Welcome to logic.rforge
Auf Du und Du mit Statistik und Co. (Oktober 2016)
Im Wintersemester 2016/17 veranstaltet das Studienbüro Statistik zusammen mit dem Center for Mathematical Philosophy (MCMP) der LMU erneut das Propädeutikum "Auf Du und Du mit Statistik und Co." als Einführung in formal(isiert)es Denken und empirisches Argumentieren.
Idee und Motivation: Formale Techniken und Argumentationen besitzen auch in den Sozial-, Geistes- und Wirtschaftswissenschaften eine große, und immer weiter wachsende, Bedeutung, stellen aber für viele, darauf nicht so gut vorbereitete Studierende eine sehr große Hürde dar. Im Rahmen eines fachübergreifenden Propädeutikums sollen nichtmathematikaffine Studierende der LMU sanft in die Formalisierung eingeführt und mit wesentlichen Techniken (wieder) vertraut gemacht werden.
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).
"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.
Typesetting KM Calculus with LaTeX
"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.
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)
- Talk "Computing Non-causal Knowledge for Causal Reasoning" (11 June 2011)
- Doctoral Thesis "Concrete Causation. About the Structures of Causal Knowledge."
- Research-based teaching with workshop videos