Admission is free. Everybody is cordially invited!

ABSTRACTS When does evidence suffice for conviction? Martin Smith (Glasgow) There is something puzzling about statistical evidence.  One place this manifests is in the law, where courts are reluctant to use evidence of this kind, in spite of the fact that it is quite capable of meeting the standards of proof enshrined in legal doctrine.  After surveying some proposed solutions to this problem, I shall outline a somewhat different approach ‚Äì one that makes use of a notion of normalcy that is distinct from the idea of statistical frequency.  The problem is not, however, merely a legal one.  Our unwillingness to base beliefs on statistical evidence is by no means limited to the courtroom, and is at odds with almost every general principle that epistemologists have ever proposed as to how we ought to manage our beliefs. Dogmas, Dominance, and Disintegrations Paul Pedersen (Berlin) Foundations for boundedly rational learning Simon Huttegger (UC Irvine) The core idea of rational Bayesian learning is that learning from experience should be consistent with one's inductive assumptions. I will use this idea to develop foundations for so-called bounded rationality learning rules, which are based on much less informational inputs than Bayesian conditioning. This results in foundations for bounded rationality learning very similar to those of a more standard Bayesian type.