Research

The Munich Center for Mathematical Philosophy (MCMP) is dedicated to advancing philosophy through the systematic use of mathematical and formal methods. Our research spans logic, epistemology, philosophy of science, philosophy of physics, decision and social choice theory, and the philosophy of language, mathematics, and artificial intelligence. Across these domains, we share a commitment to clarity, rigor, and the productive interaction between philosophy and adjacent scientific disciplines.

Our research profile

The MCMP brings together three research groups: Logic and Language (led by Hannes Leitgeb), Philosophy of Science(led by Stephan Hartmann), and Philosophy and Decision Theory (led by Christian List). Together, these groups foster a unique environment in which diverse methodological approaches converge—ranging from logic, probability theory, and formal semantics to Bayesian networks, game theory, and computational modeling.

Current research projects address fundamental questions such as: How should we understand rational belief and reasoning under uncertainty? What constitutes scientific explanation and understanding? How can models and simulations contribute to knowledge? What are the conceptual foundations of physics and the philosophy of AI? And how can decision and social choice theory illuminate individual and collective agency, responsibility, and democratic processes?

The MCMP also serves as an international hub for the exchange of ideas. We regularly host workshops, conferences, and lecture series that bring leading scholars and promising early-career researchers to Munich. Through these activities, and through our specialized Master's program in Logic and Philosophy of Science, we provide a stimulating environment for graduate training and interdisciplinary collaboration.

By combining mathematical precision with philosophical depth, the MCMP demonstrates how formal methods can shed light on some of the most profound and pressing questions in philosophy and beyond.

From Bias to Knowledge: The Epistemology of Machine Learning

From Bias to Knowledge: The Epistemology of Machine Learning (2023 - 2029)
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The Scale Revolution in Physics

The Scale Revolution in Physics (2024 - 2027)
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Amalgamating Evidence About Causes: Medicine, the Medical Sciences, and Beyond

Amalgamating Evidence About Causes: Medicine, the Medical Sciences, and Beyond (2023 - 2026)
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Causal Extrapolation in Theory and Practice

Causal Extrapolation in Theory and Practice (2023 - 2026)
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Three Methodological Problems in Memory Science

Three Methodological Problems in Memory Science (2023 - 2025)
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Fundamental Indeterminacy of Spacetime

Fundamental Indeterminacy of Spacetime (2023 - 2025)
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Deep Learning in Particle Physics

Close-up shot of a laboratory setup with a red laser

© Jan Greune / LMU

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Character Education Under Normative Uncertainty

Character Education Under Normative Uncertainty
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