Die 10 zuletzt veröffentlichten Dokumente
In this dissertation, I analyze how large players in financial markets exert influence on smaller players and how this affects the decisions of the large ones. I focus on how the large players process information in an uncertain environment, form expectations and communicate these to smaller players through their actions. I examine these relationships empirically in the foreign exchange market and in the context of a game-theoretic model of an investment project.
In Chapter 2, I investigate the relationship between the foreign exchange trading activity of large US-based market participants and the volatility of the nominal spot exchange rate. Using a novel dataset, I utilize the weekly growth rate of aggregate foreign currency positions of major market participants to proxy trading activity in the foreign exchange market. By estimating the heterogeneous autoregressive model of realized volatility (HAR-RV), I find evidence of a positive relationship between trading activity and volatility, which is mainly driven by unexpected changes in trading activity and is asymmetric for some of the currencies considered. My results contribute to the understanding of the drivers of exchange rate volatility and the role of large players in the flow of information in financial markets.
In Chapters 3 and 4, I consider a sequential global game of an investment project to examine how a large creditor influences the decisions of small creditors with her lending decision. I pay particular attention to the timing of the large player’s decision, i.e. whether she makes her decision to roll over a credit before or after the small players. I show that she faces a trade-off between signaling to and learning from small creditors. By being a focal point for coordination, her actions have a substantial impact on the probability of coordination failure and the failure of the investment project. I investigate the sensitivity of the equilibrium by comparing settings with perfect and imperfect learning. The results highlight the importance of signaling and provide a new perspective on the idea of catalytic finance and the influence of a lender-of-last-resort in self-fulfilling debt crises.
The cumulative and bidirectional groundwater-surface water (GW-SW) interaction along a stream is defined as hydrological turnover (HT) influencing solute transport and source water composition. However, HT proves to be highly variable, producing spatial exchange patterns influenced by local groundwater, geology, and topography. Hence, identifying factors controlling HT poses a challenge. We studied spatiotemporal HT variability at two reaches of a third order tributary of the river Mosel, Germany. Additionally, we sampled for silica concentrations in the stream and in the near-stream groundwater. Thus, creating snapshots of the boundary layer between ground- and surface water where HT occurs, driven by mixing processes in the hyporheic zone. We utilize an enhanced hydrograph separation method, unveiling reach differences in storage drainage based on aquifer dimension and connectivity. The data shows a site-specific negative correlation of HT with discharge, while hydraulic gradients correlate with HT only at the reach with faster catchment drainage behavior. Examining silica concentrations between stream and wells shows that silica variation increases significantly with the decrease of HT under low flow conditions at the slower draining reach. At the fast draining reach this relationship is seasonal. In Summary, our results show that stream discharge shapes the influence of HT on solute transport. Yet, reach drainage behavior shapes seasonal states of groundwater storages and can be an additional control of HT. Hence, concentration change of pollutants could be masked by HT. Thus, our findings contribute to the understanding of HT variability along streams and its ability of influencing physico-chemical stream water composition.
Introduction: Apart from a few studies with limited sample sizes, we have little data on attitudes toward lesbian and gay (LG) people in Greece. Methods: This study examines this topic in 949 heterosexual Greek participants. Based on previous research in cultural contexts other than Greece, we hypothesized that four demographics (gender, age, education, area of residence) and religious and political orientation predict a substantial amount of variance in homophobia (i.e., anti-LG attitudes). Results: We verified all observed variables except area of residence as significant predictors. Regarding the “intergroup contact hypothesis,” we distinguished the direct effects of the predictor variables from indirect effects mediated by contact with lesbians and gay men. All variables except area of residence showed a direct effect and, except for education, also an indirect effect on homophobia. The strongest effects were found for religious and political orientation, followed by gender. Highly religious, right-wing oriented, and male participants reported the highest levels of homophobia, partially mediated by their low level of contact with LG people. Discussion/Conclusion: The results confirm and further explain the detrimental role the Greek Orthodox Church, right-wing political parties, and traditional gender roles play in the acceptance of sexual minorities.
Background: Large language models (LLMs) are increasingly used in mental health, showing promise in assessing disorders. However, concerns exist regarding their accuracy, reliability, and fairness. Societal biases and underrepresentation of certain populations may impact LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1%-2%, affecting more women than men. Among men, homosexual men face a higher risk of eating disorders (EDs) than heterosexual men. However, men are underrepresented in ED research, and studies on gender, sexual orientation, and their impact on AN and BN prevalence, symptoms, and treatment outcomes remain limited.
Objectives: We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by a common LLM as well as a smaller LLM specifically trained for mental health analyses, exemplified in the context of ED symptomatology and health-related quality of life (HRQoL) of patients with AN or BN.
Methods: We extracted 30 case vignettes (22 AN and 8 BN) from scientific papers. We adapted each vignette to create 4 versions, describing a female versus male patient living with their female versus male partner (2 × 2 design), yielding 120 vignettes. We then fed each vignette into ChatGPT-4 and to “MentaLLaMA” based on the Large Language Model Meta AI (LLaMA) architecture thrice with the instruction to evaluate them by providing responses to 2 psychometric instruments, the RAND-36 questionnaire assessing HRQoL and the eating disorder examination questionnaire. With the resulting LLM-generated scores, we calculated multilevel models with a random intercept for gender and sexual orientation (accounting for within-vignette variance), nested in vignettes (accounting for between-vignette variance).
Results: In ChatGPT-4, the multilevel model with 360 observations indicated a significant association with gender for the RAND-36 mental composite summary (conditional means: 12.8 for male and 15.1 for female cases; 95% CI of the effect –6.15 to -0.35; P=.04) but neither with sexual orientation (P=.71) nor with an interaction effect (P=.37). We found no indications for main effects of gender (conditional means: 5.65 for male and 5.61 for female cases; 95% CI –0.10 to 0.14; P=.88), sexual orientation (conditional means: 5.63 for heterosexual and 5.62 for homosexual cases; 95% CI –0.14 to 0.09; P=.67), or for an interaction effect (P=.61, 95% CI –0.11 to 0.19) for the eating disorder examination questionnaire overall score (conditional means 5.59-5.65 95% CIs 5.45 to 5.7). MentaLLaMA did not yield reliable results.
Conclusions: LLM-generated mental HRQoL estimates for AN and BN case vignettes may be biased by gender, with male cases scoring lower despite no real-world evidence supporting this pattern. This highlights the risk of bias in generative artificial intelligence in the field of mental health. Understanding and mitigating biases related to gender and other factors, such as ethnicity, and socioeconomic status are crucial for responsible use in diagnostics and treatment recommendations.
Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text.
Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features.
Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model.
Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language.
Conclusions: Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.
Background: As digital mental health delivery becomes increasingly prominent, a solid evidence base regarding its efficacy is needed.
Objective: This study aims to synthesize evidence on the comparative efficacy of systemic psychotherapy interventions provided via digital versus face-to-face delivery modalities.
Methods: We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for searching PubMed, Embase, Cochrane CENTRAL, CINAHL, PsycINFO, and PSYNDEX and conducting a systematic review and meta-analysis. We included randomized controlled trials comparing mental, behavioral, and somatic outcomes of systemic psychotherapy interventions using self- and therapist-guided digital versus face-to-face delivery modalities. The risk of bias was assessed with the revised Cochrane Risk of Bias tool for randomized trials. Where appropriate, we calculated standardized mean differences and risk ratios. We calculated separate mean differences for nonaggregated analysis.
Results: We screened 3633 references and included 12 articles reporting on 4 trials (N=754). Participants were youths with poor diabetic control, traumatic brain injuries, increased risk behavior likelihood, and parents of youths with anorexia nervosa. A total of 56 outcomes were identified. Two trials provided digital intervention delivery via videoconferencing: one via an interactive graphic interface and one via a web-based program. In total, 23% (14/60) of risk of bias judgments were high risk, 42% (25/60) were some concerns, and 35% (21/60) were low risk. Due to heterogeneity in the data, meta-analysis was deemed inappropriate for 96% (54/56) of outcomes, which were interpreted qualitatively instead. Nonaggregated analyses of mean differences and CIs between delivery modalities yielded mixed results, with superiority of the digital delivery modality for 18% (10/56) of outcomes, superiority of the face-to-face delivery modality for 5% (3/56) of outcomes, equivalence between delivery modalities for 2% (1/56) of outcomes, and neither superiority of one modality nor equivalence between modalities for 75% (42/56) of outcomes. Consequently, for most outcome measures, no indication of superiority or equivalence regarding the relative efficacy of either delivery modality can be made at this stage. We further meta-analytically compared digital versus face-to-face delivery modalities for attrition (risk ratio 1.03, 95% CI 0.52-2.03; P=.93) and number of sessions attended (standardized mean difference –0.11; 95% CI –1.13 to –0.91; P=.83), finding no significant differences between modalities, while CIs falling outside the range of the minimal important difference indicate that equivalence cannot be determined at this stage.
Conclusions: Evidence on digital and face-to-face modalities for systemic psychotherapy interventions is largely heterogeneous, limiting conclusions regarding the differential efficacy of digital and face-to-face delivery. Nonaggregated and meta-analytic analyses did not indicate the superiority of either delivery condition. More research is needed to conclude if digital and face-to-face delivery modalities are generally equivalent or if—and in which contexts—one modality is superior to another.
Background: Psychoeducation positively influences the psychological components of chronic low back pain (CLBP) in conventional treatments. The digitalization of health care has led to the discussion of virtual reality (VR) interventions. However, CLBP treatments in VR have some limitations due to full immersion. In comparison, augmented reality (AR) supplements the real world with virtual elements involving one’s own body sensory perception and can combine conventional and VR approaches.
Objective: The aim of this study was to review the state of research on the treatment of CLBP through psychoeducation, including immersive technologies, and to formulate suggestions for psychoeducation in AR for CLBP.
Methods: A scoping review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed in August 2024 by using Livivo ZB MED, PubMed, Web of Science, American Psychological Association PsycINFO (PsycArticle), and PsyArXiv Preprints databases. A qualitative content analysis of the included studies was conducted based on 4 deductively extracted categories.
Results: We included 12 studies published between 2019 and 2024 referring to conventional and VR-based psychoeducation for CLBP treatment, but no study referred to AR. In these studies, educational programs were combined with physiotherapy, encompassing content on pain biology, psychological education, coping strategies, and relaxation techniques. The key outcomes were pain intensity, kinesiophobia, pain catastrophizing, degree of disability, quality of life, well-being, self-efficacy, depression, attrition rate, and user experience. Passive, active, and gamified strategies were used to promote intrinsic motivation from a psychological point of view. Regarding user experience from a software development perspective, user friendliness, operational support, and application challenges were recommended.
In the face of uncontrollable complexity, the concept of a rational design of the organization is being replaced by the notion of an open future that is inherently unpredictable and unplanable. In rapidly changing environments, organizations and leaders are confronted with a constant stream of irritations and unexpected developments, that require ongoing attention. This prompts the question of whether the conceptualization of digital transformation as a paradigm shift also implies the need for new forms of leadership. The article analyzes the discourse on digital leadership and assesses the extent to which this concept relativizes leadership in the context of the evolution of leadership theory, which is characterized by a persistent process of modification and relativization of preceding concepts. Leadership concepts are not only responsive to general needs, but also vary according to specific contexts, such as non-profit leadership or leadership in social welfare organizations and meta-organizations. Results of a discourse analysis, which underscore the significance of adopting a complexity theory perspective on digital leadership, will therefore be contrasted with the initial findings of an empirical study on digitization in such meta-organizations. This allows for a discussion of the general findings on the revitalization of leadership, which will serve as a paradigmatic example of the previously developed context. The article concludes with implications for further theory development with the aim of making a specific contribution to organization-sensitive digitization research. The findings of the empirical study indicate the significance of employing informal structures and a heightened emphasis on subjectivity within meta-organizations, as opposed to the formal structures of organizations. The concept of digital leadership does not signify the obsolescence of traditional leadership; rather, it can be conceptualized as an advanced form of unheroic leadership within the context of external and internal complexity.
Investment theory and related theoretical approaches suggest a dynamic interplay between crystallized intelligence, fluid intelligence, and investment traits like need for cognition. Although cross-sectional studies have found positive correlations between these constructs, longitudinal research testing all of their relations over time is scarce. In our pre-registered longitudinal study, we examined whether initial levels of crystallized intelligence, fluid intelligence, and need for cognition predicted changes in each other. We analyzed data from 341 German students in grades 7–9 who were assessed twice, one year apart. Using multi-process latent change score models, we found that changes in fluid intelligence were positively predicted by prior need for cognition, and changes in need for cognition were positively predicted by prior fluid intelligence. Changes in crystallized intelligence were not significantly predicted by prior Gf, prior NFC, or their interaction, contrary to theoretical assumptions. This pattern of results was largely replicated in a model including all constructs simultaneously. Our findings support the notion that intelligence and investment traits, particularly need for cognition, positively interact during cognitive development, but this interplay was unexpectedly limited to Gf.
Attention in social interactions is directed by social cues such as the face or eye region of an interaction partner. Several factors that influence these attentional biases have been identified in the past. However, most findings are based on paradigms with static stimuli and no interaction potential. Therefore, the current study investigated the influence of one of these factors, namely facial affect in natural social interactions using an evaluated eye-tracking setup. In a sample of 35 female participants, we examined how individuals’ gaze behavior responds to changes in the facial affect of an interaction partner trained in affect modulation.
Our goal was to analyze the effects on attention to facial features and to investigate their temporal dynamics in a natural social interaction. The study results, obtained from both aggregated and dynamic analyses, indicate that facial affect has only subtle influences on gaze behavior during social interactions. In a sample with high measurement precision, these findings highlight the difficulties of capturing the subtleties of social attention in more naturalistic settings. The methodology used in this study serves as a foundation for future research on social attention differences in more ecologically valid scenarios.