3 papers in the archive (2022–2026), average empirical rigor 2/10, average math complexity 7.3/10. Quadrant mix: Lab Rats ×2, Philosophers ×1.
Works mostly in Risk Management & Tail Risk.
Frequent co-authors: Johannes Langner (2), Alexander Voß (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
This paper assumes a robust, in general not dominated, probabilistic framework and provides necessary and sufficient conditions for a bipolar representation of subsets of the set of all quasi-sure equivalence classes of non-negative random variables, without any further conditions on the underlying
This paper assumes a robust stochastic model where a set $\mathcal{P}$ of probability measures replaces the single probability measure of dominated models. We introduce and study $\mathcal{P}$-sensitive functions defined on robust function spaces of random variables. We show that $\mathcal{P}$-sensi
We introduce a decision-making framework tailored for the management of systemic risk in networks. This framework is constructed upon three fundamental components: (1) a set of acceptable network configurations, (2) a set of interventions aimed at risk mitigation, and (3) a cost function quantifying