Timetable

Evening Lecture: Updating on the credence of others (21.04.2015)

18:30 – 20:00

24.52, level 1, room 81

Abstract: How should you update your credences upon learning the credences of others? Because of the complexity of Bayesian conditionalization in this context, there has been considerable interest in developing simple heuristics, the most popular being linear averaging. However, linear averaging has a number of drawbacks: it does not commute with itself, or with conditionalization; it does not preserve independence; and it is not always compatible with conditionalization. In addition, we argue that a further drawback with linear averaging is that it lacks a property we call ‘synergy’. We propose a new rule that is just is simple as linear averaging but doesn’t have these drawbacks.

Evening Lecture: The causal bases of propensities (22.04.2015)

18:30 – 20:00

23.21, level 2, room 22

Abstract: According to propensity interpretations, propensities are causal dispositions that come in degrees. Insufficient attention has been paid to the question of what kind of causal basis is necessary to give rise to a propensity. I will propose an answer to this question that makes use of causal Bayes nets. The proposal will have several other virtues. It will provide an answer to Humphreys’ paradox, which has plagued propensity interpretations of probability. In addition, my proposal will explain why beliefs about propensities obey Lewis’s Principal Principle.

Keynote Lecture: Conditioning, intervening, and decision (24.04.2015)

15:30 – 17:00

23.21, ground floor, room 44B

Abstract: This paper develops a proposal of Meek and Glymour (Brit J Phil Sci 1994) to interpret causal decision theory using interventions in graphical causal models. I use their framework to defend causal decision theory from a number of objections, and extend the analysis by showing how graphical causal models might be used to address decision problems that arise in “exotic” situations, such as those involving crystal balls or time travelers. More generally, the use of graphical causal models can help us to specify the causal structure of a decision problem, to specify the question that is to be answered, and to specify the information that we have available.