Upcoming Workshops
Information, Causal Models and Model Diagnostics
April 1415, 2018
Cosponsored by the InfoMetrics Institute and Dietrich College of Humanities & Social Sciences
The fundamental concepts of information theory are being used for modeling and inference of problems across most disciplines, such as biology, ecology, economics, finance, physics, political sciences and statistics (for examples, see Fall 2014 conference celebrating the fifth anniversary of the InfoMetrics Institute).
The objective of spring 2018 workshop is to study the interconnection between information, information processing, modeling (or model misspecification and diagnostics) and causal inference. In particular, it focuses on modeling and causal inference with an informationtheoretic perspective.
Background: Generally speaking, causal inference deals with inferring that A causes B by looking at information concerning the occurrences of both, while probabilistic causation constrains causation in terms of probabilities and conditional probabilities given interventions. In this workshop we are interested in both. We are interested in studying the modeling framework  including the necessary observed and unobserved required information  that allows causal inference. In particular we are interested in studying modeling and causality within the infometrics  the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information  framework. Unlike the more 'traditional' inference, causal analysis goes a step further: its aim is to infer not only beliefs or probabilities under static conditions, but also the dynamics of beliefs under changing conditions, such as the changes induced by treatments or external interventions.
This workshop will (i) provide a forum for the dissemination of new research in this area and will (ii) stimulate discussion among research from different disciplines. The topics of interest include both, the more philosophical and logical concepts of causal inference and modeling, and the more applied theory of inferring causality from the observed information. We welcome all topics within the intersection of infometrics, modeling and causal inference, but we encourage new studies on information or informationtheoretic inference in conjunction with causality, model specification (and misspecification). These topics may include, but are not limited to:
 Causal Inference and Information
 Probabilistic Causation and Information
 Nonmonotonic Reasoning, Default Logic and InformationTheoretic Methods
 Randomized Experiments and Causal Inference
 Nonrandomized Experiments and Causal Inference
 Modeling, Model Misspecification and Information
 Causal Inference in Network Analysis
 Causal Inference, Instrumental Variables and InformationTheoretic Methods
 Granger Causality and Transfer Entropy
 Counterfactuals, Causality and Policy Analysis in Macroeconomics
PROGRAM COMMITTEE
 Richard Scheines, CoChair (CMU)
 Teddy Seidenfeld, CoChair (CMU)
 Amos Golan (American University)
CONFIRMED INVITED SPEAKERS AND DISCUSSANTS
 Thomas Augustin (Department of Statistics, University of Munich)
 Gert de Cooman (SYSTeMS Research Group, Ghent University)
 Erik Hoel (Department of Biological Sciences, Columbia University)
 Dominik Janzing (Max Planck Institute for Intelligent Systems)
 David Krakauer (Complexity, Info, Causality; Santa Fe Institute)
 Sarah E. Marzen (MIT Physics of Living Systems)
 Kun Zhang (Department of Philosophy, CMU)
Save the Date – NASSLLI 2018
June 2529, 2018We are excited to announce that in June 2018, the Department of Philosophy, with support from across the campus, will host the upcoming North American Summer School in Logic, Language and Information. NASSLLI is a biennial event inaugurated in 2001, which brings together faculty and graduate students from around the world, for a week of interdisciplinary courses on cutting edge topics at the intersection of philosophy, linguistics, computer science and cognitive science. The Summer School aims to promote discussion and interaction between students and faculty in these fields. High level introductory courses allow students in one field to find their way into related work in another field, while other courses focus on areas of active research. With its focus on formalization and on crossdisciplinary interactions, NASSLLI is a natural fit for us here at CMU. We are delighted to be hosting. The summer school will take place June 2529, 2018, with preparatory events June 2324.
Past Workshops and Conferences
Category Theory Octoberfest
October 2829, 2017
View slides from Dana Scott’s Talk: What is Explicit Mathematics?
The 2017 Category Theory Octoberfest will be held on the weekend of Saturday, October 28 and Sunday, October 29 at Carnegie Mellon University in Pittsburgh. Following the tradition of past Octoberfests, this is intended to be an informal meeting, covering all areas of category theory and its applications.
Talks by PhD students and young researchers are particularly encouraged!
Details and travel information can be found here:
https://www.andrew.cmu.edu/user/awodey/CToctoberfest/Octoberfest.html
Registration:
There is no registration fee. Registration is optional, but participants are requested to contact the organizers in advance, especially if they would like to give a talk. To register and/or submit a talk, please send email to the organizers with the following information: your name, will you give a talk (yes or no), the title of your talk (if yes).
Organizers:
Modality and Method Workshop
June 9 and 10, 2017  Center for Formal Epistemology
Margaret Morrison 103
This workshop showcases cuttingedge applications of modality to an intriguing range of methodological issues, including reference, action, causation, information, and the scientific method. Following the tradition of CFE workshops, it is structured to provide ample time for real interaction with, and between, the speakers.
All are welcome to attend.
For more information please email.
Workshop Speakers:
Alexandru Baltag Oxford University 
Title: Knowing Correlations: how to use questions to answer other questions Abstract: Informationally, a question can be encoded as a variable, taking various values ("answers") in different possible worlds. If, in accordance to the recent trend towards an interrogative epistemology, "To know is to know the answer to a question" (Schaffer), then we are lead to paraphrasing the Quinean motto: To know is to know the value of a variable. There are two issues with this assertion. First, questions are never investigated in isolation: we answer questions by reducing them to other questions. This means that the proper object of knowledge is uncovering correlations between questions. To know is to know a functional dependence between variables. Second, when talking about empirical questions/variables, the exact value/answer might not be knowable, and instead only "feasible answers" can be known: this suggests a topology on the space of possible values, in which the open neighborhoods of the actual value represent the feasible answers (knowable approximations of the actual value). A question Q epistemically solves question Q' if every feasible answer to Q' can be known if given some good enough feasible answer to Q. I argue that knowability in such an empirical context amounts to the continuity of the functional correlation. To know is to know a continuous dependence between variables. I investigate a logic of epistemic dependency, that can express knowledge of functional dependencies between (the values of) variables, as well as dynamic modalities for learning new such dependencies. This dynamic captures the widespread view of knowledge acquisition as a process of learning correlations (with the goal of eventually tracking causal relationships in the actual world). There are interesting formal connections with Dependence Logic, Inquisitive Logics, van Benthem's Generalized Semantics for first order logic, Kelly's notion of gradual learnability (as well as the usual learningtheoretic notion of identifiability in the limit), and philosophically with Situation Theory and the conception of "informationascorrelation". 
Adam Bjorndahl Carnegie Mellon University 
Title: Logic and Topology for Knowledge, Knowability, and Belief Abstract: In recent work, Stalnaker (2006) proposes a logical framework in which belief is realized as a weakened form of knowledge. Building on Stalnaker's core insights, and using frameworks developed in (Bjorndahl 2016) and (Baltag et al. 2016), we employ topological tools to refine and, we argue, improve on this analysis. The structure of topological subset spaces allows for a natural distinction between what is known and (roughly speaking) what is knowable; we argue that the foundational axioms of Stalnaker’s system rely intuitively on both of these notions. More precisely, we argue that the plausibility of the principles Stalnaker proposes relating knowledge and belief relies on a subtle equivocation between an "evidenceinhand" conception of knowledge and a weaker "evidenceoutthere" notion of what could come to be known. Our analysis leads to a trimodal logic of knowledge, knowability, and belief interpreted in topological subset spaces in which belief is definable in terms of knowledge and knowability. We provide a sound and complete axiomatization for this logic as well as its unimodal belief fragment. We also consider weaker logics that preserve suitable translations of Stalnaker's postulates, yet do not allow for any reduction of belief. We propose novel topological semantics for these irreducible notions of belief, generalizing our previous semantics, and provide sound and complete axiomatizations for the corresponding logics. This is joint work with Aybüke Özgün. 
University of Pittsburgh 
Title: Classical Opacity Abstract: In Frege's wellknown example, Hesperus was known by the Greeks to rise in the evening, and Phosphorus was not known by the Greeks to rise in the evening, even though Hesperus is Phosphorus. A predicate F such that for some a and b, a=b, Fa and not Fb is said to be opaque. Opaque predicates appear to threaten the classical logic of identity. The responses to this puzzle in the literature either deny that there are cases of opacity in this sense, or deny that one can use classical quantificational logic when opacity is in play. In this paper we motivate and explore the view that there are cases of opacity and that classical quantificational logic is valid even when quantifying in to opaque contexts. We develop the logic of identity given these assumptions in the setting of higherorder logic. We identify a key choicepoint for such views, and then develop alternative theories of identity depending on how one makes this choice. In closing, we discuss arguments for each of the two theories. 
Melissa Fusco Columbia University 
Title: Deontic Modality and Classical Logic Abstract: My favored joint solution to the Puzzle of Free Choice Permission (Kamp 1973) and Ross's Paradox (Ross 1941) involves (i) giving up the duality of natural language deontic modals, and (ii) moving to a twodimensional propositional logic which has a classical Boolean character only as a special case. In this talk, I'd like to highlight two features of this radical view: first, the extent to which Boolean disjunction is imperiled by other natural language phenomena not involving disjunction, and second, the strength of the general position that natural language semantics must treat deontic, epistemic, and circumstantial modals alike. 
Dmitri Gallow University of Pittsburgh 
Title: Learning and Value Change Abstract: Accuracyfirst accounts of rational learning attempt to vindicate the intuitive idea that, while rationallyformed belief need not be true, it is nevertheless likely to be true. To this end, they attempt to show that the Bayesian’s rational learning norms are a consequence of the rational pursuit of accuracy. Existing accounts fall short of this goal, for they presuppose evidential norms which are not and cannot be vindicated in terms of the singleminded pursuit of accuracy. They additionally fail to vindicate the Bayesian norm of Jeffrey conditionalization; the responses to uncertain evidence which they do vindicate are not epistemically defensible. I propose an alternative account according to which learning rationalizes changes in the way you value accuracy. I show that this account vindicates the Bayesian’s norm of conditionalization in terms of the singleminded pursuit of accuracy, so long as accuracy is rationally valued. 
Franz Huber 
Title: The Modality underlying Causality Abstract: I will discuss the relationship between extended causal models, which represent two modalities (causal counterfactuals and normality), and counterfactual models, which represent one modality (counterfactuals). It is shown that, under a certain condition, extended causal models that are acyclic can be embedded into counterfactual models. The relevant condition is reminiscent of Lewis (1979) "system of weights or priorities" that governs the similarity relation of causal counterfactuals. In concluding I will sketch modal idealism, a view according to which the causal relationship is a minddependent construct. 
Kevin T. Kelly and Konstantin Genin 
Title: What is Statistical Deduction? Abstract: The philosophy of induction begins by drawing a line between deductive and inductive inference. That distinction is clear when empirical information can be modeled as a nontrivial proposition that restricts the range of theoretical possibilities—inference is deductive when every possibility of error is excluded by the premise. Recently, topological methods have been used with success to characterize the boundary between induction and deduction for propositional information of that kind. The basic idea is that that the possible, propositional information states constitute a topological space in which the deductively verifiable propositions are open sets. Then refutable propositions are closed sets, decidable propositions are closedopen, and more general topological concepts characterize the hypotheses that are decidable, verifiable, or refutable in the limit. A new justification of inductive inference emerges thereby—an inductive method is justified insofar as it achieves the best possible sense of success, given the topological complexity of the inference problem faced. That revealing, topological approach to empirical information does not apply directly to statistical inference, because statistical information typically rules out no theoretical possibilities whatever—the sample might just be very unlucky. For that reason, the received view in the philosophy of science has been that all statistical inference is inductive. However, some statistical inferences are evidently very similar to deductive inferences—e.g., rejecting a sharp null hypothesis or generating a confidence interval—whereas others are more similar to inductive inferences—e.g., accepting a sharp null hypothesis or selecting a statistical model. The basis for the analogy is that statistically deductive inferences are ''nearly deductive’’, in the sense that they are performed with a guaranteed low chance of error. The key to connecting the topologicalpropositional perspective on information with statistics is, therefore, to identify the unique topology for which the propositions that are verifiable with low chance of error are exactly the open propositions. In this talk, we show how to do just that. The result opens the door to a free flow of logical/topological insights into statistical methodology. 
Tamar Lando Columbia University 
Title: Topology and Measure in Logics for PointFree Space 
Workshop on Exploitation and Coercion
Nov 45, 2016  Center for Ethics & Policy
The Center for Ethics & Policy at Carnegie Mellon University invites paper abstracts for an inaugural Workshop on Ethics and Policy to be hosted November 45, 2016 at the CMU campus in Pittsburgh, PA. We are pleased to welcome Richard Arneson as our keynote speaker. In celebration of the 20th Anniversary of the publication of Alan Wertheimer's seminal work Exploitation, the theme for our inaugural workshop is "Exploitation and Coercion".
Download CFP
Attitudes and Questions Workshop
June 10 and 11, 2016  Center for Formal Epistemology
Question embedding in natural language allows a subject to be related to a question by either a (traditionally) propositional attitude like knowledge and forgetting, or an (apparently) inherently questionoriented predicate like asking or wondering. Attitudes held of questions are an important locus of research into the semantics of both interrogative clauses and clauseembedding verbs, closely connected with the notion of the answerhood conditions of a question, and with the operations of composition involved in combining these types of predicates with semantically heterogeneous arguments. The attitudes that relate us to questions are also of considerable epistemic interest, touching on the nature of the knowledge relation and on the way that questions structure our inquiries. This workshop aims to bring together a diverse group of experts on the semantics and epistemic issues raised by these phenomena, to promote exchange of theoretical perspectives and approaches, and to help to move forward current work on questions and attitudes.
Workshop Speakers:
Harvard University 
Sensitivity to false answers in indirect questions Abstract: 
Duke University 
Reducibility, George's challenge, and Intermediate Readings: In search for an Alternative Explanation Abstract: 
Massachusetts Institute of Technology 
Mention Some, Reconstruction, and Free Choice Abstract: 
École Normale Supérieure, 
Plurality effects and exhaustive readings of embedded questions

Institut Jean Nicod & Ecole Normale Supérieure 
Predicting the presuppositions triggered by responsive predicates Abstract: 
Carnegie Mellon University 
Simplicity and Scientific Questions Abstract: Ockham’s razor instructs the scientist to favor the simplest theory compatible with current information. There is a broad consensus that simplicity is a principal consideration guiding inductive inference in science. But that familiar observation raises several subtle questions. When is one theory simpler than another? And why should one prefer simpler theories if there is no guarantee that simpler theories are — in some objective sense — more likely to be true? We present a model of empirical inquiry in which simplicity relates answers to an empirical question, and is grounded in the underlying information topology, the topological space generated by the set of possible information states inquiry might encounter. We show that preferring simple theories is a necessary condition for optimally direct convergence to the truth, where directness consists in avoiding unnecessary cycles of opinion on the way to the truth. Our approach relates to linguistics in two ways. First, it illustrates how questions under discussion can shape simplicity and, hence, the course of theoretical science. Second, it explains how, and in what sense, empirical simplicity can serve as a theoretical guide in empirical linguistics. 
Carnegie Mellon University 
The False Belief Effect for know wh and its Conceptual Neighbors Abstract: 
Harvard University 
"Differentiating Contents" CFE/Linguistics Workshop
Saturday, December 5, 2015  Carnegie Mellon, Baker Hall, Dean’s Conference Room, 154R
A variety of phenomena have motivated researchers to distinguish between different types of linguistic content. One classical distinction is that made by Austin (1962) and Searle (1969) between the propositional content of utterances and their speech act force. Another classical distinction is that between assertoric and presupposed content (Frege 1893, Strawson 1950, Stalnaker 1974, inter alia). In recent years, a new distinction between atissue and not atissue content (Potts 2005, Simons et al. 2010) has been introduced, to some extent offered as a replacement for the asserted/presupposed distinction. One empirical domain where the atissue/not atissue distinction has been utilized by some researchers is in the study of evidentials, a category of linguistic forms which provide information about the speaker’s evidential relation to the (remaining) content of her utterance.
This one day workshop will bring together researchers with intersecting work on the nature of these distinctions, on the empirical evidence for them, and on how to model them.
Fifteenth conference on Theoretical Aspects of Rationality and Knowledge (TARK 2015)
Cosponsored by the Center for Formal Epistemology
June 46, 2015  Carnegie Mellon
Pitt/CMU Graduate Student Conference
March 2021, 2015  Carnegie Mellon
Locations: Mellon Institute, Room 348 (March 20) and Margaret Morrison, Room A14 (March 21)
Workshop on Simplicity and Causal Discovery
Cosponsored by the Center for Formal Epistemology
June 68, 2014  Carnegie Mellon
Modal Logic Workshop: Consistency and Structure
Cosponsored by the Center for Formal Epistemology
Saturday, April 12, 2014  Carnegie Mellon
Trimester: Semantics of Proofs and Certified Mathematics Trimester at the Institut Henri Poincare
April 7  July 11, 2014  Paris, France
Workshop: Philosophy of Physics
September 7, 2013
With Hans Halvorson (Princeton University) and James Weatherall (UC Irvine)
Conference: Type Theory, Homotopy Theory, and Univalent Foundations
September 2327, 2013  Barcelona, Spain
Workshop: Case Studies of Causal Discovery with Model Search
October 2527, 2013  Carnegie Mellon