What do you want to do with that? Answers from philosophers outside the academy

September 13, 2015

What do you want to do with that?
Call for Participation
October 23-24, 2015
Munich Center for Mathematical Philosophy, LMU Munich

Philosophers are everywhere—-in private industry, nonprofit organizations, government, the arts, and even universities. It should come as no surprise to find philosophers thriving throughout all corners of society. What is surprising, and what this conference is conceived to respond to, is the degree to which philosophers inside the academy remain isolated from those outside of it.

This two-day conference brings together a distinguished group of philosophers who know of life inside and outside the academy, and who will share their insights and experiences navigating the transitions from one realm to another; which insights and experiences translate well, and which do not; and what habits and best practices the broader community of philosophers can and should adopt to create opportunities for philosophers at all stages of their careers, but especially for philosophy undergraduate and graduate students.

Keynote Speakers:

  • Jeffrey HelznerEliciting Expert Knowledge When Time is Scarce, the Stakes are High, Scalability is a Concern, and the Distributions are Skewed
  • Andreas EdmüllerThe World of Business Needs Philosophy!
  • Rebekka Reinhard ‘What are You Living for?’ On How to be a Free-Lance Philosopher
  • Zachary Ernst Barriers to Exit: Real and Imaginary

Each lecture will be filmed and made available on the MCMP iTunes video archive. In addition, each speaker will lead a workshop to allow for one-on-one discussions that draw on their experiences in starting a consulting business, joining a fast growing startup, managing a public profile and mass media engagements, and leading interdisciplinary research and development teams within a corporate environment.

Stephan Hartmann
Gregory Wheeler

Bridges 2 – Workshop at Rutgers

September 7, 2015

The Rutgers Philosophy Department and the Rutgers Center for Cognitive Science will be hosting a workshop (on Logic, Language, Epistemology, and Philosophy of Science) September 18-20. The workshop will bring together scholars from the NYC area, Amsterdam (the Institute for Logic, Language, and Computation), and Munich (the Munich Center for Mathematical Philosophy). The schedule for the workshop is posted on the Bridges 2 webpage:


The event is open to the public.


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


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…

Read the rest of this entry »

(Im)Possible Conference in Turin

June 2, 2015


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

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

With the generous support of: COMPAGNIA DI SANPAOLO



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

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

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

13.15 – Lunch break

Samuele CHILOVI (Universitat de Barcelona)
Maurice dispelled

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

16.30 – Coffee break

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

Alexander DINGES (Humboldt-Universität Berlin)
Innocent implicatures



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

11.15 – Coffee break

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

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

13.15 – Lunch break

Martin VACEK (Slovenská Akadémia Vied)
Impossible worlds and the incredulous stare

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.


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