Background: lukeprog wrote this post about articles he wouldn't have the time to write, and the first one on the list was something I was confident about, and so I decided to write a post on it. (As a grad student in operations research, practical decision theory is what I spend most of my time thinking about.)

Amusingly enough, I had the most trouble working in his 'classic example.' Decision analysis tends to be hinged on Bayesian assumptions often referred to as "small world"- that is, your model is complete and unbiased (If you knew there was a bias in your model, you'd incorporate that into your model and it would be unbiased!). Choosing a career is more of a search problem, though- specifying what options you have is probably more difficult than picking from them. You can still use the VoI concept- but mostly for deciding when to stop accumulating new information. Before you've done your first research, you can't predict the results of your research very well, and so it's rather hard to put a number on how valuable looking into potential careers is.

There seems to be a lot of interest in abstract decision theory, but is there interest in more practical decision analysis? That's the sort of thing I suspect I could write a useful primer on, whereas I find it hard to care about, say, Sleeping Beauty.