Selected work

Representative Papers

A Centrality Measure Using Magnitude Homology

Jul 17, 2026

This study addresses the problem of effectively measuring node centrality in graphs from geometric and topological perspectives. To this end, it introduces magnitude homology—a novel application in graph centrality analysis—and proposes a local centrality measure grounded in relative homology: the importance of a node is quantified by the change in magnitude homology resulting from its removal. The proposed measure satisfies several natural axioms, exhibits favorable theoretical properties, and demonstrates unique effectiveness in experiments, offering complementary insights to classical centrality metrics. This work thus provides a new topological lens for evaluating node importance in complex networks.

0 citationsRead paper

When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration

Jul 16, 2026

The widespread adoption of generative AI blurs the boundaries of users’ actual contributions in creative processes, often leading to misperceptions of authorship. This work introduces the novel concept of “authorship calibration”—defined as users’ accurate self-assessment of their genuine contribution in human-AI collaboration—and presents an empirical analysis based on the CoAuthor dataset. The study reveals that frequent AI users systematically overestimate their own input, whereas infrequent users exhibit more accurate calibration, thereby uncovering a link between AI usage intensity and metacognitive bias. These findings offer a new theoretical lens and empirical foundation for understanding how generative AI reshapes human perceptions of creative agency and authorship.

0 citationsRead paper

Comparing Chatbot Performance Enhanced with Persistent Homology

Jun 29, 2026

This study addresses the challenge of limited scalability and data scarcity in privacy-sensitive domains such as mental health, where chatbots often rely on small proprietary datasets. The authors propose a novel approach that leverages persistent homology—a technique from topological data analysis—to enrich input representations without increasing data volume, computational overhead, or compromising privacy. By embedding original text vectors with persistent homology features and integrating localized training with multi-model evaluation, the method demonstrates significant performance improvements over baseline models across multiple metrics. This work achieves a zero-cost, privacy-preserving enhancement in model efficacy, offering a practical solution for deploying robust conversational agents in constrained, sensitive settings.

0 citationsRead paper

Swimming in Dark Water: When Cartels Mimic Competition

Jun 29, 2026

This study uncovers a highly covert bid-rigging cartel in the road construction sector of Switzerland’s Canton Ticino between 1999 and 2005, which achieved effective collusion while evading detection. Drawing on extensive archival records, the research reconstructs how cartel members coordinated bids and allocated contracts through a formal “convention.” Combining regression analysis, machine learning, and double machine learning techniques, the study identifies behavioral patterns that mimicked competitive bidding. It reveals, for the first time, that a cost-based allocation mechanism—without side payments—can approximate optimal collusive outcomes. Moreover, the cartel systematically circumvented conventional econometric detection methods. Estimated overcharges average at least 45%, underscoring the substantial fiscal harm such collusion inflicts on public procurement.

0 citationsRead paper

Visible or Covert? The Causal Effect of Inspector Visibility on Fare Evasion Detection: A Causal Machine Learning and Policy Learning Approach

Jun 23, 2026

This study investigates the causal effect of inspector visibility—uniformed versus plainclothes—on fare evasion detection efficiency in public transport inspections. Leveraging 21,727 inspection records from Switzerland’s PostAuto, the analysis integrates causal machine learning, average treatment effect estimation, and heterogeneity analysis, and introduces strategy trees—a novel application in this domain—to optimize inspection resource allocation. The findings reveal that plainclothes inspections increase detection efficiency by 26% on average (0.173 additional detections per hour) and are superior in 83.3% of operational contexts; uniformed inspections are only more effective on routes with low proportions of foreign residents and high population density. This work is the first to apply strategy trees to public transport inspection policy, demonstrating the conditional efficacy of inspection approaches.

0 citationsRead paper

Recent publications

Latest Papers

A Centrality Measure Using Magnitude Homology

Jul 17, 2026

This study addresses the problem of effectively measuring node centrality in graphs from geometric and topological perspectives. To this end, it introduces magnitude homology—a novel application in graph centrality analysis—and proposes a local centrality measure grounded in relative homology: the importance of a node is quantified by the change in magnitude homology resulting from its removal. The proposed measure satisfies several natural axioms, exhibits favorable theoretical properties, and demonstrates unique effectiveness in experiments, offering complementary insights to classical centrality metrics. This work thus provides a new topological lens for evaluating node importance in complex networks.

0 citationsRead paper

When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration

Jul 16, 2026

The widespread adoption of generative AI blurs the boundaries of users’ actual contributions in creative processes, often leading to misperceptions of authorship. This work introduces the novel concept of “authorship calibration”—defined as users’ accurate self-assessment of their genuine contribution in human-AI collaboration—and presents an empirical analysis based on the CoAuthor dataset. The study reveals that frequent AI users systematically overestimate their own input, whereas infrequent users exhibit more accurate calibration, thereby uncovering a link between AI usage intensity and metacognitive bias. These findings offer a new theoretical lens and empirical foundation for understanding how generative AI reshapes human perceptions of creative agency and authorship.

0 citationsRead paper

Comparing Chatbot Performance Enhanced with Persistent Homology

Jun 29, 2026

This study addresses the challenge of limited scalability and data scarcity in privacy-sensitive domains such as mental health, where chatbots often rely on small proprietary datasets. The authors propose a novel approach that leverages persistent homology—a technique from topological data analysis—to enrich input representations without increasing data volume, computational overhead, or compromising privacy. By embedding original text vectors with persistent homology features and integrating localized training with multi-model evaluation, the method demonstrates significant performance improvements over baseline models across multiple metrics. This work achieves a zero-cost, privacy-preserving enhancement in model efficacy, offering a practical solution for deploying robust conversational agents in constrained, sensitive settings.

0 citationsRead paper

Swimming in Dark Water: When Cartels Mimic Competition

Jun 29, 2026

This study uncovers a highly covert bid-rigging cartel in the road construction sector of Switzerland’s Canton Ticino between 1999 and 2005, which achieved effective collusion while evading detection. Drawing on extensive archival records, the research reconstructs how cartel members coordinated bids and allocated contracts through a formal “convention.” Combining regression analysis, machine learning, and double machine learning techniques, the study identifies behavioral patterns that mimicked competitive bidding. It reveals, for the first time, that a cost-based allocation mechanism—without side payments—can approximate optimal collusive outcomes. Moreover, the cartel systematically circumvented conventional econometric detection methods. Estimated overcharges average at least 45%, underscoring the substantial fiscal harm such collusion inflicts on public procurement.

0 citationsRead paper

Visible or Covert? The Causal Effect of Inspector Visibility on Fare Evasion Detection: A Causal Machine Learning and Policy Learning Approach

Jun 23, 2026

This study investigates the causal effect of inspector visibility—uniformed versus plainclothes—on fare evasion detection efficiency in public transport inspections. Leveraging 21,727 inspection records from Switzerland’s PostAuto, the analysis integrates causal machine learning, average treatment effect estimation, and heterogeneity analysis, and introduces strategy trees—a novel application in this domain—to optimize inspection resource allocation. The findings reveal that plainclothes inspections increase detection efficiency by 26% on average (0.173 additional detections per hour) and are superior in 83.3% of operational contexts; uniformed inspections are only more effective on routes with low proportions of foreign residents and high population density. This work is the first to apply strategy trees to public transport inspection policy, demonstrating the conditional efficacy of inspection approaches.

0 citationsRead paper