If you've recently joined the Less Wrong community, please leave a comment here and introduce yourself. We'd love to know who you are, what you're doing, what you value, how you came to identify as a rationalist or how you found us. You can skip right to that if you like; the rest of this post consists of a few things you might find helpful. More can be found at the FAQ.

Summary: If you've been wondering why people keep going on about decision theory on Less Wrong, I wrote you this post as an answer. I explain what decision theories are, show how Causal Decision Theory works and where it seems to give the wrong answers, introduce (very briefly) some candidates for a more advanced decision theory, and touch on the (possible) connection between decision theory and ethics.

This is the first part in a mini-sequence presenting content from Keith E. Stanovich's excellent book What Intelligence Tests Miss: The psychology of rational thought. It will culminate in a review of the book itself.

People who care a lot about rationality may frequently be asked why they do so. There are various answers, but I think that many of ones discussed here won't be very persuasive to people who don't already have an interest in the issue. But in real life, most people don't try to stay healthy because of various far-mode arguments for the virtue of health: instead, they try to stay healthy in order to avoid various forms of illness. In the same spirit, I present you with a list of real-world events that have been caused by failures of rationality, so that you might better persuade others of this being important.

What happens if you, or the people around you, are not rational? Well, in order from least serious to worst, you may...

Have a worse quality of living. Status Quo bias is a general human tendency to prefer the default state, regardless of whether the default is actually good or not. In the 1980's, Pacific Gas and Electric conducted a survey of their customers. Because the company was serving a lot of people in a variety of regions, some of their customers suffered from more outages than others. Pacific Gas asked customers with unreliable service whether they'd be willing to pay extra for more reliable service, and customers with reliable service whether they'd be willing to accept a less reliable service in exchange for a discount. The customers were presented with increases and decreases of various percentages, and asked which ones they'd be willing to accept. The percentages were same for both groups, only with the other having increases instead of decreases. Even though both groups had the same income, customers of both groups overwhelmingly wanted to stay with their status quo. Yet the service difference between the groups was large: the unreliable service group suffered 15 outages per year of 4 hours' average duration and the reliable service group suffered 3 outages per year of 2 hours' average duration! (Though note caveats.)

A study by Philips Electronics found that one half of their products had nothing wrong in them, but the consumers couldn't figure out how to use the devices. This can be partially explained by egocentric bias on behalf of the engineers. Cognitive scientist Chip Heath notes that he has "a DVD remote control with 52 buttons on it, and every one of them is there because some engineer along the line knew how to use that button and believed I would want to use it, too. People who design products are experts... and they can't imagine what it's like to be as ignorant as the rest of us."

Suffer financial harm. John Allen Paulos is a professor of mathematics at Temple University. Yet he fell prey to serious irrationality which began when he purchased WorldCom stock at $47 per share in early 2000. As bad news about the industry began mounting, WorldCom's stock price started falling - and as it did so, Paulos kept buying, regardless of accumulating evidence that he should be selling. Later on, he admitted that his "purchases were not completely rational" and that "I bought shares even though I knew better". He was still buying - partially on borrowed money - when the stock price was $5. When it momentarily rose to $7, he finally decided to sell. Unfortunately, he didn't get off from work until the market closed, and on the next market day the stock had lost a third of its value. Paulos finally sold everything, at a huge loss.

This article is an attempt to summarize basic material, and thus probably won't have anything new for the hard core posting crowd. If you're new and this article got you curious, we recommend the Sequences.

People who know a little bit of statistics - enough to use statistical techniques, not enough to understand why or how they work - often end up horribly misusing them. Statistical tests are complicated mathematical techniques, and to work, they tend to make numerous assumptions. The problem is that if those assumptions are not valid, most statistical tests do not cleanly fail and produce obviously false results. Neither do they require you to carry out impossible mathematical operations, like dividing by zero. Instead, they simply produce results that do not tell you what you think they tell you. As a formal system, pure math exists only inside our heads. We can try to apply it to the real world, but if we are misapplying it, nothing in the system itself will tell us that we're making a mistake.

Examples of misapplied statistics have been discussed here before. Cyan discussed a "test" that could only produce one outcome. PhilGoetz critiqued a statistical method which implicitly assumed that taking a healthy dose of vitamins had a comparable effect as taking a toxic dose.

Even a very simple statistical technique, like taking the correlation between two variables, might be misleading if you forget about the assumptions it's making. When someone says "correlation", they are most commonly talking about Pearson's correlation coefficient, which seeks to gauge whether there's a linear relationship between two variables. In other words, if X increases, does Y also tend to increase. (Or decrease.) However, like with vitamin dosages and their effects on health, two variables might have a non-linear relationship. Increasing X might increase Y up to a certain point, after which increasing X would decrease Y. Simply calculating Pearson's correlation on two such variables might cause someone to get a low correlation, and therefore conclude that there's no relationship or there's only a weak relationship between the two. (See also Anscombe's quartet.)

The lesson here, then, is that not understanding how your analytical tools work will get you incorrect results when you try to analyze something. A person who doesn't stop to consider the assumptions of the techniques she's using is, in effect, thinking that her techniques are magical. No matter how she might use them, they will always produce the right results. Of course, assuming that makes about as much sense as assuming that your hammer is magical and can be used to repair anything. Even if you had a broken window, you could fix that by hitting it with your magic hammer. But I'm not only talking about statistics here, for the same principle can be applied in a more general manner.