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:

1. Uncertainty: the basics of treating uncertainties as probabilities and doing Bayesian math.
2. 5 Axioms of Decision Making: the five steps / assumptions that form the foundation of careful decision-making.
3. 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.
4. Measures, Risk, Death, and War: how to deal with many similar prospects (utilities), risks of death, and adversaries.
5. 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?

This is the fourth post of a sequence on decision analysis, preceded by Compressing Reality to Math. It touches on a wide variety of topics which didn't seem to work well as posts of their own, either because they were too short or too long.

This is part of a sequence on decision analysis and follows 5 Axioms of Decision-Making, which explains how to turn a well-formed problem into a solution. Here we discuss turning reality into a well-formed problem. There are three basic actions I'd like to introduce, and then work through some examples.

This is part of a sequence on decision analysis; the first post is a primer on Uncertainty.

Decision analysis has two main parts: abstracting a real situation to math, and then cranking through the math to get an answer. We started by talking a bit about how probabilities work, and I'll finish up the inner math in this post. We're working from the inside out because it's easier to understand the shell once you understand the kernel. I'll provide an example of prospects and deals to demonstrate the math, but first we should talk about axioms. In order to be comfortable with using this method, there are five axioms¹ you have to agree with, and if you agree with those axioms, then this method flows naturally. They are: Probability, Order, Equivalence, Substitution, and Choice.

This is part of a sequence on decision analysis.

Decision-making under certainty is pretty boring. You know exactly what each choice will do, and so you order the outcomes based on your preferences, and pick the action that leads to the best outcome.

Human decision-making, though, is made in the presence of uncertainty. Decision analysis - careful decision making - is all about coping with the existence of uncertainty.

Some terminology: a distinction is something uncertain; an event is each of the possible outcomes of that distinction; a prospect is an event that you have a personal stake in, and a deal is a distinction over prospects. This post will focus on distinctions and events. If you're comfortable with probability just jump to the four bolded questions and make sure you get the answers right. Deals are the interesting part, but require this background.

Continuing my interest in tracking real-world predictions, I notice that the recent acquittal of Knox & Sollecito offers an interesting opportunity - specifically, many LessWrongers gave probabilities for guilt back in 2009 in komponisto’s 2 articles:

• “You Be the Jury: Survey on a Current Event”
• “The Amanda Knox Test: How an Hour on the Internet Beats a Year in the Courtroom”

Both were interesting exercises, and it’s time to do a followup. Specifically, there are at least 3 new pieces of evidence to consider:

1. the failure of any damning or especially relevant evidence to surface in the ~2 years since (see also: the hope function)
2. the independent experts’ report on the DNA evidence
3. the freeing of Knox & Sollecito, and continued imprisonment of Rudy Guede (with reduced sentence)

Point 2 particularly struck me (the press attributes much of the acquittal to the expert report, an acquittal I had not expected to succeed), but other people may find the other 2 points or unmentioned news more weighty.

Related to: Which Parts Are "Me"?, Making your explicit reasoning trustworthy, The 5-Second Level.

What's damaging about moralizing that we wish to avoid, what useful purpose does moralizing usually serve, and what allows to avoid the damage while retaining the usefulness? It engages psychological adaptations that promote conflict (by playing on social status), which are unpleasant to experience and can lead to undesirable consequences in the long run (such as feeling systematically uncomfortable interacting with a person, and so not being able to live or work or be friends with them). It serves the purpose of imprinting your values, which you feel to be right, on the people you interact with. Consequentialist elucidation of reasons for approving or disapproving of a given policy (virtue) is an effective persuasion technique if your values are actually right (for the people you try to confer them on), and it doesn't engage the same parts of your brain that make moralizing undesirable.

What happens here is transfer of responsibility for important tasks from the imperfect machinery that historically used to manage them (with systematic problems in any given context that humans but not evolution can notice), to explicit reasoning.