Eric Raidl
PD Dr. Eric Raidl is studying the epistemology and logic of machine learning in science, looking in particular at how knowledge (or belief) represented in ML systems is revised e.g. in the light of new evidence. He is project leader of the AITE project.
Recent Publications
Raidl, E. (2021). Definable conditionals, Topoi, 40, 87-105. [https://link.springer.com/article/10.1007/s11245-020-09704-3]
Raidl, E. (2021). Three Conditionals: Contraposition, Difference-making and Dependency, Logica Yearbook 2020, 201-217. [https://www.collegepublications.co.uk/logica/?00034]
Raidl, E. (2021). Strengthened conditionals, in Context, Conflict and Reasoning. Logic in Asia: Studia Logica Library (Liao, B., Wang, Y., eds.), Springer, Singapore, 139-155. [https://link.springer.com/chapter/10.1007/978-981-15-7134-3_11]
Raidl, E., Iacona, A., Crupi, V. (2021). The logic of the evidential conditional, Review of Symbolic Logic, online first. [https://www.cambridge.org/core/journals/review-of-symbolic-logic/article/logic-of-the-evidential-conditional/4705B434E11CB191145DF631DEEF5693]
Raidl, E. (2020). Open-minded orthodox Bayesianism by epsilon-conditionalization, The British Journal for the Philosophy of Science, 71(1), 139-176 [https://www.journals.uchicago.edu/doi/10.1093/bjps/axy075]
Raidl, E., Spohn, W. (2020). An Accuracy Argument in Favor of Ranking Theory, Journal of Philosophical Logic, 49, 283-313. [https://link.springer.com/article/10.1007/s10992-019-09518-8]
Raidl, E. (2019). Lewis’ Triviality for Quasi Probabilities, Journal of Logic, Language and Information, 28,515–549. [https://doi.org/10.1007/s10849-019-09289-0 ]
Contact
AI Research Building
Maria-von-Linden-Str. 6
Room
72076 Tübingen