Convergence to the truth is viewed with some ambivalence in philosophy of science. On the one hand, methods of inquiry that lead to the truth in the limit are prized as marks of scientific rationality. But an agent who, by using some method, expects to always converge to the truth seems to fail a minimum standard of epistemic modesty. This point was recently brought home by Gordon Belot in his critique of Bayesian epistemology. In this paper I will study convergence to the truth theorems within the framework of Edward Nelson’s radically elementary probability theory. This theory provides an enriched conceptual framework for investigating convergence and gives rise to an appropriately modest from of Bayesianism.
The seminar is concerned with applying formal methods to fundamental issues, with an emphasis on probabilistic reasoning, decision theory and games. In this context “logic” is broadly interpreted as covering applications that involve formal representations. The topics of interest have been researched within a very broad spectrum of different disciplines, including philosophy (logic and epistemology), statistics, economics, and computer science. The seminar is intended to bring together scholars from different fields of research so as to illuminate problems of common interest from different perspectives. Throughout each academic year, meetings are regularly presented by the members of the seminar and distinguished guest speakers.
details tba
02/08/2019 Faculty House, Columbia University
4:00 PM
03/22/2019 Faculty House, Columbia University
4:00 PM
04/19/2018 Faculty House, Columbia University
4:00 PM
A number of puzzles concerning how truth-ascriptions are grounded have recently been discovered by several theorists, following Fine (2010). Most previous commentators on these puzzles have taken them to shed light on the theory of ground. In this paper, I argue that they also shed light on the theory of truth. In particular, I argue that the notion of ground can be deployed to clearly articulate one strand of deflationary thinking about truth, according to which truth is “metaphysically lightweight.” I will propose a ground-theoretic explication of the (entirely bearable) lightness of truth, and then show how this broadly deflationary view yields a novel solution to the puzzles concerning how truth is grounded. So, if the proposal I sketch is on target, the theory of truth and the theory of ground interact fruitfully: we can apply the notion of ground to offer a clear explication of the deflationist claim that truth is “metaphysically lightweight” that both captures the motivations for that claim and solves the puzzles.
In public discourse, but also in political theory, the opinion prevails, that democracy is incompatible with aspirations of truth. Some assume, in the Hobbesian tradition, that civic peace requires that truth assertions be restricted to science and religion (normative positivism), whereas the political sphere is constituted by interests, bargaining and collective decisions based on interests, bargaining and rules of aggregation, be they implicit or explicit. In this perspective Collective Choice as preference aggregation is paradigmatic for the understanding of democracy. Postmodernist and neo-pragmatist thought dismisses truth, because it threatens solidarity and belonging. Libertarian political thought relies on market mechanisms reducing citizens to consumers and producers of material and immaterial goods like security and welfare. Accounts of deliberative democracy focus on reasoning in the public sphere but dismiss a realistic understanding of truth, because it is thought to threaten collective and individual self-determination.
In my talk I will argue that a realistic understanding of empirical and normative truth is compatible, even necessary, for an adequate understanding of democracy, that truth assertions do not threaten civic peace, that postmodernist relativity undermines democratic practice, that libertarian market-orientation is incompatible with the status of citizens in democracy and that even deliberative, but anti-realist, accounts of democracy do not allow for an adequate understanding of democracy. My argument is based on a Davidsonian, or pragmatist, understanding of truth, therefore one might say: it critizises normative positivism, postmodernism, libertarianism, and critical theory using pragmatist insights.
Julian Nida-Rümelin presently holds a chair for philosophy and political theory at Ludwig-Maximilians-Universität München, is a member of the European Academy of Sciences, was president of the German Philosophical Association (DGphil) and state-minister for culture and media in the first government of Gerhard Schröder. The topics of his books include Democracy as Cooperation (1999); Democracy and Truth (2006), translated in Chinese and Italian, Philosophy and the form of Life (2009), Realism (2018) and A Theory of Practical Reason (2020, forthcoming, de Gruyter and PUP).
Generous support provided by the New York Institute of Philosophy.
With responses from Mark Siderits (Illinois State University)
ABSTRACT: Buddhist philosophers often draw a distinction between two different kinds of truth: conventional truth (saṃvṭi-satya) and ultimate truth (paramārtha-satya). Abhidharma Buddhists philosophers typically understand this distinction in terms of an ontological distinction between two different kinds of entities: ultimately real entities (paramārtha-sat) and conventionally real entities (saṃvṛti-sat). Similar to contemporary philosophical discussions about ordinary objects, Buddhist philosophers debate the ontological status of conventional entities and the semantics of discourse concerning them. Mark Siderits (2015, 2021, 2022) has influentially argued for an eliminitivist position he calls “Buddhist reductionism” that interprets the Abhidharma position as one that denies conventional entities exist but that retains discourse involving apparent reference to them. However, in a recent article Kris McDaniel (2019), a prominent defender of ontological pluralism, challenges that view by proposing that the Abhidharma Buddhist distinction between conventional truth and ultimate truth be “defined up” from a more basic distinction between two different ways an entity can exist: conventionally or ultimately. In this paper I argue that Saṃghabhadra’s account of conventional reality and truth does lends itself well to McDaniel’s proposal but I will also argue that the account of conventional and ultimate truth that results differs in important ways from the models he offers. I will end by offering a modification of McDaniel’s account of conventional truth that is derived from Saṃghabhadra’s pluralist ontology. That view will, unlike the views suggested by both Siderits and McDaniel, allow for there to be ultimate truths about what is conventionally true.
Dinner will be kindly offered by the Columbia University Seminars.
RSVP is required for dinner. Please email Lucilla with eating requirements at lm3335@columbia.edu.
The International Conference on Brain Informatics (BI) series has established itself as the world’s premier research conference on Brain Informatics, which is an emerging interdisciplinary and multidisciplinary research field that combines the efforts of Cognitive Science, Neuroscience, Machine Learning, Data Science, Artificial Intelligence (AI), and Information and Communication Technology (ICT) to explore the main problems that lie in the interplay between human brain studies and informatics research.
The 16th International Conference on Brain Informatics (BI’23) provides a premier international forum to bring together researchers and practitioners from diverse fields for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on brain Informatics research, brain-inspired technologies and brain/mental health applications.
The key theme of the conference is “Brain Science meets Artificial Intelligence“.
The BI’23 solicits high-quality original research and application papers (both full paper and abstract submissions). Relevant topics include but are not limited to:
- Track 1: Cognitive and Computational Foundations of Brain Science
- Track 2: Human Information Processing Systems
- Track 3: Brain Big Data Analytics, Curation and Management
- Track 4: Informatics Paradigms for Brain and Mental Health Research
- Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
Keynote Speakers
Professor Emery N. Brown
MIT, Massachusetts General Hospital, USA
Profile: Emery Neal Brown is the Warren M. Zapol Professor of Anesthesia at Harvard Medical School and at Massachusetts General Hospital (MGH), and a practicing anesthesiologist at MGH. At MIT he is the Edward Hood Taplin Professor of Medical Engineering and professor of computational neuroscience, the Associate Director of the Institute for Medical Engineering and Science, and the Director of the Harvard–MIT Program in Health Sciences and Technology. Brown is one of only 19 individuals who has been elected to all three branches of the National Academies of Sciences, Engineering, and Medicine, as well as the first African American and the first anesthesiologist to be elected to all three National Academies.
Professor Bin He
Carnegie Mellon University, USA
Profile: Bin He is the Trustee Professor of Biomedical Engineering, Professor of the Neuroscience Institute, and Professor by courtesy of Electrical and Computer Engineering at Carnegie Mellon University. Dr. He has made significant research and education contributions to the field of neuroengineering and biomedical imaging, including functional biomedical imaging, noninvasive brain-computer interface (BCI), and noninvasive neuromodulation. His pioneering research has helped transforming electroencephalography from a 1-dimensional detection technique to 3-dimensional neuroimaging modality. His lab demonstrated for the first time for humans to fly a drone and control a robotic arm just by thinking about it using a noninvasive BCI. He is an elected Fellow of International Academy of Medical and Biological Engineering (IAMBE), American Institute of Medical and Biological Engineering (AIMBE), Biomedical Engineering Society (BMES), and IEEE. Dr. He served as a Past President of the IEEE Engineering in Medicine and Biology Society, the Editor-in-Chief of IEEE Transactions on Biomedical Engineering from 2013-2018, the Chair of the International Academy of Medical and Biological Engineering from 2018-2021. Dr. He has been a Member of NIH BRAIN Initiative Multi-Council Working Group from 2014-2019.
Professor John Ngai
NIH BRAIN Initiative, USA
Profile: John J. Ngai, Ph.D., is the Director of the NIH’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Dr. Ngai earned his bachelor’s degree in chemistry and biology from Pomona College, Claremont, California, and Ph.D. in biology from the California Institute of Technology (Caltech) in Pasadena. He was a postdoctoral researcher at Caltech and at the Columbia University College of Physicians and Surgeons before starting his faculty position at the University of California at Berkeley. During more than 25 years as a Berkeley faculty member, Dr. Ngai has trained 20 undergraduate students, 24 graduate students and 15 postdoctoral fellows in addition to teaching well over 1,000 students in the classroom. His work has led to the publication of more than 70 scientific articles in some of the field’s most prestigious journals and 10 U.S. and international patents. Dr. Ngai has received many awards including from the Sloan Foundation, Pew Charitable Trusts, and McKnight Endowment Fund for Neuroscience. As a faculty member, Dr. Ngai has served as the director of Berkeley’s Neuroscience Graduate Program and Helen Wills Neuroscience Institute. He has also provided extensive service on NIH study sections, councils and steering groups, including as previous co-chair of the NIH BRAIN Initiative Cell Census Consortium Steering Group. Dr. Ngai oversees the long-term strategy and day-to-day operations of the NIH BRAIN Initiative as it strives to revolutionize our understanding of the brain in both health and disease.
Professor Helen Mayberg
Icahn School of Medicine at Mount Sinai, USA
Profile: Helen Mayberg is a neurologist recognized for her neuroimaging studies of brain circuits in depression and their translation to the development of deep brain stimulation as a novel therapeutic for treatment resistant patients. Born and raised in Southern California, she received a BA in Psychobiology from UCLA and a MD from the University of Southern California, then trained in Neurology at Columbia’s Neurological Institute in New York and did a research fellowship in nuclear medicine at Johns Hopkins. She had early academic appointments at Johns Hopkins and the University of Texas Health Sciences Center in San Antonio, held the inaugural Sandra Rotman Chair in Neuropsychiatry at the University of Toronto, the first Dorothy C. Fuqua Chair in Psychiatric Imaging and Therapeutics at Emory University and is now the Mount Sinai Professor of Neurotherapeutics at the Icahn School of Medicine where she is founding Director of the Nash Family Center for Advanced Circuit Therapeutics. She is a member of the both the National Academy of Sciences and the National Academy of Medicine as well as the National Academy of Inventors and American Academy of Arts and Sciences.
Professor Vinod Goel
York University, Canada
Profile: Vinod Goel is a professor of cognitive neuroscience at York University, Toronto, Canada. He completed his PhD in cognitive science at UC-Berkeley, and received postdoctoral training in neuroscience at the NIH (NINDS) and the Wellcome Department of Cognitive Neurology, Institute of Neurology, UCL, UK. He has made significant empirical contributions to our understanding of the roles of prefrontal cortex in real-world problem solving and reasoning, hemispheric asymmetry in prefrontal cortex, and models of rationality, using the methodologies of fMRI and lesion studies. He has most recently completed a book reconstructing the role of rationality in human behavior entitled “Reason and Less: Pursuing Food, Sex, and Politics” (The MIT Press, 2022). His current project is to explore the implications of this work on our understanding of reason and legal responsibility.
Professor Amy Kuceyeski
Cornell University, USA
Profile: Amy Kuceyeski is an Associate Professor of Mathematics and Neuroscience in Radiology at Weill Cornell Medicine and the Computational Biology Department at Cornell University. She is the director of the Computational Connectomics (CoCo) Laboratory and the Machine Learning in Medicine group at Cornell. Over the past 14 years, she has been working to understand the human brain using quantitative modeling approaches, including machine learning, to map anatomical and physiological characteristics to behavior. Specifically, she is interested in understanding how brains recover from injury so we can devise strategies, possibly via non-invasive neuromodulation, to support natural recovery processes. She also performs research at the intersection of biological and artificial neural networks that aims to understand how human brains process incoming visual information.
Professor Patrick Purdon
Harvard Medical School, USA
Profile: Patrick L. Purdon, Ph.D., is an Associate Professor of Anaesthesia at Harvard Medical School and the Nathaniel M. Sims Endowed Chair in Anesthesia Innovation and Bioengineering at Massachusetts General Hospital. Dr. Purdon received his A.B. in Engineering Sciences from Harvard College in 1996, his M.S. in Electrical Engineering from MIT in 1998, and his Ph.D. in Biomedical Engineering from MIT in 2005. Dr. Purdon’s research in neuroengineering encompasses the mechanisms of anesthesia, Alzheimer’s disease and brain health, anesthesia and the developing brain, neural signal processing, and the development of novel technologies for brain monitoring. He has published over 90 peer-reviewed publications, is an inventor on 16 pending patents, and is a Fellow of the American Institute for Medical and Biological Engineering. Dr. Purdon has won numerous awards, including the prestigious National Institutes of Health Director’s New Innovator Award.
Important Dates
- 15 April 2023: Full paper submission deadline
- 20 April 2023: Workshop proposal deadline
- 10 May 2023: Abstract presentation submission deadline
- 30 May 2023: Final paper and abstract acceptance notification
- 20 Jun 2023: Accepted paper and abstract registration deadline
- 1-3 Aug 2023: The Brain Informatics Conference
Paper Submission and Publications
Full Paper (Regular):
1. 9-12 pages are strongly encouraged for the regular papers including figures and references in Springer LNCS Proceedings format(https://www.springer.com/us/computer-science/lncs/conference-proceedings-guidelines). Over length papers will be charged for 100$ per page.
2. All papers will be peer-reviewed and accepted based on originality, significance of contribution, technical merit, and presentation quality.
3. All papers accepted (and all workshop & special sessions’ full-length papers) will be published by Springer as a volume of the Springer-Nature LNAI Brain Informatics Book Series(https://link.springer.com/conference/brain).
Abstract (Only for Workshops/Special Sessions):
Research abstracts are encouraged and will be accepted for presentations in an oral presentation format and/or poster presentation format. Each abstract submission should include the title of the paper and an abstract body within 500 words. The abstract will not be included in the conference proceedings to be published by Springer.
Journal Opportunities:
High-quality BI conference papers will be nominated for a fast-track review and publication at the Brain Informatics Journal, (https://braininformatics.springeropen.com/) an international, peer-reviewed, interdisciplinary Open Access journal published by Springer Nature. Discount or no open access article-processing fee will be charged for BI conference paper authors.
Special Issues & Books Opportunities:
Workshop/special session organizers and BI conference session chairs may consider and can be invited to prepare a book proposal of special topics for possible book publication in the Springer-Nature Brain Informatics & Health Book Series (https://www.springer.com/series/15148), or a special issue at the Brain Informatics Journal.
Poster-Conference Publication
1. Accepted full papers will be selected to publish in the Brain Informatics Journal upon revision.
2. Discount or no article-processing fee will be charged for authors of Brain Informatics conference (https://braininformatics.springeropen.com/).
3. The organizers of Workshops and Special-Sessions are invited to prepare a book proposal based on the topics of the workshop/special session for possible book publication in the Springer-Nature Brain Informatics and Health book series (http://www.springer.com/series/15148).
The Saul Kripke Center is pleased to announce that James Walsh (Assistant Professor, Philosophy, NYU) will deliver a talk on Friday, May 10th, 2024, from 4:15 to 6:15 pm at the CUNY Graduate Center (Room 9207). The talk is free and open to all.
Title: Modal definability and Kripke’s theory of truth
Abstract: In Outline of a Theory of Truth, Kripke introduces some of the central concepts of the logical study of truth and paradox. He informally defines some of these–such as groundedness and paradoxicality–using modal locutions. We introduce a modal language for regimenting Kripke’s informal definitions and characterize the modally definable sets. Though groundedness and paradoxicality are expressible in the modal language, we prove that intrinsicality–which Kripke emphasizes but does not define modally–is not.