This paper has two goals.
The first goal is to say something about how one might combine different agents' imprecise probabilities
to generate an aggregate imprecise probability.
The second goal is to champion the very general theory of "belief …

There is currently much discussion about how decision making should proceed when an
agent's degrees of belief are imprecise; represented by a set of probability functions.
I show that decision rules recently discussed by
Sarah Moss, Susanna Rinard …

This project explores Imprecise probabilities as a model for rational belief.

It has been argued that imprecise probabilities are a natural and intuitive way of overcoming some of the issues with orthodox precise probabilities. Models of this type have a long pedigree, and interest in such models has been growing in recent …

This paper considers a puzzling conflict between
two positions that are each
compelling: (A) it is irrational for an agent to pay to avoid 'free' evidence
before making a decision, and (B) rational agents may have *imprecise*
beliefs and/or desires. …

If one models an agent's degrees of belief by a set of probabilities, how should that agent's choices be constrained?
In other words, what choice function should the agent use?
This paper summarises some suggestions, and outlines a collection of …

Imprecise probabilism, which holds that rational belief/credence is permissibly represented by a set of probability functions, apparently suffers from a problem known as *dilation*. We explore whether this problem can be avoided or mitigated by one …

There has been much recent interest in *imprecise probabilities*, models of belief that allow unsharp or fuzzy credence. There have also been some influential criticisms of this position. Here we argue, chiefly against Elga (2010), that subjective …

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