## On Careers Outside the University

August 24, 2015

Zachary Ernst has a new blog, Goodbye, Academia!, which offers “thoughts, discussion, and guidance on leaving academia.”  Already there are a couple of excellent posts on interviewing and managing the risks of a career change.

Similarly, we are hosting a conference this October in Munich on career options for philosophers outside academia: What do you want to do with that?  Answers from philosophers outside the academy.  Along with Zach, Jeff Helzner, Andreas Edmüller, and Rebekka Reinhard, will be our keynote speakers.  In addition to their lectures, which will be filmed and made available in the MCMP iTunes library, each of our distinguished speakers will lead a workshop to allow for one-on-one discussions that draw on their experiences of starting businesses, joining a fast growing startup, managing a public profile and mass media engagements, and leading interdisciplinary research and development teams within a corporate environment.

Together, our goal is to develop resources and best-practices for offering concrete and useful information to students and peers who may be interested in career options outside of academia.

## Eighth Workshop on Principles and Methods of Statistical Inference with Interval Probability

August 4, 2015

### Munich, September 1 – 6th, 2015

http://www.statistik.lmu.de/institut/ag/agmg/research/wpmsiip_2015/index.html

The Working Group Methodological Foundations of Statistics and their Applications at the Department of Statistics, and the Munich Center for Mathematical Philosophy at Ludwig-Maximilians University Munich (LMU), is proud to host a workshop on principles and methods of statistical inference with interval probability for the third time. This workshop is a follow-up to earlier WPMSIIPs held in Durham (2008, 2010), Ljubljana (2011), Lugano (2013), Ghent (2014) and Munich (2009,2012).

Confirmed Participants

Everyone, including PhD students, is welcome to participate and /or to present their views on one or more of the covered topics, on one or more days! We welcome any stimulating contribution, ranging from a presentation to a short informal statement. If you want to join the workshop, please contact Julia Plaß and the organizers of the specific day(s) that you would like to contribute to.

## The Role of Explanatory Considerations in Updating

June 10, 2015

Heads up for any C&I readers interested in probabilistic models and how they relate to the psychology of updating, check out the following two articles coauthored with Igor Douven.  Both were published in the last month, and both are freely available at the moment.

I’m especially proud of this joint work, which defends explanationist (and probabilist) models of human learning over and above prevailing Bayesian models.  For more detail, abstracts are below the fold…

## (Im)Possible Conference in Turin

June 2, 2015

THINKING THE (IM)POSSIBLE

Northwest Italy Philosophy Consortium (FINO)
Italian Society for Analytic Philosophy (SIFA)

June 29-30, 2015
Center for Logic, Language, and Cognition
University of Turin
Lecture Hall (ground floor)
via Verdi 10, Turin

With the generous support of: COMPAGNIA DI SANPAOLO

MONDAY, JUNE 29

9.00 – Greetings: Gianmaria AJANI (Rector, Università di Torino), Massimo FERRARI (Director of the Department of Philosophy and Education, Università di Torino), Alberto VOLTOLINI (Coordinator of the FINO PhD Programme, Università di Torino).

9.45 – Opening Lecture
Mark SAINSBURY (University of Texas at Austin)
Intentionality, intensionality, and nonexistence: An outline

11.15 – Coffee break

11.45
Daniel DOHRN (Humboldt-Universität Berlin)
The case for imagination as a guide to possibility

12.30
Daniele SGARAVATTI (Università di Roma III)
Thinking about something: On a transcendental argument by E.J. Lowe

13.15 – Lunch break

15.00
Samuele CHILOVI (Universitat de Barcelona)
Maurice dispelled

15.45
Raphaël MILLIÈRE (École Normale Supérieure Paris)
Thinking the unthinkable: Berkeley’s challenge and pragmatic contradiction

16.30 – Coffee break

17.00
Thibaut GIRAUD (Institut Jean-Nicod Paris)
Logically impossible objects in classical logic

17.45
Alexander DINGES (Humboldt-Universität Berlin)
Innocent implicatures

TUESDAY, JUNE 30

9.45 – (Im)Possible Lecture
Graham PRIEST (University of Melbourne, University of St. Andrews)
Thinking the impossible

11.15 – Coffee break

11.45
Filippo CASATI (University of St. Andrews)
Nobject, one can even think of something that is not an object

12.30
Agnese PISONI (Università di Genova)
Thinking on the (im)possibility of time without change

13.15 – Lunch break

15.00
Impossible worlds and the incredulous stare

15.45
Cristina NENCHA (Università di Torino)
Was David Lewis an anti-essentialist?

16.30 – Coffee break

17.00 – Closing Lecture
Timothy WILLIAMSON (University of Oxford)
Counterpossible conditionals

## Why Bayesians Should (Sometimes) Attend to Differences Among Noninformative Stopping Rules

June 1, 2015

(Cross-posted from gandenberger.org)

### The Standard Bayesian-Frequentist Dialectic About Stopping Rules

Bayesians generally reject the frequentist view that inference and decision procedures should be sensitive to differences among “stopping rules”—that is, the (possibly implicit) processes by which experimenters decide when to stop collecting the data that will be fed into those procedures—outside of unusual cases in which the stopping rule is “informative” in a technical sense.

Frequentists often argue for their position by claiming that ignoring differences among noninformative stopping rules would allow experimenters to produce systematically misleading results. For instance, Mayo and Kruse (2001) consider the case of a subject who claims to be able to predict draws from a deck of ESP cards. On a frequentist approach, if a 5% significance level is used and the data are treated as if the sample size had been fixed in advance, then the probability of rejecting the “null hypothesis” that the subject has no extrasensory abilities within the first 1000 observations if that hypothesis is true is 53%, and the probability of rejecting it within some finite number of observations is one. Accordingly, Mayo and Kruse claim that whether the experimenter had planned to stop after 1000 trials all along or had planned to stop as soon as a statistically significant outcome had occurred must be reported and must be taken into account in inference and decision.

Bayesians have responded to this argument by showing out that their approach does not allow such “reasoning to a foregone conclusion” as long as the prior probability distributions that are used are countably additive (Kadane et al. 1996). In fact, the probability that a given experiment will produce a result that would lead a Bayesian agent to increase his or her odds in a particular hypothesis $H_a$ against a different hypothesis $H_0$ by a factor of $k$ is at most $1/k$ when $H_0$ is true, regardless of the experiment’s stopping rule.

Mayo and Kruse claim that this response misses the point of their objection, which does not require that the probability of being misled can be arbitrarily high, but only that it can be increased if stopping rules are ignored. Even on a fully Bayesian approach, disingenuous experimenters can tilt the odds in favor of their preferred hypothesis through their choice of stopping rule.

Bayesians often respond to this objection by saying that the probability that an experiment will produce a misleading result is an issue of experimental design only and is thus irrelevant to questions about inference or decision in light of the data.

### A New Objection to the Standard Bayesian Position

There seems to be a good response to this Bayesian claim that I have not yet encountered: issues of inference or decision cannot be separated from issues of experimental design when choices regarding the former may influence choices regarding the latter and the interests of those making the two kinds of choices are not aligned. For instance, consider the position of a government regulatory agency such as the FDA. The FDA has reason to adopt more or less explicit and consistent inference or decision rules regarding, for instance, when to approve a drug. If the FDA foresees that a certain policy would lead pharmaceutical companies to choose stopping rules that the FDA regards as undesirable in order to tilt the odds of getting desired decisions in their favor, then that fact is a reason for them not to adopt that policy. In this kind of case, issues of inference or decision and issues of experimental design are conceptually distinct but decision-theoretically entangled and thus cannot be treated separately.

Consider a simplified case in which a scientist can perform either a test with a fixed sample size of $n$ or a test that will continue until either the likelihood ratio of $H_a$ against $H_0$ exceeds some number $l$ or some maximum sample size $m>n$ is reached. A regulator has to decide what likelihood ratio $l_f$ would suffice for rejecting $H_0$ if the fixed-sample experiment is performed and what likelihood ratio $l_t$ would suffice if the target-likelihood-ratio procedure were performed. If there are no concerns about the regulator’s choice influencing the experimental design, then the regulator should set $l_f=l_t$, in accordance with the fact that the difference between the noninformative stopping rules in question does not affect the evidential import of the data according to the Likelihood Principle and does not affect the posterior probabilities under Bayesian conditioning. However, if the scientist can take the regulator’s choices for $l_f$ and $l_t$ into account in designing his experiment and the regulator prefers to reject $H_0$ only if it is false while the scientist prefers for it to be rejected no matter what, then under typical circumstances the regulator maximizes her expected utility by setting $l_t>l_f$ to avoid incentivizing undesirable behavior by the scientist. (A demonstration of this result is available upon request.) Thus, under these circumstances Bayesian principles entail that the regulator should act in accordance with the frequentist idea that differences among noninformative stopping rules are relevant to inference or decision.

This result is not an idle curiosity: government regulators, scientific journal editors, science journalists, scientific societies, evidence-based practitioners, and even the general public can greatly affect decisions about experimental design through their choices of inferential and decision-making practices. It is no objection to Bayesianism per se. Bayesian arguments for the irrelevance of stopping rules to inference and decision all assume that issues of experimental design can be treated separately from issues of inference and decision, and that assumption breaks down in the kinds of cases in question. In fact, the result is actually useful for defending basic Bayesian principles because it shows that those principles recover frequentist intuitions about stopping rules in precisely the kinds of cases in which those intuitions are most plausible. On the other hand, the result indicates that proposals for more widespread use of Bayesian methods in scientific practice would need to be implemented with care to avoid adopting inference and decision rules that would encourage scientists to “game the system” by using undesirable experimental designs.

April 29, 2015

## TARK 2015

### June 4-6, 2015 Carnegie Mellon University

Mission and Scope.

The mission of the TARK conferences is to bring together researchers from a wide variety of fields, including Artificial Intelligence, Cryptography, Distributed Computing, Economics and Game Theory, Linguistics, Philosophy, and Psychology, in order to further our understanding of interdisciplinary issues involving reasoning about rationality and knowledge.

Topics of interest.

Topics of interest include, but are not limited to, semantic models for knowledge, belief, awareness and uncertainty, bounded rationality and resource-bounded reasoning, commonsense epistemic reasoning, epistemic logic, epistemic game theory, knowledge and action, applications of reasoning about knowledge and other mental states, belief revision, and foundations of multi-agent systems.

Invited Speakers.

Robin Clark, University of Pennsylvania
Simon Huttegger, University of California, Irvine
Sarit Kraus, Bar-Ilan University
Marciano Siniscalchi, Northwestern University

TARK 2015 will also feature a Tutorial on Causal Inference by:

Peter Spirtes, Carnegie Mellon University
Kun Zhang , Max Planck Institute for Intelligent Systems

Contributed Talks and Poster Presentations.

The list of accepted papers is now posted at the conference web page:

www.imsc.res.in/tark/tark15.html

TARK Hotel Registration Deadline is May 3

The deadline for hotel registration at TARK rates is rapidly approaching. The deadline at the Hilton Garden Inn University place is May 3, EST. There are not so many hotels close to the venue, and we cannot guarantee that you will find a room after May 3, so it is strongly recommended to take a few minutes to register for TARK and book your hotel room now.

Please specify that you are booking your hotel room for the TARK conference, as the local organizers are financially responsible for rooms not booked by TARK attendees.

Hotel booking and TARK registration can all be done conveniently at:

www.hss.cmu.edu/philosophy/cfe_TARK2015.php

We look forward to seeing you in Pittsburgh!

Submitted by Kevin Kelly

## Postdoc position: evaluating evidence in medicine

February 16, 2015

A new research project Evaluating evidence in medicine, has secured funding from the UK Arts and Humanities Research Council.

The project will run for 3 years from 1st June 2015.

We are advertising for a postdoctoral research associate to work on the project. Find out more by searching for job reference HUM0604 here.