Shows
MCMP – Mathematical Philosophy (Archive 2011/12)Group Presentation, Munich Center for Mathematical Philosophy (LMU)Members of the MCMP (Roland Poellinger, Florian Steinberger, Thomas Meier, Vincenzo Crupi and Olivier Roy) present their current research.
2019-04-201h 15
MCMPFive Years MCMP: Looking BackRoland Poellinger (LMU/MCMP) gives a talk at the Workshop on Five Years MCMP: Quo Vadis, Mathematical Philosophy? (2-4 June, 2016) titled "Five Years MCMP: Looking Back". Abstract: In this presentation I will speak about the MCMP's outreach and line up some of the center's achievements in the last five years. I will put special emphasis on our media output since many of our activities are mirrored in our media-related efforts such as our video channels on iTunes U, our Coursera online courses, and our publication database on the MCMP's web portal.
2018-03-1700 min
Concrete CausationProbabilistic Causal Inference from Heterogeneous EvidenceRoland Poellinger (MCMP/LMU) gives a talk at the workshop on "Mechanisms in Medicine" (3-5 July, 2017) titled "Probabilistic Causal Inference from Heterogeneous Evidence" (based on joint work with Barbara Osimani and Jürgen Landes). Abstract: Current methods for the purpose of causal inference aim to deliver a categorical assessment about the presence of a causal relationship between events or variables. This is at odds with the great amount of epistemic and ethical uncertainty surrounding most applied sciences. In particular, for the sake of the precautionary principle, this uncertainty should not be dismissed but rather explicitly accounted for in detecting, preventing a...
2018-03-1343 minConcrete CausationUnifying Causal and Non-Causal KnowledgeMichael Strevens (NYU) meets Roland Poellinger (MCMP/LMU) in a joint session on "Unifying Causal and Non-Causal Knowledge" at the MCMP workshop "Bridges 2014" (2 and 3 Sept, 2014, German House, New York City). The 2-day trans-continental meeting in mathematical philosophy focused on inter-theoretical relations thereby connecting form and content of this philosophical exchange. Idea and motivation: We use theories to explain, to predict and to instruct, to talk about our world and order the objects therein. Different theories deliberately emphasize different aspects of an object, purposefully utilize different formal methods, and necessarily confine their attention to a distinct field of interest. The desire...
2014-10-2056 minMCMP – Philosophy of ScienceUnifying Causal and Non-Causal KnowledgeMichael Strevens (NYU) meets Roland Poellinger (MCMP/LMU) in a joint session on "Unifying Causal and Non-Causal Knowledge" at the MCMP workshop "Bridges 2014" (2 and 3 Sept, 2014, German House, New York City). The 2-day trans-continental meeting in mathematical philosophy focused on inter-theoretical relations thereby connecting form and content of this philosophical exchange. Idea and motivation: We use theories to explain, to predict and to instruct, to talk about our world and order the objects therein. Different theories deliberately emphasize different aspects of an object, purposefully utilize different formal methods, and necessarily confine their attention to a distinct field of interest. The desire...
2014-10-0656 minConcrete CausationThe Mind-Brain EntanglementRoland Poellinger (MCMP/LMU) gives a talk at the MCMP Colloquium (14 May, 2014) titled "The Mind-Brain Entanglement". Abstract: Listing "The Nonreductivist’s Troubles with Mental Causation" (1993) Jaegwon Kim suggested that the only remaining alternatives are the eliminativist’s standpoint or plain denial of the mind’s causal powers if we want to uphold the closure of the physical and reject causal overdetermination at the same time. Nevertheless, explaining stock market trends by referring to investors’ fear of loss is a very familiar example of attributing reality to both domains and acknowledging the mind’s interaction with the world: "if you pick a physical...
2014-05-2451 minConcrete CausationDisentangling Nets for Causal InferenceAs part of the MCMP group presentation at the DGPhil XXII Workshop on Mathematical Philosophy Roland Poellinger (Munich Center for Mathematical Philosophy/LMU Munich) gives a mini presentation titled "Disentangling Nets for Causal Inference", in which he motivates an extension of standard Bayes net causal models to also allow for the embedding of non-causal knowledge. A longer introduction to the framework of Causal Knowledge Patterns (CKPs) can be found in the recording of the talk "Computing Non-Causal Knowledge for Causal Reasoning". The video "The Mind-Brain Entanglement" contains an application of CKPs in the philosophy of mind.
2011-09-1512 minMCMP – Mathematical Philosophy (Archive 2011/12)Computing Non-Causal Knowledge for Causal ReasoningRoland Poellinger (MCMP/LMU Munich) gives a talk at the MCMP Workshop on Computational Metaphysics titled "Computing Non-Causal Knowledge for Causal Reasoning". Abstract: We use logical and mathematical knowledge to generate causal claims. Inter-definitions or semantic overlap cannot be consistently embedded in standard Bayes net causal models since in many cases the Markov requirement will be violated. These considerations motivate an extension of Bayes net causal models to also allow for the embedding of Epistemic Contours (ECs). Such non-causal functions are consistently computable in Causal Knowledge Patterns (CKPs). An application of the framework can be found, e.g., in the...
2011-06-2855 minConcrete CausationComputing Non-Causal Knowledge for Causal ReasoningRoland Poellinger (Munich Center for Mathematical Philosophy/LMU Munich) gives a talk at the MCMP Workshop on Computational Metaphysics titled "Computing Non-Causal Knowledge for Causal Reasoning". Abstract: We use logical and mathematical knowledge to generate causal claims. Inter-definitions or semantic overlap cannot be consistently embedded in standard Bayes net causal models since in many cases the Markov requirement will be violated. These considerations motivate an extension of Bayes net causal models to also allow for the embedding of Epistemic Contours (ECs). Such non-causal functions are consistently computable in Causal Knowledge Patterns (CKPs). An application of the framework can be found...
2011-06-1255 min