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This is a chapter-by-chapter summary of Value-Focusing Thinking by Ralph Keeney. The hope of this summary is to present most of the value of reading the book in a tiny fraction of the space. Reading the original chapters will provide additional elaboration, examples, and secondary concepts, but unlike the textbooks I've reviewed before only those interested in learning more should need to read the full chapters.
I'll state my basic impression of the whole book up front: it is a very useful book for the 'soft half' of decision analysis, by which I mean framing problems, understanding objectives, and interacting with humans. For a more general and individual-focused introduction to decision analysis, I recommend Smart Choices (which Keeney was a coauthor on); VFT appears primarily targeted at facilitators and contains much focused material not in Smart Choices. I will not suggest targeted reading for particular readers, as Keeney does that well.
The first two paragraphs of the preface seem worth quoting in full:
This is a chapter-by-chapter review of Thinking and Deciding by Jonathan Baron (UPenn, twitter). It won't be a detailed summary like badger's excellent summary of Epistemology and the Psychology of Human Judgment, in part because this is a 600-page textbook and so a full summary would be far longer that I want to write here. I'll try to provide enough details that people can seek out the chapters that they find interesting, but this is by no means a replacement for reading the chapters that you find interesting. Every chapter is discussed below, with a brief "what should I read?" section if you know what you're interested in.
We already have a thread for textbook recommendations, but this book is central enough to Less Wrong's mission that it seems like it's worth an in-depth review. I'll state my basic impression of the whole book up front: I expect most readers of LW would gain quite a bit from reading the book, especially newer members, as it seems like a more focused and balanced introduction to the subject of rationality than the Sequences.
Baron splits the book into three sections: Thinking in General, Probability and Belief, and Decisions and Plans.
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?
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.
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.
Value of Information (VoI) is a concept from decision analysis: how much answering a question allows a decision-maker to improve its decision. Like opportunity cost, it's easy to define but often hard to internalize; and so instead of belaboring the definition let's look at some examples.