Alyssa Ney will be giving a talk entitled “Microphysical Causation and the Case for Physicalism” on Friday, October 31st. The talk will be held from 4:10-6:00pm in Room 507, Philosophy Hall (Columbia).The abstract for her talk is below. Hope to see you all there!
“Microphysical Causation and the Case for Physicalism”
Alyssa Ney, University of Rochester
There will be a dinner after the talk. If you are interested, please email nyphilsci@gmail.com as soon as possible so that I can make the reservation for the appropriate number of people (please note that only the speaker’s dinner will be covered). If you have any other questions, please email nyphilsci@gmail.com.
RUTGERS UNIVERSITY DEPARTMENT OF STATISTICS AND BIOSTATISTICS www.stat.rutgers.edu
Seminar θSpeaker: Professor Deborah Mayo, Virginia Tech
Title: Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance
Time: 3:20 – 4:20pm, Wednesday, December 3, 2014 Place: 552 Hill Center
ABSTRACT
Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance Getting beyond today’s most pressing controversies revolving around statistical methods, I argue, requires scrutinizing their underlying statistical philosophies.Two main philosophies about the roles of probability in statistical inference are probabilism and performance (in the long-run). The first assumes that we need a method of assigning probabilities to hypotheses; the second assumes that the main function of statistical method is to control long-run performance. I offer a third goal: controlling and evaluating the probativeness of methods. An inductive inference, in this conception, takes the form of inferring hypotheses to the extent that they have been well or severely tested. A report of poorly tested claims must also be part of an adequate inference. I develop a statistical philosophy in which error probabilities of methods may be used to evaluate and control the stringency or severity of tests. I then show how the “severe testing” philosophy clarifies and avoids familiar criticisms and abuses of significance tests and cognate methods (e.g., confidence intervals). Severity may be threatened in three main ways: fallacies of statistical tests, unwarranted links between statistical and substantive claims, and violations of model assumptions.
David Papineau (King’s College London) will be giving our first talk of the semester, entitled “Physicalism without Causal Closure” on Monday, March 2nd. The talk will be held from 4:00-6:00pm in Room B01, Goddard Hall (NYU). The abstract for his talk is below. Hope to see you all there!
“Physicalism without Causal Closure“
David Papineau, Kings College London
The standard argument for physicalism assumes the causal closure of the physical. But if causation is a macroscopic phenomenon, in the thermodynamic sense, then it cannot be taken for granted that the physical realm is causally closed. I argue that there remain strong reasons for embracing physicalism, but that the case needs to carefully constructed. We need to make sure that we do not beg the question, and that we introduce causal notions at the right place in the argument.
There will be a dinner after the talk. If you are interested, please email nyphilsci@gmail.com as soon as possible so that I can make the reservation for the appropriate number of people (please note that all faculty and grad students are welcome, but only the speaker’s dinner will be covered). If you have any other questions, please email nyphilsci@gmail.com.
Relativity, Causality and Natural Selection
In this talk I’ll present an alternative causal structure for biological evolution. First the causalist and statisticalist perspectives on evolutionary fitness are analyzed, finding them to implicitly depend on each other, and hence cannot be individually fundamental. I argue that this can be seen as an instance of a relativistic perspective over evolutionary phenomena and, therefore, insoluble. New accounts of fitness, the struggle for life, and Natural Selection are developed under this interpretation. This biological relativism is unique in that it draws from General Relativity in physics, unlike previous theories that drew upon statistical mechanics or Newtonian dynamics. Some consequences of this relativism, like a mathematical law of evolutionary change, as well as new theoretical biological concepts to underpin it, are discussed. The law and theory are then applied to give examples of how cannon and problems within evolutionary theory and biology can be understood using these new methods.
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People from outside NYU: if this is your *first* time coming to the seminar, let them know so we can make sure you will have access to the building.
*~*~* Beer is $2. Bring CHANGE *~*~*
The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference,
2015 APS Annual Convention
Saturday, May 23 2:00 PM- 3:50 PM in Wilder
(Marriott Marquis 1535 B’way)
Presenters:
Andrew Gelman, Professor of Statistics & Political Science, Columbia University
Stephen Senn, Head of Competence Center for Methodology and Statistics (CCMS) Luxembourg Institute of Health
D.G. Mayo, Professor of Philosophy, Virginia Tech
Richard Morey, Session Chair & Discussant, Senior Lecturer School of Psychology, Cardiff University
Shamik Dasgupta (Princeton University). Topic: TBA.
Michael Strevens (NYU). Topic:
Title: The Mathematical Route to Causal Understanding
Abstract: Causal explanation is a matter of isolating the elements of the causal web that make a difference to the explanandum event or regularity (so I and others have argued). Causal understanding is a matter of grasping a causal explanation (so says what I have elsewhere called the “simple theory” of understanding). It follows that causal understanding is a matter of grasping the facts about difference-making, and in particular grasping the reasons why some properties of the web are difference-makers and some are not. Mathematical reasoning frequently plays a role in our coming to grasp these reasons, and in some causal explanations, deep mathematical theorems may do almost all the work. In these cases — such as the explanation why a person cannot complete a traverse of the bridges of Königsberg without crossing at least one bridge twice — our understanding seems to hinge more on our appreciation of mathematical than of physical facts. We have the sense that mathematics gives us physical understanding. But this is quite compatible with the explanation in question being causal in exactly the same sense as more unremarkable causal explanations.
Laura Franklin-Hall (NYU). Topic: TBA.
Kathryn Tabb (Columbia University)
Topic: (joint work with Ken Schaffner, Pittsburgh HPS) “Random Walks and Torturous Paths: Moving from the Descriptive to the Etiological in Psychiatry”
Metro Area Philosophy of Science Presents:
Elizabeth Miller (Yale),
Title: TBA.
Jonathan Bain (NYU)
What Explains the Spin-Statistics Connection?
The spin-statistics connection plays an essential role in explanations of non-relativistic phenomena associated with both field-theoretic and non-field-theoretic systems (for instance, it explains the electronic structure of solids and the behavior of Einstein-Bose condensates and superconductors). However, it is only derivable within the context of relativistic quantum field theory (RQFT) in the form of the Spin-Statistics Theorem; and moreover, there are multiple, mutually incompatible ways of deriving it. This essay attempts to determine the sense in which the spin-statistics connection can be said to be an essential property in RQFT, and how it is that an essential property of one type of theory can figure into fundamental explanations offered by other, inherently distinct theories.
The Causality in the Sciences conference series brings together philosophers and scientists to explore various aspects of causality. This 12th conference in the series will focus on the relationship between time and causality.
The conference will explore all facets of the relationship between time and causality across philosophy, computation, and specific scientific disciplines. Some key themes include: arrow of time * causal inference from time series data * role of time in causal perception and judgment * time and causal metaphysics * applications to longitudinal datasets
Abstracts should be no more than 500 words, submitted via easychair: https://easychair.org/conferences/?conf=tacits2017
Important dates:
March 15 – Submission
April 15 – Notification of acceptance
Organizers: Samantha Kleinberg (Stevens), Michael Strevens (NYU)
Steering Committee: Phyllis Illari (UCL), Bert Leuridan (University of Antwerp), Julian Reiss, (Durham), Federica Russo (UvA), Erik Weber (Ghent) Jon Williamson (Kent)
In light of the chaos and fear caused for travel to the US by the possible immigration ban, and resulting calls to boycott US conferences, we have discussed whether we should go ahead with TaCits NY in June http://tacits.stevens.edu/. Given the work already put in by local organisers, and the fact that US academics would also appreciate support just now, we have decided to continue.
We are, however, very aware that some people may be unable or unwilling to travel to the conference. We ask that citizens of countries who wish to submit abstracts, but are potentially affected by the ban, get in touch with us, so that we can see whether it is possible to make any arrangements for some kind of remote access. We know that this is at best a half-solution, and apologise for that.
All the very best,
Causality in the Sciences steering committee
What does it mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. (What exactly was the actual cause of the car accident or the medical problem?) The philosophy literature has been struggling with the problem of defining causality since the days of Hume, in the 1700s. Many of the definitions have been couched in terms of counterfactuals. (C is a cause of E if, had C not happened, then E would not have happened.) In 2001, Judea Pearl and I introduced a new definition of actual cause, using Pearl’s notion of structural equations to model counterfactuals. The definition has been revised twice since then, extended to deal with notions like “responsibility” and “blame”, and applied in databases and program verification. I survey the last 15 years of work here, including joint work with Judea Pearl, Hana Chockler, and Chris Hitchcock. The talk will be completely self-contained.