I try to bring more epistemology wherever I am. Each term, I invite epistemologists to give a talk and discuss their current research. My goal is to invite not only well-established researchers but also early-career philosophers to present their work. Everybody, including students of any level, is most welcome to join us.



Apr. 2018



Feb. 2018



Dec. 2017



Oct. 2017

Patrick Johannes Klug (University of Vienna)

A Dual Stability Theory of Justification

Following Alston, an agent’s belief that p is justified iff that belief is based on adequate grounds. This characterization of justification implies, as Alston illustrated, internal as well as external requirements. On the one hand, for an agent’s belief to be based on grounds has to be interpreted internally. While on the other hand, adequacy is an external quality of those grounds. The DST builds upon those internal and external requirements and defines them as internal and external stability.

The concept of stability used in DST is the one defined by Leitgeb's Stability of Belief. Leitgeb uses the concept of stability define an agent’s rational belief that p as a stably high (rational) degree of belief in p. This framework will be used to formalize the concepts of internal rational belief as well as external rational belief. The differences between those two concepts are (1) the interpretation of the probability function that is used to characterize the rational degree of belief, and (2) the threshold of stability that is required by this probability function.

The resulting concept of internal rationality will be used to reformulate Alston’s internal requirement that a justified belief has to be based on grounds. Alston’s external requirement of accuracy will be described by external rationality. In this sense, following DST, an agent’s belief that p is justified iff it is internally and externally rational to believe that p. That is to say, if the agent’s internal as well as the external rational degree of belief in p is stable.

The Art of Learning

Confirmational holism is at odds with Jeffrey conditioning — the orthodox Bayesian policy for accommodating uncertain learning experiences. Two of the great insights of holist epistemology are that (i) the effects of experience ought to be mediated by one’s background beliefs, and (ii) the support provided by one’s learning experience can and often is undercut by subsequent learning. Jeffrey conditioning fails to vindicate either of these insights. My aim is to describe and defend a new updating policy that does better. In addition to showing that this new policy is more holism-friendly than Jeffrey conditioning, I will also show that it has an accuracy-centered justification.

Prague-Bristol Workshop in Contemporary Epistemology


10:30 am 11:30 am: Ondrej Majer (Prague): Substructural epistemic logics

11:30 am – 11:40 am: break

11:40 am – 12:40 pm:  Johannes Stern (Bristol)From Because via Truth to Grounds

12:40 pm – 2:00 pm: lunch

2:00 pm – 3:00 pm: Timothy Childers (Prague)Probabilities over Dunn-Belnap logics

3:00 pm – 3:10 pm: break

3:10 pm – 4:10 pm: Catrin Campbell-Moore (Bristol): Probabilities, Non-classical logic and
                                                                            the Liar Paradox                                         
4:10 pm: drinks and dinner

Can Probabilistic Knowledge Help Knowledge-First Epistemology? 

The knowledge-first approach to epistemology paints a unified picture according to which knowledge is the norm of belief, assertion, and action. Placing knowledge in this position seems to set the bar very high -- perhaps too high -- because knowledge requires certainty, and agents are often faced with uncertainty. Notably, standard decision theory defines rational action partially as a product of probabilistic beliefs. The hypothesis of probabilistic knowledge, as defended by Sarah Moss, would provide a neat solution to this problem, but probabilistic knowledge faces problems of its own: most critically, a plausible factivity condition for probabilistic knowledge has yet to be specified. I provide a solution to these problems by translating probabilistic knowledge into a modified Kripke framework, which allows me to fill in the details of probabilistic knowledge in a sensible way. Part 1 of the talk explains the knowledge-first approach to epistemology. Part 2 presents the relevant aspects of probabilistic knowledge as defended by Moss, shows how it makes knowledge norms more compelling, and highlights some of the first hurdles it must clear. Part 3 shows the basic change needed to model probabilistic knowledge in a Kripke framework, and argues that probabilistic knowledge makes good sense when understood in this way. 

Date: Monday 23th of April

Room: zasedací místnost

Address: Institute of Philosophy, Czech Academy of Sciences, Jilská 1, Prague 1, 110 00

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