This is the introduction (conclusion) to my decision analysis sequence. It covers (much more quickly and less completely) what you would expect to see in a semester-long course on decision making. The posts are:

Uncertainty: the basics of treating uncertainties as probabilities and doing Bayesian math.
5 Axioms of Decision Making: the five steps / assumptions that form the foundation of careful decision-making.
Compressing Reality to Math: how to take a sticky, complicated situation and condense it down to something a calculator can solve, without feeling like you've left something important out.
Measures, Risk, Death, and War: how to deal with many similar prospects (utilities), risks of death, and adversaries.
Value of Information: Four Examples: how to value information-gathering activity, like tests or waiting, and incorporate it into your decision-making process.

I'd like to welcome any comments about the sequence here. What parts did I do well? What parts need work? What parts would you like to see expanded (or removed)?

One of the difficulties in posting about a topic like this is that it's foundational: basic, but important to get right. The idea of an expected utility calculation is not new (although the approach I take here may be novel for many of you) and, like I say in the VoI post, there's often more benefit in applying the process to examples than repeatedly talking about the process. The case studies I have access to, though, are not ones I can publish online, and I don't think I can construct an example that would work as well as a real one. Do people have problems they would like me to analyze with this framework as examples?