# Welcome to logic.rforge

## Talk "Reclaiming Markov in Entangled Structures of Deterministic Causal Knowledge" @ CMU Pittsburgh (30 Nov and 7 Dec 2011, 5pm)

On 30 Nov 2011 Roland Poellinger will give a talk at the Center for Formal Epistemology of Carnegie Mellon University, Pittsburgh, titled "Reclaiming Markov in Entangled Structures of Deterministic Causal Knowledge – Part 1: Epistemic Contours" (5pm, Baker Hall 150). This talk will concentrate on the observation that in many cases of causal reasoning non-causal, non-directional knowledge is drawn on and computed efficiently and consistently, although reasoning with this kind of knowledge seems to violate the causal Markov condition in standard Bayes net causal models. Examples are found in causal decision theory, where modeling Newcomb’s paradox (in its original formulation) or the prisoners’ dilemma seemingly yields counter-intuitive solutions, or in cases of inter-level (e. g., genuine bottom-up or top-down) causation that are usually simply collapsed to “flat” models incorporating (unanalyzed) cross-level mechanisms. Intermediate stages in the process of reducing theories, learning, or modeling given situations (e. g., stages with intensionally separated but extensionally equal variables) find no formalized expression in causal models obeying Markov. Embedding “entangled” variables in causal models renders those models non-Markovian. If we want to stick with these models, how can Markov be reclaimed?

Part 2 of the talk, "The Principle of Explanatory Dominance", will take place on 7 Dec 2011 (5pm, Baker Hall 150).

Download the abstract here as PDF.

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

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

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!

## Talk "Computing Non-causal Knowledge for Causal Reasoning" (11 June 2011)

*workshop on computational metaphysics*(June 11th 2011), Roland Poellinger will give a talk titled "Computing Non-Causal Knowledge for Causal Reasoning". The workshop at the Munich Center for Mathematical Philosophy (MCMP) featured speakers like Ed Zalta from Stanford and Stephan Hartmann from Tilburg. Watch the talk online on iTunes U!

## Talk "Disentangling Nets for Causal Inference" @ DGPhil 2011 (13 Sept 2011)

On occasion of the MCMP workshop on mathematical philosophy (13 & 14 Sep 2011) as part of the DGPhil conference 2011, Roland Poellinger gave a short presentation titled "Disentangling Nets for Causal Inference". The topics: What problems arise, when Bayes net causal models are augmented by the insertion of non-causal, non-directional knowledge? How can structural knowledge about given situations be enriched to decide about the utility of available semi-DAGs? And is there any possibility to regain the Markov condition for consistent computation of causal claims from graphs that unify directional and undirectional information? Watch the talk on iTunes U!

## Doctoral Thesis "Concrete Causation. About the Structures of Causal Knowledge."

Roland Poellinger's doctoral thesis "Concrete Causation. About the Structures of Causal Knowledge" (supervisor: Godehard Link - logic; further comittee members: Thomas Augustin, statistics, Klaus Schulz, computational linguistics, and C. Ulises Moulines, philosophy of science) centers about theories of causation, their interpretation and embedding in metaphysical-ontological questions, as well as the application of such theories in the context of science or decision theory. The main proposal is the development of a framework for integrating causal and non-causal knowledge in unified structures and the definition of methods for consistent inference of causal claims from such patterns. Applications are problems of (causal) decision theory or the representation of logical-mathematical, synonymical, as well as reductive relationships in efficiently structured, operational nets of belief propagation.

The dissertation concludes the PhD project comprising the international conference "Concrete Causation" (2010) and the appertaining channel in iTunes U containing recordings of the "Concrete Causation" conference talks and of further research.

Download a summary of the thesis here [PDF].

[Keywords: causal modeling, interventionist account of causation, DAG, Bayesian networks, intervention, causal knowledge pattern, epistemic contour, subjective causation, Newcomb's paradox, prisoners' dilemma, causal Markov condition, Judea Pearl, David Lewis, Wolfgang Spohn]

## Talk "Strukturen kausalen Wissens" (5. Mai 2011)

Am 5. Mai 2011 findet eine weitere Session in der "Research Seminar Series on Foundations of Statistics" am Institut für Statistik der LMU statt. Roland Poellinger wird unter dem Titel "Strukturen kausalen Wissens" zum Thema Bayesnetze und automatisierbare Kausalinferenz sprechen:

Die Frage nach der Ontologie oder der Beschreibung von Kausalzusammenhängen kreist immer wieder um das Verhältnis von Determinismus und (ontologischem oder deskriptiven) Indeterminismus innerhalb einer Theorie der Verursachung. Einige prominente Ansätze gründen die Kausalanalyse allein auf Korrelationen oder auf geeignet eingeschränkte Anforderungen an statistische Abhängigkeiten. Etliche Fallstricke und kontraintuitive Gegenbeispiele vereiteln allerdings dieses Vorhaben und erzwingen weitere Methodenverfeinerung. Judea PEARL (2000/2009) basiert seinen Analyseansatz auf die Abbildung von statistischen Abhängigkeiten in so genannten Bayesnetzen und formuliert seinen deterministischen Kausalbegriff mithilfe von systematischen, strukturellen Manipulationen solcher Bayesnetze.^{}Der Vortrag soll als "Work-in-Progress" auf das Format der Bayesnetze eingehen, automatische Verfahren der Netzgenerierung vorstellen und dies in den Zusammenhang mit einem deterministischen Verständnis von Kausalität bringen, welches im Kern (notwendigerweise) epistemisch ausgerichtet ist.