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Talk "Newcomb's Paradox – Wissen ordnen und erschließen in hybriden Netzen" @ Foundations of Statistics (16 May, 2012) |
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Wednesday, 16 May 2012 00:00 |
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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." |
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Last Updated on Wednesday, 16 May 2012 11:08 |
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Doctoral Thesis "Concrete Causation. About the Structures of Causal Knowledge." |
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Monday, 06 June 2011 19:01 |
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Roland Poellinger's doctoral thesis "Concrete Causation. About the Structures of Causal Knowledge" (supervisor: Godehard Link - logic; further comittee members: Thomas Augustin, statistics, and Klaus Schulz, computational linguistics) 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] |
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Last Updated on Friday, 20 January 2012 10:21 |
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Talk "Computing Non-causal Knowledge for Causal Reasoning" (11 June 2011) |
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Monday, 06 June 2011 19:01 |
 On occasion of the workshop on computational metaphysics (June 11th 2011), Roland Poellinger will give a talk titled "Computing Non-Causal Knowledge for Causal Reasoning". Find more information about the other speakers (including Ed Zalta from Stanford and Stephan Hartmann from Tilburg), times, and location on the MCMP website. Plus: Watch the talk on iTunes U! |
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Last Updated on Tuesday, 08 November 2011 01:56 |
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Talk "Reclaiming Markov in Entangled Structures of Deterministic Causal Knowledge" @ CMU Pittsburgh (30 Nov and 7 Dec 2011, 5pm) |
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Monday, 06 June 2011 19:01 |
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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. |
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Last Updated on Monday, 05 December 2011 00:41 |
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Talk "What is Causal Knowledge?" @ LMU Centrum für Informations- und Sprachverarbeitung (19 Jan, 2012) |
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Friday, 20 January 2012 00:00 |
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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).
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Last Updated on Friday, 20 January 2012 17:29 |
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Typesetting KM Calculus with LaTeX |
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Saturday, 17 October 2009 18:05 |
 KMcalc is a macro package for LaTeX, which facilitates typesetting of box diagrams in the natural deduction calculus à la Kalish & Montague. A detailed description of the calculus can be found in: Kalish, Donald; Montague, Richard; Mar, Gary: Logic - Techniques of Formal Reasoning. Second Edition. Harcourt Brace Jovanovich, Publishers, 1980. Features of the package are the correct placement of annotations and automatic line numbering. User input is being processed line by line (not column-wise) with specific commands for different line types.The package supports continued lines as well as cross-referencing within the diagram with the ref command.
Click here to download the package (which is published under the lppl LaTeX Public Project Licence) and a how-to manual (a PDF reference document) from rforge.com. |
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Last Updated on Tuesday, 08 November 2011 01:57 |
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