovery, device replacement due to complications) were evaluated for patients in the device strategy destination therapy in the Interagency Registry for Mechanically Assisted Circulation (INTERMACS). Additionally, the association of gender and psychosocial risk factors with the major outcomes were analyzed. Women had similar probabilities to die on LVAD support, and even higher probabilities to experience explant of the device due to cardiac recovery compared with men in the destination therapy subgroup. Pre-implant psychosocial risk factors were not associated with major outcomes. The third article focused on gender differences in 10 adverse events (e.g., device malfunction, bleeding) after LVAD implant in INTERMACS. The association of a psychosocial risk indicator with gender and adverse events after LVAD implantation was evaluated. Women were less likely to have psychosocial risk pre-implant but more likely to experience seven out of 10 adverse events compared with men. Pre-implant psychosocial risk was associated with adverse events, even suggesting a dose response-relationship. These associations appeared to be more pronounced in women.
In conclusion, women appear to have similar survival to men when accounting for device strategy. They have higher probabilities of recovery, but higher probabilities of device replacement and adverse events compared with men. Regarding these adverse events, women may be more susceptible to psychosocial risk factors than men. The results of this dissertation illustrate the importance of gender-sensitive research and suggest considering device strategy when studying gender differences in LVAD recipients. Further research is warranted to elucidate the role of specific psychosocial risk factors that lead to higher probabilities of adverse events, to intervene early and improve patient care in both, women and men
This thesis contains four parts that are all connected by their contributions to the Efficient Market Hypothesis and decision-making literature. Chapter two investigates how national stock market indices reacted to the news of national lockdown restrictions in the period from January to May 2020. The results show that lockdown restrictions led to different reactions in a sample of OECD and BRICS countries: there was a general negative effect resulting from the increase in lockdown restrictions, but the study finds strong evidence for underreaction during the lockdown announcement, followed by some overreaction that is corrected subsequently. This under-/overreaction pattern, however, is observed mostly during the first half of our time series, pointing to learning effects. Relaxation of the lockdown restrictions, on the other hand, had a positive effect on markets only during the second half of our sample, while for the first half of the sample, the effect was negative. The third chapter investigates the gender differences in stock selection preferences on the Taiwan Stock Exchange. By utilizing trading data from the Taiwan Stock Exchange over a span of six years, it becomes possible to analyze trading behavior while minimizing the self-selection bias that is typically present in brokerage data. To study gender differences, this study uses firm-level data. The percentage of male traders in a company is the dependent variable, while the company’s industry and fundamental/technical aspects serve as independent variables. The results show that the percentage of women trading a company rises with a company’s age, market capitalization, a company’s systematic risk, and return. Men trade more frequently and show a preference for dividend-paying stocks and for industries with which they are more familiar. The fourth chapter investigated the relationship between regret and malicious and benign envy. The relationship is analyzed in two different studies. In experiment 1, subjects had to fill out psychological scales that measured regret, the two types of envy, core self-evaluation and the big 5 personality traits. In experiment 2, felt regret is measured in a hypothetical scenario, and the subject’s felt regret was regressed on the other variables mentioned above. The two experiments revealed that there is a positive direct relationship between regret and benign envy. The relationship between regret and malicious envy, on the other hand, is mostly an artifact of core self-evaluation and personality influencing both malicious envy and regret. The relationship can be explained by the common action tendency of self-improvement for regret and benign envy. Chapter five discusses the differences in green finance regulation and implementation between the EU and China. China introduced the Green Silk Road, while the EU adopted the Green Deal and started working with its own green taxonomy. The first difference comes from the definition of green finance, particularly with regard to coal-fired power plants. Especially the responsibility of nation-states’ emissions abroad. China is promoting fossil fuel projects abroad through its Belt and Road Initiative, but the EU’s Green Deal does not permit such actions. Furthermore, there are policies in both the EU and China that create contradictory incentives for economic actors. On the one hand, the EU and China are improving the framework conditions for green financing while, on the other hand, still allowing the promotion of conventional fuels. The role of central banks is also different between the EU and China. China’s central bank is actively working towards aligning the financial sector with green finance. A possible new role of the EU central bank or the priority financing of green sectors through political decision-making is still being debated.
Knowledge acquisition comprises various processes. Each of those has its dedicated research domain. Two examples are the relations between knowledge types and the influences of person-related variables. Furthermore, the transfer of knowledge is another crucial domain in educational research. I investigated these three processes through secondary analyses in this dissertation. Secondary analyses comply with the broadness of each field and yield the possibility of more general interpretations. The dissertation includes three meta-analyses: The first meta-analysis reports findings on the predictive relations between conceptual and procedural knowledge in mathematics in a cross-lagged panel model. The second meta-analysis focuses on the mediating effects of motivational constructs on the relationship between prior knowledge and knowledge after learning. The third meta-analysis deals with the effect of instructional methods in transfer interventions on knowledge transfer in school students. These three studies provide insights into the determinants and processes of knowledge acquisition and transfer. Knowledge types are interrelated; motivation mediates the relation between prior and later knowledge, and interventions influence knowledge transfer. The results are discussed by examining six key insights that build upon the three studies. Additionally, practical implications, as well as methodological and content-related ideas for further research, are provided.
When humans encounter attitude objects (e.g., other people, objects, or constructs), they evaluate them. Often, these evaluations are based on attitudes. Whereas most research focuses on univalent (i.e., only positive or only negative) attitude formation, little research exists on ambivalent (i.e., simultaneously positive and negative) attitude formation. Following a general introduction into ambivalence, I present three original manuscripts investigating ambivalent attitude formation. The first manuscript addresses ambivalent attitude formation from previously univalent attitudes. The results indicate that responding to a univalent attitude object incongruently leads to ambivalence measured via mouse tracking but not ambivalence measured via self-report. The second manuscript addresses whether the same number of positive and negative statements presented block-wise in an impression formation task leads to ambivalence. The third manuscript also used an impression formation task and addresses the question of whether randomly presenting the same number of positive and negative statements leads to ambivalence. Additionally, the effect of block size of the same valent statements is investigated. The results of the last two manuscripts indicate that presenting all statements of one valence and then all statements of the opposite valence leads to ambivalence measured via self-report and mouse tracking. Finally, I discuss implications for attitude theory and research as well as future research directions.
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.
Social entrepreneurship is a successful activity to solve social problems and economic
challenges. Social entrepreneurship uses for-profit industry techniques and tools to build
financially sound businesses that provide nonprofit services. Social entrepreneurial activities
also lead to the achievement of sustainable development goals. However, due to the complex,
hybrid nature of the business, social entrepreneurial activities are typically supported by macrolevel
determinants. To expand our knowledge of how beneficial macro-level determinants can
be, this work examines empirical evidence about the impact of macro-level determinants on
social entrepreneurship. Another aim of this dissertation is to examine the impact at the micro
level, as the growth ambitions of social and commercial entrepreneurs differ. At the beginning,
the introductory section is explained in Chapter 1, which contains the motivation for the
research, the research question, and the structure of the work.
There is an ongoing debate about the origin and definition of social entrepreneurship.
Therefore, the numerous phenomena of social entrepreneurship are examined theoretically in
the previous literature. To determine the common consensus on the topic, Chapter 2 presents
the theoretical foundations and definition of social entrepreneurship. The literature shows that
a variety of determinants at the micro and macro levels are essential for the emergence of social
entrepreneurship as a distinctive business model (Hartog & Hoogendoorn, 2011; Stephan et
al., 2015; Hoogendoorn, 2016). It is impossible to create a society based on a social mission without the support of micro and macro-level-level determinants. This work examines the
determinants and consequences of social entrepreneurship from different methodological
perspectives. The theoretical foundations of the micro- and macro-level determinants
influencing social entrepreneurial activities were discussed in Chapter 3
The purpose of reproducibility in research is to confirm previously published results
(Hubbard et al., 1998; Aguinis & Solarino, 2019). However, due to the lack of data, lack of
transparency of methodology, reluctance to publish, and lack of interest from researchers, there
is a lack of promoting replication of the existing research study (Baker, 2016; Hedges &
Schauer, 2019a). Promoting replication studies has been regularly emphasized in the business
and management literature (Kerr et al., 2016; Camerer et al., 2016). However, studies that
provide replicability of the reported results are considered rare in previous research (Burman
et al., 2010; Ryan & Tipu, 2022). Based on the research of Köhler and Cortina (2019), an
empirical study on this topic is carried out in Chapter 4 of this work.
Given this focus, researchers have published a large body of research on the impact of microand
macro-level determinants on social inclusion, although it is still unclear whether these
studies accurately reflect reality. It is important to provide conceptual underpinnings to the
field through a reassessment of published results (Bettis et al., 2016). The results of their
research make it abundantly clear that the macro determinants support social entrepreneurship.
In keeping with the more narrative approach, which is a crucial concern and requires attention,
Chapter 5 considered the reproducibility of previous results, particularly on the topic of social
entrepreneurship. We replicated the results of Stephan et al. (2015) to establish the trend of
reproducibility and validate the specific conclusions they drew. The literal and constructive
replication in the dissertation inspired us to explore technical replication research on social
entrepreneurship. Chapter 6 evaluates the fundamental characteristics that have proven to be key factors in the
growth of social ventures. The current debate reviews and references literature that has
specifically focused on the development of social entrepreneurship. An empirical analysis of
factors directly related to the ambitious growth of social entrepreneurship is also carried out.
Numerous social entrepreneurial groups have been studied concerning this association. Chapter
6 compares the growth ambitions of social and traditional (commercial) entrepreneurship as
consequences at the micro level. This study examined many characteristics of social and
commercial entrepreneurs' growth ambitions. Scholars have claimed to some extent that the
growth of social entrepreneurship differs from commercial entrepreneurial activities due to
objectivity differences (Lumpkin et al., 2013; Garrido-Skurkowicz et al., 2022). Qualitative
research has been used in studies to support the evidence on related topics, including Gupta et
al (2020) emphasized that research needs to focus on specific concepts of social
entrepreneurship for the field to advance. Therefore, this study provides a quantitative,
analysis-based assessment of facts and data. For this purpose, a data set from the Global
Entrepreneurship Monitor (GEM) 2015 was used, which examined 12,695 entrepreneurs from
38 countries. Furthermore, this work conducted a regression analysis to evaluate the influence
of various social and commercial characteristics of entrepreneurship on economic growth in
developing countries. Chapter 7 briefly explains future directions and practical/theoretical
implications.
Allocating scarce resources efficiently is a major task in mechanism design. One of the most fundamental problems in mechanism design theory is the problem of selling a single indivisible item to bidders with private valuations for the item. In this setting, the classic Vickrey auction of~\citet{vickrey1961} describes a simple mechanism to implement a social welfare maximizing allocation.
The Vickrey auction for a single item asks every buyer to report its valuation and allocates the item to the highest bidder for a price of the second highest bid. This auction features some desirable properties, e.g., buyers cannot benefit from misreporting their true value for the item (incentive compatibility) and the auction can be executed in polynomial time.
However, when there is more than one item for sale and buyers' valuations for sets of items are not additive or the set of feasible allocations is constrained, then constructing mechanisms that implement efficient allocations and have polynomial runtime might be very challenging. Consider a single seller selling $n\in \N$ heterogeneous indivisible items to several bidders. The Vickrey-Clarke-Groves auction generalizes the idea of the Vickrey auction to this multi-item setting. Naturally, every bidder has an intrinsic value for every subset of items. As in in the Vickrey auction, bidders report their valuations (Now, for every subset of items!). Then, the auctioneer computes a social welfare maximizing allocation according to the submitted bids and charges buyers the social cost of their winning that is incurred by the rest of the buyers. (This is the analogue to charging the second highest bid to the winning bidder in the single item Vickrey auction.) It turns out that the Vickrey-Clarke-Groves auction is also incentive compatible but it poses some problems: In fact, say for $n=40$, bidders would have to submit $2^{40}-1$ values (one value for each nonempty subset of the ground set) in total. Thus, asking every bidder for its valuation might be impossible due to time complexity issues. Therefore, even though the Vickrey-Clarke-Groves auction implements a social welfare maximizing allocation in this multi-item setting it might be impractical and there is need for alternative approaches to implement social welfare maximizing allocations.
This dissertation represents the results of three independent research papers all of them tackling the problem of implementing efficient allocations in different combinatorial settings.
This cumulative thesis encompass three studies focusing on the Weddell Sea region in the Antarctic. The first study produces and evaluates a high quality data set of wind measurements for this region. The second study produces and evaluates a 15 year regional climate simulation for the Weddell Sea region. And the third study produces and evaluates a climatology of low level jets (LLJs) from the simulation data set. The evaluations were done in the attached three publications and the produced data sets are published online.
In 2015/2016, the RV Polarstern undertook an Antarctic expedition in the Weddell Sea. We operated a Doppler wind lidar on board during that time running different scan patterns. The resulting data was evaluated, corrected, processed and we derived horizontal wind speed and directions for vertical profiles with up to 2 km height. The measurements cover 38 days with a temporal resolution of 10-15 minutes. A comparisons with other radio sounding data showed only minor differences.
The resulting data set was used alongside other measurements to evaluate temperature and wind of simulation data. The simulation data was produced with the regional climate model CCLM for the period of 2002 to 2016 for the Weddell Sea region. Only smaller biases were found except for a strong warm bias during winter near the surface of the Antarctic Plateau. Thus we adapted the model setup and were able to remove the bias in a second simulation.
This new simulation data was then used to derive a climatology of low level jets (LLJs). Statistics of occurrence frequency, height and wind speed of LLJs for the Weddell Sea region are presented along other parameters. Another evaluation with measurements was also performed in the last study.
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
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.
Official business surveys form the basis for national and regional business statistics and are thus of great importance for analysing the state and performance of the economy. However, both the heterogeneity of business data and their high dynamics pose a particular challenge to the feasibility of sampling and the quality of the resulting estimates. A widely used sampling frame for creating the design of an official business survey is an extract from an official business register. However, if this frame does not accurately represent the target population, frame errors arise. Amplified by the heterogeneity and dynamics of business populations, these errors can significantly affect the estimation quality and lead to inefficiencies and biases. This dissertation therefore deals with design-based methods for optimising business surveys with respect to different types of frame errors.
First, methods for adjusting the sampling design of business surveys are addressed. These approaches integrate auxiliary information about the expected structures of frame errors into the sampling design. The aim is to increase the number of sampled businesses that are subject to frame errors. The element-specific frame error probability is estimated based on auxiliary information about frame errors observed in previous samples. The approaches discussed consider different types of frame errors and can be incorporated into predefined designs with fixed strata.
As the second main pillar of this work, methods for adjusting weights to correct for frame errors during estimation are developed and investigated. As a result of frame errors, the assumptions under which the original design weights were determined based on the sampling design no longer hold. The developed methods correct the design weights taking into account the errors identified for sampled elements. Case-number-based reweighting approaches, on the one hand, attempt to reconstruct the unknown size of the individual strata in the target population. In the context of weight smoothing methods, on the other hand, design weights are modelled and smoothed as a function of target or auxiliary variables. This serves to avoid inefficiencies in the estimation due to highly scattering weights or weak correlations between weights and target variables. In addition, possibilities of correcting frame errors by calibration weighting are elaborated. Especially when the sampling frame shows over- and/or undercoverage, the inclusion of external auxiliary information can provide a significant improvement of the estimation quality. For those methods whose quality cannot be measured using standard procedures, a procedure for estimating the variance based on a rescaling bootstrap is proposed. This enables an assessment of the estimation quality when using the methods in practice.
In the context of two extensive simulation studies, the methods presented in this dissertation are evaluated and compared with each other. First, in the environment of an experimental simulation, it is assessed which approaches are particularly suitable with regard to different data situations. In a second simulation study, which is based on the structural survey in the services sector, the applicability of the methods in practice is evaluated under realistic conditions.
Wasserbezogene regulierende und versorgende Ökosystemdienstleistungen (ÖSDL) wurden im Hinblick auf das Abflussregime und die Grundwasserneubildung im Biosphärenreservat Pfälzerwald im Südwesten Deutschlands anhand hydrologischer Modellierung unter Verwendung des Soil and Water Assessment Tool (SWAT+) untersucht. Dabei wurde ein holistischer Ansatz verfolgt, wonach den ÖSDL Indikatoren für funktionale und strukturelle ökologische Prozesse zugeordnet werden. Potenzielle Risikofaktoren für die Verschlechterung von wasserbedingten ÖSDL des Waldes, wie Bodenverdichtung durch Befahren mit schweren Maschinen im Zuge von Holzerntearbeiten, Schadflächen mit Verjüngung, entweder durch waldbauliche Bewirtschaftungspraktiken oder durch Windwurf, Schädlinge und Kalamitäten im Zuge des Klimawandels, sowie der Kli-mawandel selbst als wesentlicher Stressor für Waldökosysteme wurden hinsichtlich ihrer Auswirkungen auf hydrologische Prozesse analysiert. Für jeden dieser Einflussfaktoren wurden separate SWAT+-Modellszenarien erstellt und mit dem kalibrierten Basismodell verglichen, das die aktuellen Wassereinzugsgebietsbedingungen basierend auf Felddaten repräsentierte. Die Simulationen bestätigten günstige Bedingungen für die Grundwasserneubildung im Pfälzerwald. Im Zusammenhang mit der hohen Versickerungskapazität der Bodensubstrate der Buntsandsteinverwitterung, sowie dem verzögernden und puffernden Einfluss der Baumkronen auf das Niederschlagswasser, wurde eine signifikante Minderungswirkung auf die Oberflächenabflussbildung und ein ausgeprägtes räumliches und zeitliches Rückhaltepotential im Einzugsgebiet simuliert. Dabei wurde festgestellt, dass erhöhte Niederschlagsmengen, die die Versickerungskapazität der sandigen Böden übersteigen, zu einer kurz geschlossenen Abflussreaktion mit ausgeprägten Oberflächenabflussspitzen führen. Die Simulationen zeigten Wechselwirkungen zwischen Wald und Wasserkreislauf sowie die hydrologische Wirksamkeit des Klimawandels, verschlechterter Bodenfunktionen und altersbezogener Bestandesstrukturen im Zusammenhang mit Unterschieden in der Baumkronenausprägung. Zukunfts-Klimaprojektionen, die mit BIAS-bereinigten REKLIES- und EURO-CORDEX-Regionalklimamodellen (RCM) simuliert wurden, prognostizierten einen höheren Verdunstungsbedarf und eine Verlängerung der Vegetationsperiode bei gleichzeitig häufiger auftretenden Dürreperioden innerhalb der Vegetationszeit, was eine Verkürzung der Periode für die Grundwasserneubildung induzierte, und folglich zu einem prognostizierten Rückgang der Grundwasserneubildungsrate bis zur Mitte des Jahrhunderts führte. Aufgrund der starken Korrelation mit Niederschlagsintensitäten und der Dauer von Niederschlagsereignissen, bei allen Unsicherheiten in ihrer Vorhersage, wurde für die Oberflächenabflussgenese eine Steigerung bis zum Ende des Jahrhunderts prognostiziert.
Für die Simulation der Bodenverdichtung wurden die Trockenrohdichte des Bodens und die SCS Curve Number in SWAT+ gemäß Daten aus Befahrungsversuchen im Gebiet angepasst. Die günstigen Infiltrationsbedingungen und die relativ geringe Anfälligkeit für Bodenverdichtung der grobkörnigen Buntsandsteinverwitterung dominierten die hydrologischen Auswirkungen auf Wassereinzugsgebietsebene, sodass lediglich moderate Verschlechterungen wasserbezogener ÖSDL angezeigt wurden. Die Simulationen zeigten weiterhin einen deutlichen Einfluss der Bodenart auf die hydrologische Reaktion nach Bodenverdichtung auf Rückegassen und stützen damit die Annahme, dass die Anfälligkeit von Böden gegenüber Verdichtung mit dem Anteil an Schluff- und Tonbodenpartikeln zunimmt. Eine erhöhte Oberflächenabflussgenese ergab sich durch das Wegenetz im Gesamtgebiet.
Schadflächen mit Bestandesverjüngung wurden anhand eines artifiziellen Modells innerhalb eines Teileinzugsgebiets unter der Annahme von 3-jährigen Baumsetzlingen in einem Entwicklungszeitraum von 10 Jahren simuliert und hinsichtlich spezifischer Was-serhaushaltskomponenten mit Altbeständen (30 bis 80 Jahre) verglichen. Die Simulation ließ darauf schließen, dass bei fehlender Kronenüberschirmung die hydrologisch verzögernde Wirkung der Bestände beeinträchtigt wird, was die Entstehung von Oberflächenabfluss begünstigt und eine quantitativ geringfügig höhere Tiefensickerung fördert. Hydrologische Unterschiede zwischen dem geschlossenem Kronendach der Altbestände und Jungbeständen mit annähernden Freilandniederschlagsbedingungen wurden durch die dominierenden Faktoren atmosphärischer Verdunstungsanstoß, Niederschlagsmengen und Kronenüberschirmungsgrad bestimmt. Je weniger entwickelt das Kronendach von verjüngten Waldbeständen im Vergleich zu Altbeständen, je höher der atmosphärische Verdunstungsanstoß und je geringer die eingetragenen Niederschlagsmengen, desto größer war der hydrologische Unterschied zwischen den Bestandestypen.
Verbesserungsmaßnahmen für den dezentralen Hochwasserschutz sollten folglich kritische Bereiche für die Abflussbildung im Wald (CSA) berücksichtigen. Die hohe Sensibilität und Anfälligkeit der Wälder gegenüber Verschlechterungen der Ökosystembedingungen legen nahe, dass die Erhaltung des komplexen Gefüges und von intakten Wechselbeziehungen, insbesondere unter der gegebenen Herausforderung des Klimawandels, sorgfältig angepasste Schutzmaßnahmen, Anstrengungen bei der Identifizierung von CSA sowie die Erhaltung und Wiederherstellung der hydrologischen Kontinuität in Waldbeständen erfordern.
Traditional workflow management systems support process participants in fulfilling business tasks through guidance along a predefined workflow model.
Flexibility has gained a lot of attention in recent decades through a shift from mass production to customization. Various approaches to workflow flexibility exist that either require extensive knowledge acquisition and modelling effort or an active intervention during execution and re-modelling of deviating behaviour. The pursuit of flexibility by deviation is to compensate both of these disadvantages through allowing alternative unforeseen execution paths at run time without demanding the process participant to adapt the workflow model. However, the implementation of this approach has been little researched so far.
This work proposes a novel approach to flexibility by deviation. The approach aims at supporting process participants during the execution of a workflow through suggesting work items based on predefined strategies or experiential knowledge even in case of deviations. The developed concepts combine two renowned methods from the field of artificial intelligence - constraint satisfaction problem solving with process-oriented case-based reasoning. This mainly consists of a constraint-based workflow engine in combination with a case-based deviation management. The declarative representation of workflows through constraints allows for implicit flexibility and a simple possibility to restore consistency in case of deviations. Furthermore, the combined model, integrating procedural with declarative structures through a transformation function, increases the capabilities for flexibility. For an adequate handling of deviations the methodology of case-based reasoning fits perfectly, through its approach that similar problems have similar solutions. Thus, previous made experiences are transferred to currently regarded problems, under the assumption that a similar deviation has been handled successfully in the past.
Necessary foundations from the field of workflow management with a focus on flexibility are presented first.
As formal foundation, a constraint-based workflow model was developed that allows for a declarative specification of foremost sequential dependencies of tasks. Procedural and declarative models can be combined in the approach, as a transformation function was specified that converts procedural workflow models to declarative constraints.
One main component of the approach is the constraint-based workflow engine that utilizes this declarative model as input for a constraint solving algorithm. This algorithm computes the worklist, which is proposed to the process participant during workflow execution. With predefined deviation handling strategies that determine how the constraint model is modified in order to restore consistency, the support is continuous even in case of deviations.
The second major component of the proposed approach constitutes the case-based deviation management, which aims at improving the support of process participants on the basis of experiential knowledge. For the retrieve phase, a sophisticated similarity measure was developed that integrates specific characteristics of deviating workflows and combines several sequence similarity measures. Two alternative methods for the reuse phase were developed, a null adaptation and a generative adaptation. The null adaptation simply proposes tasks from the most similar workflow as work items, whereas the generative adaptation modifies the constraint-based workflow model based on the most similar workflow in order to re-enable the constraint-based workflow engine to suggest work items.
The experimental evaluation of the approach consisted of a simulation of several types of process participants in the exemplary domain of deficiency management in construction. The results showed high utility values and a promising potential for an investigation of the transfer on other domains and the applicability in practice, which is part of future work.
Concluding, the contributions are summarized and research perspectives are pointed out.
Energy transport networks are one of the most important infrastructures for the planned energy transition. They form the interface between energy producers and consumers and their features make them good candidates for the tools that mathematical optimization can offer. Nevertheless, the operation of energy networks comes with two major challenges. First, the nonconvexity of the equations that model the physics in the network render the resulting problems extremely hard to solve for large-scale networks. Second, the uncertainty associated to the behavior of the different agents involved, the production of energy, and the consumption of energy make the resulting problems hard to solve if a representative description of uncertainty is to be considered.
In this cumulative dissertation we study adaptive refinement algorithms designed to cope with the nonconvexity and stochasticity of equations arising in energy networks. Adaptive refinement algorithms approximate the original problem by sequentially refining the model of a simpler optimization problem. More specifically, in this thesis, the focus of the adaptive algorithm is on adapting the discretization and description of a set of constraints.
In the first part of this thesis, we propose a generalization of the different adaptive refinement ideas that we study. We sequentially describe model catalogs, error measures, marking strategies, and switching strategies that are used to set up the adaptive refinement algorithm. Afterward, the effect of the adaptive refinement algorithm on two energy network applications is studied. The first application treats the stationary operation of district heating networks. Here, the strength of adaptive refinement algorithms for approximating the ordinary differential equation that describes the transport of energy is highlighted. We introduce the resulting nonlinear problem, consider network expansion, and obtain realistic controls by applying the adaptive refinement algorithm. The second application concerns quantile-constrained optimization problems and highlights the ability of the adaptive refinement algorithm to cope with large scenario sets via clustering. We introduce the resulting mixed-integer linear problem, discuss generic solution techniques, make the link with the generalized framework, and measure the impact of the proposed solution techniques.
The second part of this thesis assembles the papers that inspired the contents of the first part of this thesis. Hence, they describe in detail the topics that are covered and will be referenced throughout the first part.
THE NONLOCAL NEUMANN PROBLEM
(2023)
Instead of presuming only local interaction, we assume nonlocal interactions. By doing so, mass
at a point in space does not only interact with an arbitrarily small neighborhood surrounding it,
but it can also interact with mass somewhere far, far away. Thus, mass jumping from one point to
another is also a possibility we can consider in our models. So, if we consider a region in space, this
region interacts in a local model at most with its closure. While in a nonlocal model this region may
interact with the whole space. Therefore, in the formulation of nonlocal boundary value problems
the enforcement of boundary conditions on the topological boundary may not suffice. Furthermore,
choosing the complement as nonlocal boundary may work for Dirichlet boundary conditions, but
in the case of Neumann boundary conditions this may lead to an overfitted model.
In this thesis, we introduce a nonlocal boundary and study the well-posedness of a nonlocal Neu-
mann problem. We present sufficient assumptions which guarantee the existence of a weak solution.
As in a local model our weak formulation is derived from an integration by parts formula. However,
we also study a different weak formulation where the nonlocal boundary conditions are incorporated
into the nonlocal diffusion-convection operator.
After studying the well-posedness of our nonlocal Neumann problem, we consider some applications
of this problem. For example, we take a look at a system of coupled Neumann problems and analyze
the difference between a local coupled Neumann problems and a nonlocal one. Furthermore, we let
our Neumann problem be the state equation of an optimal control problem which we then study. We
also add a time component to our Neumann problem and analyze this nonlocal parabolic evolution
equation.
As mentioned before, in a local model mass at a point in space only interacts with an arbitrarily
small neighborhood surrounding it. We analyze what happens if we consider a family of nonlocal
models where the interaction shrinks so that, in limit, mass at a point in space only interacts with
an arbitrarily small neighborhood surrounding it.
Survey data can be viewed as incomplete or partially missing from a variety of perspectives and there are different ways of dealing with this kind of data in the prediction and the estimation of economic quantities. In this thesis, we present two selected research contexts in which the prediction or estimation of economic quantities is examined under incomplete survey data.
These contexts are first the investigation of composite estimators in the German Microcensus (Chapters 3 and 4) and second extensions of multivariate Fay-Herriot (MFH) models (Chapters 5 and 6), which are applied to small area problems.
Composite estimators are estimation methods that take into account the sample overlap in rotating panel surveys such as the German Microcensus in order to stabilise the estimation of the statistics of interest (e.g. employment statistics). Due to the partial sample overlaps, information from previous samples is only available for some of the respondents, so the data are partially missing.
MFH models are model-based estimation methods that work with aggregated survey data in order to obtain more precise estimation results for small area problems compared to classical estimation methods. In these models, several variables of interest are modelled simultaneously. The survey estimates of these variables, which are used as input in the MFH models, are often partially missing. If the domains of interest are not explicitly accounted for in a sampling design, the sizes of the samples allocated to them can, by chance, be small. As a result, it can happen that either no estimates can be calculated at all or that the estimated values are not published by statistical offices because their variances are too large.
Coastal erosion describes the displacement of land caused by destructive sea waves,
currents or tides. Due to the global climate change and associated phenomena such as
melting polar ice caps and changing current patterns of the oceans, which result in rising
sea levels or increased current velocities, the need for countermeasures is continuously
increasing. Today, major efforts have been made to mitigate these effects using groins,
breakwaters and various other structures.
This thesis will find a novel approach to address this problem by applying shape optimization
on the obstacles. Due to this reason, results of this thesis always contain the
following three distinct aspects:
The selected wave propagation model, i.e. the modeling of wave propagation towards
the coastline, using various wave formulations, ranging from steady to unsteady descriptions,
described from the Lagrangian or Eulerian viewpoint with all its specialties. More
precisely, in the Eulerian setting is first a steady Helmholtz equation in the form of a
scattering problem investigated and followed subsequently by shallow water equations,
in classical form, equipped with porosity, sediment portability and further subtleties.
Secondly, in a Lagrangian framework the Lagrangian shallow water equations form the
center of interest.
The chosen discretization, i.e. dependent on the nature and peculiarity of the constraining
partial differential equation, we choose between finite elements in conjunction
with a continuous Galerkin and discontinuous Galerkin method for investigations in the
Eulerian description. In addition, the Lagrangian viewpoint offers itself for mesh-free,
particle-based discretizations, where smoothed particle hydrodynamics are used.
The method for shape optimization w.r.t. the obstacle’s shape over an appropriate
cost function, constrained by the solution of the selected wave-propagation model. In
this sense, we rely on a differentiate-then-discretize approach for free-form shape optimization
in the Eulerian set-up, and reverse the order in Lagrangian computations.
Behavioural traces from interactions with digital technologies are diverse and abundant. Yet, their capacity for theory-driven research is still to be constituted. In the present cumulative dissertation project, I deliberate the caveats and potentials of digital behavioural trace data in behavioural and social science research. One use case is online radicalisation research. The three studies included, set out to discern the state-of-the-art of methods and constructs employed in radicalization research, at the intersection of traditional methods and digital behavioural trace data. Firstly, I display, based on a systematic literature review of empirical work, the prevalence of digital behavioural trace data across different research strands and discern determinants and outcomes of radicalisation constructs. Secondly, I extract, based on this literature review, hypotheses and constructs and integrate them to a framework from network theory. This graph of hypotheses, in turn, makes the relative importance of theoretical considerations explicit. One implication of visualising the assumptions in the field is to systematise bottlenecks for the analysis of digital behavioural trace data and to provide the grounds for the genesis of new hypotheses. Thirdly, I provide a proof-of-concept for incorporating a theoretical framework from conspiracy theory research (as a specific form of radicalisation) and digital behavioural traces. I argue for marrying theoretical assumptions derived from temporal signals of posting behaviour and semantic meaning from textual content that rests on a framework from evolutionary psychology. In the light of these findings, I conclude by discussing important potential biases at different stages in the research cycle and practical implications.
No Longer Printing the Legend: The Aporia of Heteronormativity in the American Western (1903-1969)
(2023)
This study critically investigates the U.S.-American Western and its construction of sexuality and gender, revealing that the heteronormative matrix that is upheld and defended in the genre is consistently preceded by the exploration of alternative sexualities and ways to think gender beyond the binary. The endeavor to naturalize heterosexuality seems to be baked in the formula of the U.S.-Western. However, as I show in this study, this endeavor relies on an aporia, because the U.S.-Western can only ever attempt to naturalize gender by constructing it first, hence inevitably and simultaneously construct evidence that supports the opposite: the unnaturalness and contingency of gender and sexuality.
My study relies on the works of Raewyn Connell, Pierre Bourdieu, and Judith Butler, and amalgamates in its methodology established approaches from film and literary studies (i.e., close readings) with a Foucaultian understanding of discourse and discourse analysis, which allows me to relate individual texts to cultural, socio-political and economical contexts that invariably informed the production and reception of any filmic text. In an analysis of 14 U.S.-Westerns (excluding three excursions) that appeared between 1903 and 1969 I give ample and minute narrative and film-aesthetical evidence to reveal the complex and contradictory construction of gender and sexuality in the U.S.-Western, aiming to reveal both the normative power of those categories and its structural instability and inconsistency.
This study proofs that the Western up until 1969 did not find a stable pattern to represent the gender binary. The U.S.-Western is not necessarily always looking to confirm or stabilize governing constructs of (gendered) power. However, it without fail explores and negotiates its legitimacy. Heterosexuality and male hegemony are never natural, self-evident, incontestable, or preordained. Quite conversely: the U.S.-Western repeatedly – and in a surprisingly diverse and versatile way – reveals the illogical constructedness of the heteronormative matrix.
My study therefore offers a fresh perspective on the genre and shows that the critical exploration and negotiation of the legitimacy of heteronormativity as a way to organize society is constitutive for the U.S.-Western. It is the inquiry – not necessarily the affirmation – of the legitimacy of this model that gives the U.S.-Western its ideological currency and significance as an artifact of U.S.-American popular culture.
Non-probability sampling is a topic of growing relevance, especially due to its occurrence in the context of new emerging data sources like web surveys and Big Data.
This thesis addresses statistical challenges arising from non-probability samples, where unknown or uncontrolled sampling mechanisms raise concerns in terms of data quality and representativity.
Various methods to quantify and reduce the potential selectivity and biases of non-probability samples in estimation and inference are discussed. The thesis introduces new forms of prediction and weighting methods, namely
a) semi-parametric artificial neural networks (ANNs) that integrate B-spline layers with optimal knot positioning in the general structure and fitting procedure of artificial neural networks, and
b) calibrated semi-parametric ANNs that determine weights for non-probability samples by integrating an ANN as response model with calibration constraints for totals, covariances and correlations.
Custom-made computational implementations are developed for fitting (calibrated) semi-parametric ANNs by means of stochastic gradient descent, BFGS and sequential quadratic programming algorithms.
The performance of all the discussed methods is evaluated and compared for a bandwidth of non-probability sampling scenarios in a Monte Carlo simulation study as well as an application to a real non-probability sample, the WageIndicator web survey.
Potentials and limitations of the different methods for dealing with the challenges of non-probability sampling under various circumstances are highlighted. It is shown that the best strategy for using non-probability samples heavily depends on the particular selection mechanism, research interest and available auxiliary information.
Nevertheless, the findings show that existing as well as newly proposed methods can be used to ease or even fully counterbalance the issues of non-probability samples and highlight the conditions under which this is possible.
Modern decision making in the digital age is highly driven by the massive amount of
data collected from different technologies and thus affects both individuals as well as
economic businesses. The benefit of using these data and turning them into knowledge
requires appropriate statistical models that describe the underlying observations well.
Imposing a certain parametric statistical model goes along with the need of finding
optimal parameters such that the model describes the data best. This often results in
challenging mathematical optimization problems with respect to the model’s parameters
which potentially involve covariance matrices. Positive definiteness of covariance matrices
is required for many advanced statistical models and these constraints must be imposed
for standard Euclidean nonlinear optimization methods which often results in a high
computational effort. As Riemannian optimization techniques proved efficient to handle
difficult matrix-valued geometric constraints, we consider optimization over the manifold
of positive definite matrices to estimate parameters of statistical models. The statistical
models treated in this thesis assume that the underlying data sets used for parameter
fitting have a clustering structure which results in complex optimization problems. This
motivates to use the intrinsic geometric structure of the parameter space. In this thesis,
we analyze the appropriateness of Riemannian optimization over the manifold of positive
definite matrices on two advanced statistical models. We establish important problem-
specific Riemannian characteristics of the two problems and demonstrate the importance
of exploiting the Riemannian geometry of covariance matrices based on numerical studies.
Even though proper research on Cauchy transforms has been done, there are still a lot of open questions. For example, in the case of representation theorems, i.e. the question when a function can be represented as a Cauchy transform, there is 'still no completely satisfactory answer' ([9], p. 84). There are characterizations for measures on the circle as presented in the monograph [7] and for general compactly supported measures on the complex plane as presented in [27]. However, there seems to exist no systematic treatise of the Cauchy transform as an operator on $L_p$ spaces and weighted $L_p$ spaces on the real axis.
This is the point where this thesis draws on and we are interested in developing several characterizations for the representability of a function by Cauchy transforms of $L_p$ functions. Moreover, we will attack the issue of integrability of Cauchy transforms of functions and measures, a topic which is only partly explored (see [43]). We will develop different approaches involving Fourier transforms and potential theory and investigate into sufficient conditions and characterizations.
For our purposes, we shall need some notation and the concept of Hardy spaces which will be part of the preliminary Chapter 1. Moreover, we introduce Fourier transforms and their complex analogue, namely Fourier-Laplace transforms. This will be of extraordinary usage due to the close connection of Cauchy and Fourier(-Laplace) transforms.
In the second chapter we shall begin our research with a discussion of the Cauchy transformation on the classical (unweighted) $L_p$ spaces. Therefore, we start with the boundary behavior of Cauchy transforms including an adapted version of the Sokhotski-Plemelj formula. This result will turn out helpful for the determination of the image of the Cauchy transformation under $L_p(\R)$ for $p\in(1,\infty).$ The cases $p=1$ and $p=\infty$ are playing special roles here which justifies a treatise in separate sections. For $p=1$ we will involve the real Hardy space $H_{1}(\R)$ whereas the case $p=\infty$ shall be attacked by an approach incorporating intersections of Hardy spaces and certain subspaces of $L_{\infty}(\R).$
The third chapter prepares ourselves for the study of the Cauchy transformation on subspaces of $L_{p}(\R).$ We shall give a short overview of the basic facts about Cauchy transforms of measures and then proceed to Cauchy transforms of functions with support in a closed set $X\subset\R.$ Our goal is to build up the main theory on which we can fall back in the subsequent chapters.
The fourth chapter deals with Cauchy transforms of functions and measures supported by an unbounded interval which is not the entire real axis. For convenience we restrict ourselves to the interval $[0,\infty).$ Bringing once again the Fourier-Laplace transform into play, we deduce complex characterizations for the Cauchy transforms of functions in $L_{2}(0,\infty).$ Moreover, we analyze the behavior of Cauchy transform on several half-planes and shall use these results for a fairly general geometric characterization. In the second section of this chapter, we focus on Cauchy transforms of measures with support in $[0,\infty).$ In this context, we shall derive a reconstruction formula for these Cauchy transforms holding under pretty general conditions as well as results on the behaviur on the left half-plane. We close this chapter by rather technical real-type conditions and characterizations for Cauchy transforms of functions in $L_p(0,\infty)$ basing on an approach in [82].
The most common case of Cauchy transforms, those of compactly supported functions or measures, is the subject of Chapter 5. After complex and geometric characterizations originating from similar ideas as in the fourth chapter, we adapt a functional-analytic approach in [27] to special measures, namely those with densities to a given complex measure $\mu.$ The chapter is closed with a study of the Cauchy transformation on weighted $L_p$ spaces. Here, we choose an ansatz through the finite Hilbert transform on $(-1,1).$
The sixth chapter is devoted to the issue of integrability of Cauchy transforms. Since this topic has no comprehensive treatise in literature yet, we start with an introduction of weighted Bergman spaces and general results on the interaction of the Cauchy transformation in these spaces. Afterwards, we combine the theory of Zen spaces with Cauchy transforms by using once again their connection with Fourier transforms. Here, we shall encounter general Paley-Wiener theorems of the recent past. Lastly, we attack the issue of integrability of Cauchy transforms by means of potential theory. Therefore, we derive a Fourier integral formula for the logarithmic energy in one and multiple dimensions and give applications to Fourier and hence Cauchy transforms.
Two appendices are annexed to this thesis. The first one covers important definitions and results from measure theory with a special focus on complex measures. The second appendix contains Cauchy transforms of frequently used measures and functions with detailed calculations.
Die Dissertation beschäftigt sich mit einer neuartigen Art von Branch-and-Bound Algorithmen, deren Unterschied zu klassischen Branch-and-Bound Algorithmen darin besteht, dass
das Branching durch die Addition von nicht-negativen Straftermen zur Zielfunktion erfolgt
anstatt durch das Hinzufügen weiterer Nebenbedingungen. Die Arbeit zeigt die theoretische Korrektheit des Algorithmusprinzips für verschiedene allgemeine Klassen von Problemen und evaluiert die Methode für verschiedene konkrete Problemklassen. Für diese Problemklassen, genauer Monotone und Nicht-Monotone Gemischtganzzahlige Lineare Komplementaritätsprobleme und Gemischtganzzahlige Lineare Probleme, präsentiert die Arbeit
verschiedene problemspezifische Verbesserungsmöglichkeiten und evaluiert diese numerisch.
Weiterhin vergleicht die Arbeit die neue Methode mit verschiedenen Benchmark-Methoden
mit größtenteils guten Ergebnissen und gibt einen Ausblick auf weitere Anwendungsgebiete
und zu beantwortende Forschungsfragen.
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.
Startups are essential agents for the evolution of economies and the creative destruction of established market conditions for the benefit of a more effective and efficient economy. Their significance is manifested in their drive for innovation and technological advancements, their creation of new jobs, their contribution to economic growth, and their impact on increased competition and increased market efficiency. By reason of their attributes of newness and smallness, startups often experience a limitation in accessing external financial resources. Extant research on entrepreneurial finance examines the capital structure of startups, various funding tools, financing environments in certain regions, and investor selection criteria among other topics. My dissertation contributes to this research area by examining the becoming increasingly important funding instrument of venture debt. Prior research on venture debt only investigated the business model of venture debt, the concept of venture debt, the selection criteria of venture debt providers, and the role of patents in the venture debt provider’s selection process. Based on qualitative and quantitative methods, the dissertation outlines the emergence of venture debt in Europe as well as the impact of venture debt on startups to open up a better understanding of venture debt.
The results of the qualitative studies indicate that venture debt was formed based on a ‘Kirznerian’ entrepreneurial opportunity and venture debt impacts startups positive and negative in their development via different impact mechanisms.
Based on these results, the dissertation analyzes the empirical impact of venture debt on a startup’s ability to acquire additional financial resources as well as the role of the reputation of venture debt providers. The results suggest that venture debt increases the likelihood of acquiring additional financial resources via subsequent funding rounds and trade sales. In addition, a higher venture debt provider reputation increases the likelihood of acquiring additional financial resources via IPOs.
Diese Dissertation beschäftigt sich mit der Fragestellung, ob und wie Intersektionalität als analytische Perspektive für literarische Texte eine nützliche Ergänzung für ethnisch geordnete Literaturfelder darstellt. Diese Fragestellung wird anhand der Analyse dreier zeitgenössischer chinesisch-kanadischer Romane untersucht.
In der Einleitung wird die Relevanz der Themenbereiche Intersektionalität und asiatisch-kanadische Literatur erörtert. Das darauffolgende Kapitel bietet einen historischen Überblick über die chinesisch-kanadische Einwanderung und geht detailliert auf die literarischen Produktionen ein. Es wird aufgezeigt, dass, obwohl kulturelle Güter auch zur Artikulation von Ungleichheitsverhältnissen aufgrund von zugeschriebener ethnischer Zugehörigkeit entstehen, ein Diversifizierungsbestreben innerhalb der literarischen Gemeinschaft von chinesisch-kanadischen Autor:innen identifiziert werden kann. Das dritte Kapitel widmet sich dem Begriff „Intersektionalität“ und stellt, nach einer historischen Einordnung des Konzeptes mit seinen Ursprüngen im Black Feminism, Intersektionalität als bindendes Element zwischen Postkolonialismus, Diversität und Empowerment dar – Konzepte, die für die Analyse (kanadischer) Literatur in dieser Dissertation von besonderer Relevanz sind. Anschließend wird die Rolle von Intersektionalität in der Literaturwissenschaft aufgegriffen. Die darauffolgenden exemplarischen Analysen von Kim Fus For Today I Am a Boy, Wayson Choys The Jade Peony und Yan Lis Lily in the Snow veranschaulichen die vorangegangen methodischen Überlegungen. Allen drei Romanen vorangestellt ist die Kontextualisierung des jeweiligen Werkes als chinesisch-kanadisch, aber auch bisher vorgenommene Überlegungen, die diese Einordnung infrage stellen. Nach einer Zusammenfassung des Inhalts folgt eine intersektionale Analyse auf der inhaltlichen Ebene, die in den familiären und weiteren sozialen Bereich unterteilt ist, da sich die Hierarchiemechanismen innerhalb dieser Bereiche unterscheiden oder gegenseitig verstärken, wie aus den Analysen hervorgeht. Anschließend wird die formale Analyse mit einem intersektionalen Schwerpunkt in einem separaten Unterkapitel näher beleuchtet. Ein drittes Unterkapitel widmet sich einem dem jeweiligen Roman spezifischen Aspekt, der im Zusammenhang mit einer intersektionalen Analyse von besonderer Relevanz ist. Die Arbeit schließt mit einem übergreifenden Fazit, welches die wichtigsten Ergebnisse aus der Analyse zusammenfasst und mit weiteren Überlegungen zu den Implikationen dieser Dissertation, vor allem im Hinblick auf sogenannte kanadische „master narratives“, die eine weitreichende, kontextuelle Relevanz für das Arbeiten mit literarischen Texten aufweisen und durch einen intersektionalen literarischen Ansatz in Zukunft gegebenenfalls gewinnbringend ergänzt werden können.
While humans find it easy to process visual information from the real world, machines struggle with this task due to the unstructured and complex nature of the information. Computer vision (CV) is the approach of artificial intelligence that attempts to automatically analyze, interpret, and extract such information. Recent CV approaches mainly use deep learning (DL) due to its very high accuracy. DL extracts useful features from unstructured images in a training dataset to use them for specific real-world tasks. However, DL requires a large number of parameters, computational power, and meaningful training data, which can be noisy, sparse, and incomplete for specific domains. Furthermore, DL tends to learn correlations from the training data that do not occur in reality, making DNNs poorly generalizable and error-prone.
Therefore, the field of visual transfer learning is seeking methods that are less dependent on training data and are thus more applicable in the constantly changing world. One idea is to enrich DL with prior knowledge. Knowledge graphs (KG) serve as a powerful tool for this purpose because they can formalize and organize prior knowledge based on an underlying ontological schema. They contain symbolic operations such as logic, rules, and reasoning, and can be created, adapted, and interpreted by domain experts. Due to the abstraction potential of symbols, KGs provide good prerequisites for generalizing their knowledge. To take advantage of the generalization properties of KG and the ability of DL to learn from large-scale unstructured data, attempts have long been made to combine explicit graph and implicit vector representations. However, with the recent development of knowledge graph embedding methods, where a graph is transferred into a vector space, new perspectives for a combination in vector space are opening up.
In this work, we attempt to combine prior knowledge from a KG with DL to improve visual transfer learning using the following steps: First, we explore the potential benefits of using prior knowledge encoded in a KG for DL-based visual transfer learning. Second, we investigate approaches that already combine KG and DL and create a categorization based on their general idea of knowledge integration. Third, we propose a novel method for the specific category of using the knowledge graph as a trainer, where a DNN is trained to adapt to a representation given by prior knowledge of a KG. Fourth, we extend the proposed method by extracting relevant context in the form of a subgraph of the KG to investigate the relationship between prior knowledge and performance on a specific CV task. In summary, this work provides deep insights into the combination of KG and DL, with the goal of making DL approaches more generalizable, more efficient, and more interpretable through prior knowledge.
Do Personality Traits, Trust and Fairness Shape the Stock-Investing Decisions of an Individual?
(2023)
This thesis is comprised of three projects, all of which are fundamentally connected to the choices that individuals make about stock investments. Differences in stock market participation (SMP) across countries are large and difficult to explain. The second chapter focuses on differences between Germany (low SMP) and East Asian countries (mostly high SMP). The study hypothesis is that cultural differences regarding social preferences and attitudes towards inequality lead to different attitudes towards stock markets and subsequently to different SMPs. Using a large-scale survey, it is found that these factors can, indeed, explain a substantial amount of the country differences that other known factors (financial literacy, risk preferences, etc.) could not. This suggests that social preferences should be given a more central role in programs that aim to enhance SMP in countries like Germany. The third chapter documented the importance of trust as well as herding for stock ownership decisions. The findings show that trust as a general concept has no significant contribution to stock investment intention. A thorough examination of general trust elements reveals that in group and out-group trust have an impact on individual stock market investment. Higher out group trust directly influences a person's decision to invest in stocks, whereas higher in-group trust increases herding attitudes in stock investment decisions and thus can potentially increase the likelihood of stock investments as well. The last chapter investigates the significance of personality traits in stock investing and home bias in portfolio selection. Findings show that personality traits do indeed have a significant impact on stock investment and portfolio allocation decisions. Despite the fact that the magnitude and significance of characteristics differ between two groups of investors, inexperienced and experienced, conscientiousness and neuroticism play an important role in stock investments and preferences. Moreover, high conscientiousness scores increase stock investment desire and portfolio allocation to risky assets like stocks, discouraging home bias in asset allocation. Regarding neuroticism, a higher-level increases home bias in portfolio selection and decreases willingness to stock investment and portfolio share. Finally, when an investor has no prior experience with portfolio selection, patriotism generates home bias. For experienced investors, having a low neuroticism score and a high conscientiousness and openness score seemed to be a constant factor in deciding to invest in a well-diversified international portfolio
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.
This thesis comprises of four research papers on the economics of education and industrial relations, which contribute to the field of empirical economic research. All of the corresponding papers focus on analysing how much time individuals spend on specific activities. The allocation of available time resources is a decision that individuals make throughout their lifetime. In this thesis, we consider individuals at different stages of their lives - students at school, university students, and dependent employees at the workplace.
Part I includes two research studies on student's behaviour in secondary and tertiary education.
Chapter 2 explores whether students who are relatively younger or older within the school year exhibit differential time allocation. Building on previous findings showing that relatively younger students perform worse in school, the study shows that relatively younger students are aware of their poor performance in school and feel more strain as a result. Nevertheless, there are no clear differences to be found in terms of time spent on homework, while relatively younger students spend more time watching television and less time on sports activities. Thus, the results suggest that the lower learning outcomes are not associated with different time allocations between school-related activities and non-school-related activities.
Chapter 3 analyses how individual ability and labour market prospects affect study behaviour. The theoretical modelling predicts that both determinants increase study effort. The empirical investigation is based on cross-sectional data from the National Educational Panel Study (NEPS) and includes thousands of students in Germany. The analyses show that more gifted students exhibit lower subjective effort levels and invest less time in self-study. In contrast, very good labour market prospects lead to more effort exerted by the student, both qualitatively and quantitatively. The potential endogeneity problem is taken into account by using regional unemployment data as an instrumental variable.
Part II includes two labour economic studies on determinants of overtime. Both studies belong to the field of industrial relations, as they focus on union membership on the one hand and the interplay of works councils and collective bargaining coverage on the other.
Chapter 4 shows that union members work less overtime than non-members do. The econometric approach takes the problem of unobserved heterogeneity into account; but provides no evidence that this issue affects the results. Different channels that could lead to this relationship are analysed by examining relevant subgroups separately. For example, this effect of union membership can also be observed in establishments with works councils and for workers who are very likely to be covered by collective bargaining agreements. The study concludes that the observed effect is due to the fact that union membership can protect workers from corresponding increased working time demands by employers.
Chapter 5 builds on previous studies showing a negative effect of works councils on overtime. In addition to co-determination by works councils at the firm level, collective bargaining coverage is an important factor in the German industrial relations system. Corresponding data was not available in the SOEP for quite some time. Therefore, the study uses recent SOEP data, which also contains information on collective bargaining coverage. A cross-sectional analysis is conducted to examine the effects of works councils in establishments with and without collective bargaining coverage. Similar to studies analysing other outcome variables, the results show that the effect of works councils exists only for employees covered by a collective bargaining agreement.
Striving for sustainable development by combating climate change and creating a more social world is one of the most pressing issues of our time. Growing legal requirements and customer expectations require also Mittelstand firms to address sustainability issues such as climate change. This dissertation contributes to a better understanding of sustainability in the Mittelstand context by examining different Mittelstand actors and the three dimensions of sustainability - social, economic, and environmental sustainability - in four quantitative studies. The first two studies focus on the social relevance and economic performance of hidden champions, a niche market leading subgroup of Mittelstand firms. At the regional level, the impact of 1,645 hidden champions located in Germany on various dimensions of regional development is examined. A higher concentration of hidden champions has a positive effect on regional employment, median income, and patents. At the firm level, analyses of a panel dataset of 4,677 German manufacturing firms, including 617 hidden champions, show that the latter have a higher return on assets than other Mittelstand firms. The following two chapters deal with environmental strategies and thus contribute to the exploration of the environmental dimension of sustainability. First, the consideration of climate aspects in investment decisions is compared using survey data from 468 European venture capital and private equity investors. While private equity firms respond to external stakeholders and portfolio performance and pursue an active ownership strategy, venture capital firms are motivated by product differentiation and make impact investments. Finally, based on survey data from 443 medium-sized manufacturing firms in Germany, 54% of which are family-owned, the impact of stakeholder pressures on their decarbonization strategies is analyzed. A distinction is made between symbolic (compensation of CO₂-emissions) and substantive decarbonization strategies (reduction of CO₂-emissions). Stakeholder pressures lead to a proactive pursuit of decarbonization strategies, with internal and external stakeholders varying in their influence on symbolic and substantial decarbonization strategies, and the relationship influenced by family ownership.
In recent years, the establishment of new makerspaces in Germany has increased significantly. The underlying phenomenon of the Maker Movement is a cultural and technological movement focused on making physical and digital products using open source principles, collaborative production, and individual empowerment. Because of its potential to democratize the innovation and production process, empower individuals and communities, and enable innovators to solve problems at the local level, the Maker Movement has received considerable attention in recent years. Despite numerous indicators, little is known about the phenomenon and its individual members, especially in Germany. Initial research suggests that the Maker Movement holds great potential for innovation and entrepreneurship. However, there is still a gap in understanding how Makers discover, evaluate and exploit entrepreneurial opportunities. Moreover, there is still controversy - both among policy makers and within the maker community itself - about the impact the maker movement has and can have on innovation and entrepreneurship in the future. This dissertation uses a mixed-methods approach to explore these questions. In addition to a quantitative analysis of maker characteristics, the results show that social impact, market size, and property rights have significant effects on the evaluation of entrepreneurial opportunities. The findings within this dissertation expand research in the field of the Maker Movement and offer multiple implications for practice. This dissertation provides the first quantitative data on makers in makerspaces in Germany, their characteristics and motivations. In particular, the relationship between the Maker Movement and entrepreneurship is explored in depth for the first time. This is complemented by the presentation of different identity profiles of the individuals involved. In this way, policy-makers can develop a better understanding of the movement, its personalities and values, and consider them in initiatives and formats.
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.
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.
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
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.
Zeitgleich mit stetig wachsenden gesellschaftlichen Herausforderungen haben im vergangenen Jahrzehnt Sozialunternehmen stark an Bedeutung gewonnen. Sozialunternehmen verfolgen das Ziel, mit unternehmerischen Mitteln gesellschaftliche Probleme zu lösen. Da der Fokus von Sozialunternehmen nicht hauptsächlich auf der eigenen Gewinnmaximierung liegt, haben sie oftmals Probleme, geeignete Unternehmensfinanzierungen zu erhalten und Wachstumspotenziale zu verwirklichen.
Zur Erlangung eines tiefergehenden Verständnisses des Phänomens der Sozialunternehmen untersucht der erste Teil dieser Dissertation anhand von zwei Studien auf der Basis eines Experiments das Entscheidungsverhalten der Investoren von Sozialunternehmen. Kapitel 2 betrachtet daher das Entscheidungsverhalten von Impact-Investoren. Der von diesen Investoren verfolgte Investmentansatz „Impact Investing“ geht über eine reine Orientierung an Renditen hinaus. Anhand eines Experiments mit 179 Impact Investoren, die insgesamt 4.296 Investitionsentscheidungen getroffen haben, identifiziert eine Conjoint-Studie deren wichtigste Entscheidungskriterien bei der Auswahl der Sozialunternehmen. Kapitel 3 analysiert mit dem Fokus auf sozialen Inkubatoren eine weitere spezifische Gruppe von Unterstützern von Sozialunternehmen. Dieses Kapitel veranschaulicht auf der Basis des Experiments die Motive und Entscheidungskriterien der Inkubatoren bei der Auswahl von Sozialunternehmen sowie die von ihnen angebotenen Formen der nichtfinanziellen Unterstützung. Die Ergebnisse zeigen unter anderem, dass die Motive von sozialen Inkubatoren bei der Unterstützung von Sozialunternehmen unter anderem gesellschaftlicher, finanzieller oder reputationsbezogener Natur sind.
Der zweite Teil erörtert auf der Basis von zwei quantitativ empirischen Studien, inwiefern die Registrierung von Markenrechten sich zur Messung sozialer Innovationen eignet und mit finanziellem und sozialem Wachstum von sozialen Startups in Verbindung steht. Kapitel 4 erörtert, inwiefern Markenregistrierungen zur Messung von sozialen Innovationen dienen können. Basierend auf einer Textanalyse der Webseiten von 925 Sozialunternehmen (> 35.000 Unterseiten) werden in einem ersten Schritt vier Dimensionen sozialer Innovationen (Innovations-, Impact-, Finanz- und Skalierbarkeitsdimension) ermittelt. Darauf aufbauend betrachtet dieses Kapitel, wie verschiedene Markencharakteristiken mit den Dimensionen sozialer Innovationen zusammenhängen. Die Ergebnisse zeigen, dass insbesondere die Anzahl an registrierten Marken als Indikator für soziale Innovationen (alle Dimensionen) dient. Weiterhin spielt die geografische Reichweite der registrierten Marken eine wichtige Rolle. Aufbauend auf den Ergebnissen von Kapitel 4 untersucht Kapitel 5 den Einfluss von Markenregistrierungen in frühen Unternehmensphasen auf die weitere Entwicklung der hybriden Ergebnisse von sozialen Startups. Im Detail argumentiert Kapitel 5, dass sowohl die Registrierung von Marken an sich als auch deren verschiedene Charakteristiken unterschiedlich mit den sozialen und ökonomischen Ergebnissen von sozialen Startups in Verbindung stehen. Anhand eines Datensatzes von 485 Sozialunternehmen zeigen die Analysen aus Kapitel 5, dass soziale Startups mit einer registrierten Marke ein vergleichsweise höheres Mitarbeiterwachstum aufweisen und einen größeren gesellschaftlichen Beitrag leisten.
Die Ergebnisse dieser Dissertation weiten die Forschung im Social Entrepreneurship-Bereich weiter aus und bieten zahlreiche Implikationen für die Praxis. Während Kapitel 2 und 3 das Verständnis über die Eigenschaften von nichtfinanziellen und finanziellen Unterstützungsorganisationen von Sozialunternehmen vergrößern, schaffen Kapitel 4 und 5 ein größeres Verständnis über die Bedeutung von Markenanmeldungen für Sozialunternehmen.
Hybrid Modelling in general, describes the combination of at least two different methods to solve one specific task. As far as this work is concerned, Hybrid Models describe an approach to combine sophisticated, well-studied mathematical methods with Deep Neural Networks to solve parameter estimation tasks. To combine these two methods, the data structure of artifi- cially generated acceleration data of an approximate vehicle model, the Quarter-Car-Model, is exploited. Acceleration of individual components within a coupled dynamical system, can be described as a second order ordinary differential equation, including velocity and dis- placement of coupled states, scaled by spring - and damping-coefficient of the system. An appropriate numerical integration scheme can then be used to simulate discrete acceleration profiles of the Quarter-Car-Model with a random variation of the parameters of the system. Given explicit knowledge about the data structure, one can then investigate under which con- ditions it is possible to estimate the parameters of the dynamical system for a set of randomly generated data samples. We test, if Neural Networks are capable to solve parameter estima- tion problems in general, or if they can be used to solve several sub-tasks, which support a state-of-the-art parameter estimation method. Hybrid Models are presented for parameter estimation under uncertainties, including for instance measurement noise or incompleteness of measurements, which combine knowledge about the data structure and several Neural Networks for robust parameter estimation within a dynamical system.
In common shape optimization routines, deformations of the computational mesh
usually suffer from decrease of mesh quality or even destruction of the mesh.
To mitigate this, we propose a theoretical framework using so-called pre-shape
spaces. This gives an opportunity for a unified theory of shape optimization, and of
problems related to parameterization and mesh quality. With this, we stay in the
free-form approach of shape optimization, in contrast to parameterized approaches
that limit possible shapes. The concept of pre-shape derivatives is defined, and
according structure and calculus theorems are derived, which generalize classical
shape optimization and its calculus. Tangential and normal directions are featured
in pre-shape derivatives, in contrast to classical shape derivatives featuring only
normal directions on shapes. Techniques from classical shape optimization and
calculus are shown to carry over to this framework, and are collected in generality
for future reference.
A pre-shape parameterization tracking problem class for mesh quality is in-
troduced, which is solvable by use of pre-shape derivatives. This class allows for
non-uniform user prescribed adaptations of the shape and hold-all domain meshes.
It acts as a regularizer for classical shape objectives. Existence of regularized solu-
tions is guaranteed, and corresponding optimal pre-shapes are shown to correspond
to optimal shapes of the original problem, which additionally achieve the user pre-
scribed parameterization.
We present shape gradient system modifications, which allow simultaneous nu-
merical shape optimization with mesh quality improvement. Further, consistency
of modified pre-shape gradient systems is established. The computational burden
of our approach is limited, since additional solution of possibly larger (non-)linear
systems for regularized shape gradients is not necessary. We implement and com-
pare these pre-shape gradient regularization approaches for a 2D problem, which
is prone to mesh degeneration. As our approach does not depend on the choice of
forms to represent shape gradients, we employ and compare weak linear elasticity
and weak quasilinear p-Laplacian pre-shape gradient representations.
We also introduce a Quasi-Newton-ADM inspired algorithm for mesh quality,
which guarantees sufficient adaption of meshes to user specification during the rou-
tines. It is applicable in addition to simultaneous mesh regularization techniques.
Unrelated to mesh regularization techniques, we consider shape optimization
problems constrained by elliptic variational inequalities of the first kind, so-called
obstacle-type problems. In general, standard necessary optimality conditions cannot
be formulated in a straightforward manner for such semi-smooth shape optimization
problems. Under appropriate assumptions, we prove existence and convergence of
adjoints for smooth regularizations of the VI-constraint. Moreover, we derive shape
derivatives for the regularized problem and prove convergence to a limit object.
Based on this analysis, an efficient optimization algorithm is devised and tested
numerically.
All previous pre-shape regularization techniques are applied to a variational
inequality constrained shape optimization problem, where we also create customized
targets for increased mesh adaptation of changing embedded shapes and active set
boundaries of the constraining variational inequality.
For decades, academics and practitioners aim to understand whether and how (economic) events affect firm value. Optimally, these events occur exogenously, i.e. suddenly and unexpectedly, so that an accurate evaluation of the effects on firm value can be conducted. However, recent studies show that even the evaluation of exogenous events is often prone to many challenges that can lead to diverse interpretations, resulting in heated debates. Recently, there have been intense debates in particular on the impact of takeover defenses and of Covid-19 on firm value. The announcements of takeover defenses and the propagation of Covid-19 are exogenous events that occur worldwide and are economically important, but have been insufficiently examined. By answering open research questions, this dissertation aims to provide a greater understanding about the heterogeneous effects that exogenous events such as the announcements of takeover defenses and the propagation of Covid-19 have on firm value. In addition, this dissertation analyzes the influence of certain firm characteristics on the effects of these two exogenous events and identifies influencing factors that explain contradictory results in the existing literature and thus can reconcile different views.