Speaker: Konstantin Genin (Research Group: Epistemology and Ethics of Machine Learning in the Cluster of Excellence – Machine Learning for Science at the University of Tübingen)
Date and Time: Thursday, December 10th 2020, 16:30-18:00, Amsterdam time.
Venue: online.
Title: Simplicity and Scientific Progress
Abstract.
A major goal of twentieth-century philosophy of science was to show how science could make progress toward the truth even if, at any moment, our best theories are false. To that end, Popper and others tried to develop a theory of truthlikeness, hoping to prove that theories get closer to the truth over time. That program encountered several notable setbacks. I propose the following: a method for answering an empirical question is progressive if the chance of outputting the true answer is strictly increasing with sample size. Surprisingly, many standard statistical methods are not even approximately progressive. What’s worse, many problems do not admit strictly progressive solutions. However, I prove that it is often possible to approximate progressiveness arbitrarily well. Furthermore, every approximately progressive method must obey a version of Ockham’s razor. So it turns out that addressing the problem of progress uncovers a solution to another perennial problem: how can we give a non-circular argument for preferring simple theories when the truth may well be complex?
See here for the recording of the talk.