About

Krzysztof Mierzewski is an Assistant Professor in the Department of Philosophy at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. His research areas include logic and philosophy of mathematics, categorical logic, computability and automated proof search, homotopy type theory, philosophy and history of mathematics, philosophy of language and linguistics, philosophical logic, proof theory, and philosophy of science and methodology. His work also encompasses belief revision, cognitive science and philosophy of mind, computational epistemology, learning theory, and the philosophy of social science, as well as ethics and value theory, ethics in medicine and scientific research, ethics and artificial intelligence, social and political philosophy, and methodology in ethics. Mierzewski is involved in various research projects and centers, including the Center for Ethics & Policy and the Center for Formal Epistemology, contributing to the advancement of formal and philosophical understanding across these domains.

Research topics

• Computer Science
• Artificial Intelligence
• Theoretical computer science
• Mathematics
• Discrete mathematics
• Algorithm
• Combinatorics

Selected publications

• Probing the Quantitative-Qualitative Divide in Probabilistic Reasoning

  arXiv (Cornell University) · 2023-07-11 · 2 citations

  preprintOpen access

  This paper explores the space of (propositional) probabilistic logical languages, ranging from a purely `qualitative' comparative language to a highly `quantitative' language involving arbitrary polynomials over probability terms. While talk of qualitative vs. quantitative may be suggestive, we identify a robust and meaningful boundary in the space by distinguishing systems that encode (at most) additive reasoning from those that encode additive and multiplicative reasoning. The latter includes not only languages with explicit multiplication but also languages expressing notions of dependence and conditionality. We show that the distinction tracks a divide in computational complexity: additive systems remain complete for $\mathsf{NP}$, while multiplicative systems are robustly complete for $\exists\mathbb{R}$. We also address axiomatic questions, offering several new completeness results as well as a proof of non-finite-axiomatizability for comparative probability. Repercussions of our results for conceptual and empirical questions are addressed, and open problems are discussed.

  PublisherOA PDFDOI

• Probing the quantitative–qualitative divide in probabilistic reasoning

  Annals of Pure and Applied Logic · 2023 · 11 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  This paper explores the space of (propositional) probabilistic logical languages, ranging from a purely ‘qualitative’ comparative language to a highly ‘quantitative’ language involving arbitrary polynomials over probability terms. While talk of qualitative vs. quantitative may be suggestive, we identify a robust and meaningful boundary in the space by distinguishing systems that encode (at most) additive reasoning from those that encode additive and multiplicative reasoning. The latter includes not only languages with explicit multiplication but also languages expressing notions of dependence and conditionality. We show that the distinction tracks a divide in computational complexity: additive systems remain complete for NP, while multiplicative systems are robustly complete for ∃R. We also address axiomatic questions, offering several new completeness results as well as a proof of non-finite-axiomatizability for comparative probability. Repercussions of our results for conceptual and empirical questions are addressed, and open problems are discussed.

  PublisherDOI

• Proceedings Eighteenth Conference on Theoretical Aspects of Rationality and Knowledge

  Electronic Proceedings in Theoretical Computer Science · 2021-06-21

  articleOpen access1st authorCorresponding
  PublisherDOI

• The Modal Logics of the Poison Game

  Logic in Asia: Studia logica library · 2020 · 15 citations

  • Computer Science
  • Mathematics
  • Discrete mathematics
  PublisherDOI

• PROBABILISTIC STABILITY, AGM REVISION OPERATORS AND MAXIMUM ENTROPY

  The Review of Symbolic Logic · 2020-10-21 · 3 citations

  article1st authorCorresponding

  Abstract Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb’s stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum entropy. In situations of information loss, or whenever the agent relies on a qualitative description of her information state—such as a plausibility ranking over hypotheses, or a belief set—the dynamics of AGM belief revision are compatible with Bayesian conditioning; indeed, through the maximum entropy principle, conditioning naturally generates AGM revision operators. This mitigates an impossibility theorem of Lin and Kelly for tracking Bayesian conditioning with AGM revision, and suggests an approach to the compatibility problem that highlights the information loss incurred by acceptance rules in passing from probabilistic to qualitative representations of belief.

  PublisherDOI

Frequent coauthors

• Johan van Benthem

  3 shared

• Francesca Zaffora Blando

  Carnegie Mellon University

  2 shared

• Duligur Ibeling

  Stanford University

  2 shared

• Milan Mossé

  2 shared

• Carlos Areces

  Centro Científico Tecnológico - Córdoba

  2 shared

• Thomas Icard

  2 shared