# Welcome to logic.rforge

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

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)

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.

## Talk "Newcomb's Paradox – Wissen ordnen und erschließen in hybriden Netzen" @ Foundations of Statistics (16 May, 2012)

On 16 May, 2012, Roland Poellinger gives the talk "Newcomb's Paradox – Wissen ordnen und erschließen in hybriden Netzen" at the LMU Research Seminar Foundations of Statistics (Statistics Department, Ludwigstraße 33, room 245 – 6:30pm). In various approaches to solutions of the paradox the principle of dominance and the principle of maximum expected utilities are balanced or tweaked in more or (more often) less natural ways. Reconsidering the concept of prediction as an epistemic change of state yields a compact and intuitive rendition of the problem. Excerpt from the abstract: "In diesem Vortrag möchte ich die Modellierung des Paradoxons in Bayes‘schen kausalen Modellen erläutern, wie sie von Pearl (1995 oder 2000/2009) definiert und von Wolfgang Spohn („Reversing 30 Years of Discussion: Why Causal Decision Theorists Should One-Box“) bzw. Meek & Glymour (1994) zur Analyse von Newcomb’s Problem herangezogen werden. Als Antwort auf diese Ansätze möchte ich im zweiten Teil meiner Diskussion meinen Lösungsvorschlag in *Causal Knowledge Patterns* (einer Erweiterung des Bayesnetz-Frameworks mit intensionalen Informationsbrücken) präsentieren, um schließlich – näher an der Intuition und der ursprünglichen Formulierung von Nozicks Geschichte – bei der Lösung des „one-boxing“ anzugelangen."

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

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.

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

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?

## Talk "What is Causal Knowledge?" @ LMU Centrum für Informations- und Sprachverarbeitung (19 Jan, 2012)

On 19 Jan, 2012, Roland Poellinger gave the talk "What is Causal Knowledge?" at the LMU Centrum für Informations- und Sprachverarbeitung for an audience of computational linguists. The talk focused on philosophical implications of the title question, algorithmic approaches towards causal analysis, formalizations of an interventionist concept of causation, mathematical foundations of DAG search methods, and the necessity to extend the Bayes net causal model framework for the treatment of hybrid nets of mixed type knowledge (unifying extensional and intensional information).### More Articles...

- Typesetting KM Calculus with LaTeX
- Talk "Reclaiming Markov in Entangled Structures of Deterministic Causal Knowledge" @ CMU Pittsburgh (30 Nov and 7 Dec 2011, 5pm)
- "Formal Informal: The Markov Assumption" | MCMP joint event with Foundations of Statistics (13 June, 2012)
- Talk "Computing Non-causal Knowledge for Causal Reasoning" (11 June 2011)