Online research in philosophy

While philosophers have studied probability and induction, statistics has not received the kind of philosophical attention mathematics and physics have. Despite increasing use of statistics in science, statistical advances have been little noted in the philosophy of science literature. This paper shows the relevance of statistics to both theoretical and applied problems of philosophy. It begins by discussing the relevance of statistics to the problem of induction and then discusses the reasoning that leads to causal generalizations and how statistics elucidates the structure of science as it is actually practiced. In addition to being relevant for building an adequate theory of scientific inference, it is argued that statistics provides a link between philosophy, science and public policy.

I argue that the Doomsday argument fails because it fails to take into account the lesson of the Sleeping Beauty puzzle.

The Doomsday Argument says we should increase our subjective probability that Doomsday will occur once we take into account how many humans have lived before us. One objection to this conclusion is that we should accept the Self-Indication Assumption (SIA): Given the fact that you exist, you should (other things equal) favor hypotheses according to which many observers exist over hypotheses on which few observers exist. Nick Bostrom argues that we should not accept the SIA, because it can be used without knowledge of birth rank. Bradley Monton tries to construct a Doomsday Argument without knowledge of birth rank. I argue that Monton fails. The argument he constructs has implicit knowledge of birth rank and it is this knowledge that does the work. Furthermore, I argue that provided we dont have certain specific information about the future, the Doomsday Argument requires knowledge of birth rank.

A recent paper by Korb and Oliver in this journal attempts to refute the Carter-Leslie Doomsday argument. I organize their remarks into five objections and show that they all fail. Further efforts are thus called upon to find out what, if anything, is wrong with Carter and Leslie's disturbing reasoning. While ultimately unsuccessful, Korb and Oliver's objections do however in some instances force us to become clearer about what the Doomsday argument does and doesn't imply.

In a recent paper in this journal, Ken Olum attempts to refute the Doomsday argument by appealing to the self-indication assumption (SIA), the idea that your very existence gives you reason to think that there are many observers. In contrast to earlier refutation attempts that use this strategy, Olum confronts and try to counter some of the objections that have been made against SIA. We argue that his defense of SIA is unsuccessful. This does not, however, mean that one has to accept the Doomsday argument (or the other counterintuitive results that flow from related thought experiments). A developed theory of observation selection effects shows why the Doomsday argument is inconclusive and how one can consistently reject both it and SIA.

Exploratory statistics represents the transformation of a realist theory of statistics held by early nineteenth-century astronomers into an empiricist theory of statistics held by biometricians at the turn of the twentieth century. This paper discusses four key ideas in empiricist thought that influenced the form exploratory statistics took: (1) Baconianism, (2) associationism, (3) the search for cognitive calculi, and (4) phenomenalism. Some limitations of and alternatives to exploratory statistics as a hypothesis-generating methodology are discussed.

George Sowers tries to refute the Doomsday argument on grounds that true random sampling requires all possible samples to be equally probable the time when the sample is taken. Yet the Doomsday argument does not rely on true random sampling. It presupposes random sampling only in a metaphorical sense. After arguing that Sowers’ critique fails, I outline my own view on the matter, which is that the Doomsday argument is inconclusive and that by developing a theory of observation selection effects one can show why that is so.

The Carter-Leslie Doomsday argument, as standardly presented, relies on the assumption that you have knowledge of your approximate birth rank. I demonstrate that the Doomsday argument can still be given in a situation where you have no knowledge of your birth rank. This allows one to reply to Bostrom's defense of the Doomsday argument against the refutation based on the idea that your existence makes it more likely that many observers exist.

claim that his thought experiment shows that a currently living person is not a random sample is refuted. His thought experiment is reduced to a probability model, and is shown to be identical to one previously developed by Dieks. The status of the Doomsday Argument is left unresolved, since Dieks's refutation attempt is disputed in the literature.

This paper attempts three tasks in relation to Carter and Leslie's Doomsday Argument. First, it criticises Timothy Chambers' 'Ussherian Corollary', a striking but unsuccessful objection to standard Doomsday arguments. Second, it reformulates the Ussherian Corollary as an objection to Bradley Monton's variant Doomsday and Nick Bostrom's Simulation Argument. Finally, it tries to diagnose the epistemic/metaphysical problems facing Doomsday-related arguments.1.