Teaching

My teaching focuses on formal epistemology, decision theory, philosophy of science, and the philosophy of probability and statistics. I aim to combine accessible introductions to formal methods with philosophical questions about their interpretation, justification, and limits.

From October 2026, I will teach philosophy at Paris Lodron University of Salzburg. In the 2026–27 academic year, I will teach the following courses:

Can Rational Learning Lead Us Astray? Prior Beliefs, Uncertain Evidence, and Bayesianism

This course examines the foundations and strengths of Bayesian epistemology, including subjective probability, coherence, conditionalization, and the value of information. It also asks how Bayesian methods should be applied when evidence is incomplete or uncertain, when results depend on prior assumptions, and when the available evidence does not determine a unique precise representation.

All Models Are Wrong—So Why Do They Work? Representation, Evidence, and Explanation

Scientific models routinely simplify, idealize, and distort their targets, yet they can still predict successfully and provide powerful explanations. This course examines philosophical accounts of modelling and representation, the roles of abstraction and idealization, and the relation between prediction and explanation. It also considers cases in which several models are compatible with the available evidence and asks when simplification illuminates rather than misleads.

Are There Irrational Choices? Preferences, Context, and Decision Theory

This course introduces the foundations of rational choice under risk and uncertainty, including preferences, expected utility, and the representation of decision problems. It then examines challenges such as the Allais and Ellsberg paradoxes, ambiguity, framing effects, incomplete preferences, and context-sensitive choice. A central question is whether apparent irrationality always reflects a failure of rational choice, or sometimes a failure of the formal model to represent what matters to the agent.

Should We Trust the Numbers? Probability, Evidence, and Scientific Inference

This course examines the philosophical foundations of probability and statistical inference. Students compare frequentist, Bayesian, likelihood-based, and error-statistical approaches and study concepts including p-values, confidence and credible intervals, likelihood ratios, model selection, and model checking. Particular attention is paid to how assumptions about priors, stopping rules, model specification, and researcher choices affect evidential conclusions.