I've noticed something very curious on Intrade markets for 2012 Republican Presidential Nominee - Ron Paul gets 3.5% of getting a nomination - a value that's clearly (and spare me EMH here) wishful thinking of Ron Paul supporters more than any genuine estimate.

And this brings me to a question - if prediction markets overestimate chance of winning of some rare case, how can I profit from that? Naively if I know true chance is 1%, I win $3.5 99% of the time, and lose $97.5 1% of the time, for expected payoff of $2.5. But my maximum loss is 39x higher than my expected profit, and I won't be getting any money out of it for three more years.

I'd need to bet significant amount to earn any money out of it, and that would require accepting 39x as high maximum loss. No reasonably prediction market would accept this kind of leverage without some collateral, nor could I get any reasonable loan for it at rates that would make this arbitrage profitable.

The only way I can think of would be convincing someone with plenty of money that I'm right, and have him provide me with collateral for some (probably very high) portion of the payoff. But if results depend on my ability to convince rich people, that's not prediction market! None of this is a problem for people trying to artificially pump estimates for Ron Paul - they'll just take the loss, and write it off as marketing expense.

None of these problems occur if some position is vastly overestimated, like 60% estimate if I know true value to be 40% - this would be a cheap bet - maximum loss of $40 for expected profit of $20, and people who want to pump it need to take about as much risk as people who want to bring it back to the true value, not a lot more.

I'm confused. Is there some nice way to arbitrage this, or is this an inherent weakness of prediction markets and we should only trust positions they pick as leaders, not chances of their long tail?

"Statistically significant results" mean that there's a 5% chance that results are wrong in addition to chance that the wrong thing was measures, chance that sample was biased, chance that measurement instruments were biased, chance that mistakes were made during analysis, chance that publication bias skewed results, chance that results were entirely made up and so on.

"Not statistically significant results" mean all those, except chance of randomly mistaken results even if everything was ran correct is not 5%, but something else, unknown, and dependent of strength of the effect measured (if the effect is weak, you can have study where chance of false negative is over 99%).

So results being statistically significant or not, is really not that useful.

For example, here's a survey of civic knowledge. Plus or minus 3% measurement error? Not this time, they just completely made up the results.

Take home exercise - what do you estimate Bayesian chance of published results being wrong to be?

There was some talk here about height taxes, but there's a better solution - redefine shortness as a treatable condition and use HGH to cure it. They even got FDA on board with that, at least for 1.2% shortest people.

Unsatisfactory sexual performance became a treatable condition with Viagra. Depression and hyperactivity became treatable conditions with SSRIs. Being ugly is already almost considered a treatable condition, at least one can get that impression from cosmetic surgery ads. Being overweight is universally considered an illness, even though we don't have too many effective treatment options (surgery is unpopular, and effective drugs like fen-phen and ECA are not officially prescribed any more). If we ever figure out how to increase IQ, you can be certain low IQ will be considered a treatable condition too. Almost everything undesirable gets redefined as an illness as soon as an effective way to fix it is developed.

I welcome these changes. Yes, redefining large parts of normal human variability as illness is a lie, but if that's what society needs to work around its taboos against human enhancement, so be it.

I just did some reading about "Sociosexual Orientation Inventory", a simple 7-item test designed to measure one's openness to sex without love and long term commitment.

Here are the questions. How long will it take you to spot the huge problem ahead...

1. With how many different partners have you had sex (sexual intercourse) within the last year.
2. How many different partners do you foresee yourself having sex with during the next five years? (Please give a specific, realistic estimate)
3. With how many different partners have you had sex on one and only one occasion?
4. How often do (did) you fantasize about having sex with someone other than your current (most recent) dating partner? (1 never ... 8 at least once a day)
5. "Sex without love is OK" (1 strongly disagree ... 9 stronly agree)
6. "I can imagine myself being comfortable and enjoying `casual' sex with different partners (1 strongly disagree ... 9 stronly agree)
7. "I would have to be closely attached to someone (both emotionally and psychologially) before I could feel comfortable and fully enjoy having sex with him or her" (1 strongly disagree ... 9 stronly agree)

Score is: 5 x item1 + 1 x item2 (capped at 30) + 5 x item3 + 4 x item4 + 2 x (mean of item5, item6, and reversed item7)

Do you see the problem already?

Quite predictably, researchers report that men have much higher SOI scores than women in all countries. But the first three questions (ignoring non-1:1 gender ratios, differently biased sampling for different genders, different rates of homosexuality between genders, different behaviour of homosexuals of different genders, 30 partners cap on the second item, differently biased forecasts of the second item and other small details that won't affect the score much) - simply have to be identical for men and women, so the entire difference would have to be explained by items 4 to 7, which have relatively low weights!

The differences between men and women can be really extreme for some countries, Ukraine has 50.79±28.92 (mean±sd) for men, and 17.36±8.65 for women, which means that either Ukrainian men, or Ukrainian women, or both, are notoriously lying when asked about past and future sex partners. In most countries the differences are more moderate, with total 48-country sample's scores being 46.67±29.68 for men, and 27.34±19.55 for women. Latvia leads the way with smallest difference, and so most likely greatest honesty, with 49.42±23.61 for men, and 41.68±26.68 for women, what can be plausibly explained by just differences in attitude. (fake lie detector experiments have shown it's almost exclusively women who are lying when answering questions like that)

SOI seems to be considered quite useful by psychologists, it correlates with many nice things, not only other questionnaires, but country SOI averages correlate with various demographic, economic, and health scores in quite systematic way. Still, I cannot read papers about it without asking myself - why didn't they bother to perform this basic sanity check - which would detect huge number of outright lies in answers. And more importantly - what proportion of "serious science" suffers from problems like that?

References: The 48 country SOI study, fake lie detectors shows which gender lies more.

I just finished a two-week experiment of trying to live by a point system. I attached a point value to various actions and events, and made some effort to maximize the score. I cannot say it was successful in making me achieve more than normally during the same period of time, but it made more clear some of the problems with my behaviour.

Here's some notes from my experiment:

• Points are marginal utilities, they are for things you want to do more of, but don't due to akrasia. If you want to exercise more you'll assign very high value to half an hour of exercise, but that doesn't mean you want to spend 8 hours a day cross-training. As expected, I got most points for thing that weren't that important but I finally got myself to do more.
• Values can be put on terminal values (results), or instrumental values (effort, and partial results). Valuing only the former tends to be highly demoralizing, valuing the latter tends to be highly encouraging.
• It's a good idea to assign some points to cleanup of your system (decide against doing something that was previously on your list, get trivial thing off your list). It cleans your mind, even if it doesn't progress your big goals.
• Another good idea are points for sitting down and thinking, making mindmaps and so on.
• One big problem that the system didn't cover at all were distractions, like spending too much time on Wikipedia or TvTropes.
• Another big problem were times when I didn't have enough energy to do anything big, but wasn't sleepy enough to sleep. It's usually pure waste of time.
• During the first week I was more successful (in terms of points) almost every day, then it went far downhill, perhaps due to my enthusiasm running out. This made me decide against extending the experiment past the original two week schedule.
• In other words - there was some value to it, but akrasia mostly won again.

Anyone else wants to share their anti-akrasia experiments?

I love seeing counter-evidence for everything. I estimate that while most of my beliefs are true (otherwise I wouldn't believe them in the first place), a small percentage is almost certainly completely false - and I don't really have any reliable way of telling the two apart.

Indiscriminatingly looking for counter-evidence for all of them can be very rewarding - the ones that are true are much more likely to sustain the assault of it than the ones that aren't. Yes, I might ignore counter-evidence of something that's false, or accept it for something that's true, ending up worse off, but it seems plausible that on average it should improve quality of my beliefs.

For example some of the standard beliefs about human sociobiology that seemed to be extremely widely held here are:

• Men have lower chances of having any kids than women
• Richer people, especially men, are more likely to have kids, and have more kids

Charting Parenthood: Statistical Portrait of Fathers and Mothers in America disagrees with them.

• It's true that young men are less likely to have children than young women, but it reverses at old age, and total chance of having children during lifetime is - for people over 45 - 84% for men, and 86% for women. As some of childless men might still have children between 45 and their death (quite a few according to data), but almost no woman will, the difference must get smaller by the time of death, or it might even reverse. This is pretty convincing evidence against a major gender difference in chance of having children, at least as far as modern America is concerned.
• The chance of having children is highest for people between 100% and 200% of poverty line (poverty line, not median income, these are all poorer than average people). For women going either lower or higher reduces chances of having children considerably. For men getting poorer reduces chance of having children considerably, while getting richer reduces it but only slightly. However - younger people are much more likely to fall below poverty line, and men tend to reproduce later, so even that can easily be an artifact of age-income relationship. The data is fully compatible with both poverty and wealth being negatively correlated with chance of having children in both genders.

These are not direct tests of sociobiological claims, so what we have is not exactly what we would like to, but I find them to be quite convincing counter-evidence. My belief in these sociobiological claims is definitely lower than before, at least as far as they concern modern world, even though I can imagine more focused studies changing my mind back.

More counter-evidence for things we commonly believe here, sociobiological or otherwise, welcomed in comments.