Imagine that you had to give a probability density to each probability estimate you could make of Obama winning in 2012 being the correct one. You'd end up with something looking like a bell curve over probabilities
Bell curves prefer to live on unbounded intervals! It would be less jarring, (and less convenient for you?), if he ended up with something looking like a uniform distribution over probabilities.
It's equally convenient, since the mean doesn't care about the shape. I don't think it's particularly jarring - just imagine it going to 0 at the edges.
The reason you'll probably end up with something like a bell curve is a practical one - the central limit theorem. For complicated problems, you very often get what looks something like a bell curve. Hardly watertight, but I'd bet decent amounts of money that it is true in this case, so why not use it to add a little color to the description?
http://vimeo.com/22099396
What do people think of this, from a Bayesian perspective?
It is a talk given to the Oxford Transhumanists. Their previous speaker was Eliezer Yudkowsky. Audio version and past talks here: http://groupspaces.com/oxfordtranshumanists/pages/past-talks