This paper is about how an agent should rationally update her probabilistic beliefs when her conceptual space (modelled as an algebra of propositions) grows. This is not like typical cases of learning, which are cases in which an agent comes to revise her beliefs for propositions about which she was already aware. We investigate whether the learning rules for the typical cases of learning can be extended to the case of conceptual growth.

Here HMI CI Katie Steele argues that on a certain way of modelling an agent's preferences and understanding her "time preferences", exponential time discounting is uniquely rational. However, if "time preferences" are understood differently, then exponential time discounting is not uniquely rational. This helps in understanding why the prescription of exponential time discounting has many defenders but also many detractors.

This paper considers strategies for making decisions in the face of severe uncertainty, when one's beliefs are best represented by a set of probability functions over the possible states of the world (as opposed to a single precise probability function). The question is whether one can employ a decision strategy that does not have the disadvantage of making one vulnerable to sure loss in sequential-decision scenarios.