## Welcome to logic.rforge!

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- Last Updated on Wednesday, 13 May 2015 07:21
- Published on Tuesday, 31 July 2012 22:00

This is the personal academic homepage of Dr. Roland Poellinger, research fellow at the Munich Center for Mathematical Philosophy (MCMP/LMU), regular faculty member of the Doctoral School of Philosophy at the University of Pécs, Hungary (PTE), and one of the principal investigators of Exploring Quantum Matter (ExQM), joint PhD school of LMU Munich, TU Munich, and the Max Planck Institute of Quantum Optics in the Elite Network of Bavaria (ENB). His main research interests center around algorithmic methods in logic, causal modeling, and probabilistic reasoning in Bayesian networks. He is exploring ways of communicating science across audiences to shape new channels for mathematical philosophy. In 2015 he is teaching topics from the logic of conditionals at the University of Pécs in Hungary. [More in About]

## Recording of "The Mind-Brain Entanglement" on iTunes

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- Last Updated on Thursday, 03 July 2014 12:50
- Published on Monday, 26 May 2014 07:07

The recording of Roland Poellinger's talk on "The Mind-Brain Entanglement" (at LMU Munich, 14 May 2014) is now online and freely available on iTunes U and LMUcast: Click here to download the file or watch the video online.

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 event and trace its causal ancestry or posterity, you may run into mental events" (Kim 1993). In this talk I will use the formal framework of Bayes net causal models in an interventionist understanding (as devised, e.g., by Judea Pearl in *Causality*, 2000) to make the concept of causal influence precise. Investigating structurally similar cases of conflicting causal intuitions will motivate a natural extension of the interventionist Bayes net framework, *Causal Knowledge Patterns*, in which our intuition that the mind makes a difference finds an expression.

Update: More than 600 views and downloads of the recording in the first 10 days online! Thanks!

## "Formal Informal: Imprecise Probabilities" | MCMP joint event with Foundations of Statistics (4 Feb, 2013)

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- Last Updated on Sunday, 28 June 2015 07:36
- Published on Sunday, 03 February 2013 00:00

On Monday, 4 February, 2013, the MCMP is meeting the LMU Stats Department once again: The third edition of our series "Formal Informal" will center about "Imprecise Probabilities" this time. Join us for the discussion in an open round at 6:00pm, Alte Bibliothek, room 245 (Ludwigstraße 33). Presenters will be Thomas Augustin (Statistics/LMU), Radin Dardashti (MCMP/LMU), Marco Cattaneo (Statistics/LMU), Seamus Bradley (MCMP/LMU), and Stephan Hartmann (MCMP/LMU).

From the manifesto: Probability plays a fundamental role in our attempt to grasp and quantify uncertainty. However, considering the common case of limited knowledge about an event, the assignment of precise probabilities can be regarded as a limitation of the approach. Generalising probability theory to imprecise or interval probabilities offers a broader framework within which to discuss uncertainty. This approach has recently become quite popular due to its successful applications in many areas, ranging from social epistemology to econometrics and artificial intelligence.

This edition of Formal Informal will discuss foundations, applications, and problems of Imprecise Probabilities within statistics, philosophy, and physics.

Download the invitation here as a PDF document.

## Imprecise prediction and reliability in intervals - talk at WPMSIIP'12 (13 September, 2012)

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- Last Updated on Friday, 21 September 2012 15:09
- Published on Thursday, 13 September 2012 08:00

On 13 September, 2012, Roland Poellinger gives a talk at the Fifth Workshop on Principles and Methods of Statistical Inference with Interval Probability (WPMSIIP'12) in Munich, the title: "Superimposing Imprecise Evidence onto Stable Causal Knowledge: Analyzing ‘Prediction’ in the Newcomb Case". The talk sketches a possible extension of the CKP framework and outlines an agenda for introducing interval probabilities into causal reasoning, where specific concepts need to be formalized in an imprecise way – (un)reliability as quality of prediction in the Newcomb case. From the abstract:

In this talk I will prepare the ground for a understanding of causality that enables the causal decision theorist to answer Nozick’s challenge with the solution of one-boxing by drawing on the framework of *causal knowledge patterns*, i.e., Bayes net causal models built upon stable causal relations (cf. Pearl 1995 and 2000/2009) augmented by non-causal knowledge (*epistemic contours*). This rendition allows the careful re-examination of all relevant notions in the original story and facilitates approaching the following questions:

- How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge?
- How can the notion of
*prediction*be analyzed – philosophically and formally? - If all relations given in the model represent stable causal knowledge, how can imprecise evidence be embedded formally? Or in other words: How can the
*unreliable predictor*be modeled without discarding the core structure?

Finally, in what way could *unreliable prediction* be modeled with interval probability, as motivated by considerations in Nozick’s treatise? And what should be the interpretation of such a rendition?

## "Formal Informal: Inductive Logic and Probabilities" | MCMP joint event with Foundations of Statistics (12 July, 2012)

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- Last Updated on Monday, 29 June 2015 08:13
- Published on Tuesday, 12 June 2012 22:00

On Thursday, 12 July, 2012, the MCMP is meeting the LMU Stats Department once again: The second edition of our series "Formal Informal" will center about "Inductive Logic and Probabilities" this time. Join us for the discussion in an open round at 6:30pm, Alte Bibliothek, room 245 (Ludwigstraße 33). Presenters will be Karine Fradet (Philosophy, Université de Montrèal), Frederik Herzberg (Math. Economics, Bielefeld/MCMP), and Christina Schneider (Philosophy & Statistics, LMU).

From the manifesto: Carnap, who occupies a central place in the development of inductive logic, showed that the disagreements between the interpretation of probabilities as a state of the world and as a state of knowledge of the observer were vain since they were not about the same concept. He concentrated on the second of these two concepts, inductive probabilities, drew the foundations of inductive logic, systematized aspects and approaches, and presented the different methods not as competing against each other, but as part of a system, each perspective being a point on the continuum of the inductive methods.

This edition of Formal Informal will collect, sort, and discuss foundations, applications, and problems of inductive methodology, bridging views from philosophy and statistics.

Download the invitation here as a PDF document.

## "Formal Informal: The Markov Assumption" | MCMP joint event with Foundations of Statistics (13 June, 2012)

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- Last Updated on Sunday, 28 June 2015 07:37
- Published on Tuesday, 12 June 2012 22:00

On 13 June, 2012, the MCMP is meeting the LMU Stats Department again: "Formal Informal" will center about "The Markov Assumption". Join us for the discussion in an open round at 6:30pm, Alte Bibliothek, room 245 (Ludwigstraße 33). Presenters will be Conor Mayo-Wilson (CMU Philosophy, currently visitor at the MCMP), Marco Cattaneo (LMU Statistics), and Roland Poellinger (MCMP/LMU).

From the manifesto: Bayesian nets are a powerful means of representing conditional independencies between variables in compact manner. Whatever the size of the domain, consistent inference is facilitated by one simple local requirement: The Markov assumption states that a variable is independent of all other non-successors given the values of its parents in the graph. In causal guise: Direct causes screen off their direct effects from other causal influences. What other ways of reading the Markov assumption are there? Why is it justified? Where does it hold? How can it be bent?

Download the invitation here as a PDF document.

## Research-based teaching with workshop videos

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- Last Updated on Thursday, 27 September 2012 16:15
- Published on Tuesday, 09 August 2011 16:12

The poster/flyer "Research-based Teaching with Workshop Videos" was created for LMU's Virtuelle Hochschule and explains the motivation for and the process of organizing the international philosophical workshop "Concrete Causation" (July 2010) of which all talks were recorded, put online, and later used in class. Download the PDF (text in German) here!