The blog The Wrong Stuff has some excellent interviews about how people deal with errors or mistakes. It reminded me of a book I wanted to read, but I never got past the book review. Basically, most experts are no better at predicting the future than a drunken monkey. This quote reminds me of why I hate talking to smart people:
“Most people tend to dismiss new information that doesn’t fit with what they already believe. Tetlock found that his experts used a double standard: they were much tougher in assessing the validity of information that undercut their theory than they were in crediting information that supported it.”
Smart people are better at rationalizing and defending their opinions no matter how inane. That doesn’t mean they aren’t useful. Smart people excel at analyzing subjects for which they have no personal interest, i.e. the price of wheat futures. If they could learn to apply that same Vulcan logic to the things they care about, they would realize that most of what they believe rests on a very shaky foundation. Then they might be more open to information that changes their mind. Or they might, like me, wallow in a tar pit of indecisiveness.
The Wall Street Journal reports that after 128 years in business, The Bank of Japan has promoted its first female branch manager. This example points clearly to the self-destructive sexism that will doom Japan to decline. Japan has a severely low birthrate which will put immense pressure on their economy as their population ages and shrinks rapidly. They need more people, yet they refuse to allow immigration. The simple, obvious solution is to make more effective use of the talents of half their population, and yet in 2010 they still haven’t managed to integrate women into the workplace. I’m going to miss Japan when it finally disappears.
The same article points to a report from the World Economic Forum on gender parity. The countries with the best gender parity are obviously the Scandinavian countries, while the US is ranked at 31. Here are some countries that rank higher than the US: South Africa, Lesotho, Sri Lanka, Mongolia, and Mozambique. How can these countries be “better” than the US for women? That’s when you have to peek behind the ranking and analyze their methodology. It’s atrociously stupid. They defined “gender parity” merely as the ratio of female over male for several metrics. For example, let’s say literacy rates for some 3rd world country is 30% for women and 25% for men. The parity score is a whopping 1.2, which exceeds a 1st world country with literacy rates of 95% (f) and 98% (m) with a ratio of just 0.96. But which country would you rather live in? Sri Lanka is ranked #6 for political empowerment largely because a woman was head of state for 23 out of 50 years (though Sri Lanka has been independent only 38 years!). But would anyone claim that Sri Lanka offers more political power to women than the US? The methodology and the metrics are completely awful, yielding a report that is worse than junk. Nevertheless, lazy, stupid reporters will repeat these rankings uncritically and shape public perception that the US is far behind other countries in gender equality. Seriously, South Africa – “Rape Capitol of the World” – is ranked #6! Didn’t anyone at the WEF raise an eyebrow at the strange rankings?
I wrote a little script to grab the weather forecasts from the Weather Underground for the past 6 months. I want to know how good their predictions are from 1 to 4 days out. Unfortunately, it’s been 20 years since I last looked at a statistics book. For my first pass through I computed the root mean square error of predicting the daily high temperature. For predictions 1, 2, 3, and 4 days away, the values are 2.9, 3.4, 4.9 and 5.0 respectively. That doesn’t sound so good to me, but I need to figure out what other metrics to squeeze out of this. For example, I want to compare their forecasts against a simple prediction algorithm: the weather will be just like today. I also want to know the reliability of a forecast, which I think is the confidence interval (95% confidence the prediction is within 1 degree of the actual temperature). Finally, I have the data on conditions and precipitation to chew on, too. For now, the error residuals are looking pretty bleak. You should always carry an umbrella.
This is my dog, Elvis. He’s a 2 year old Siberian Husky from a local animal rescue organization, which really consists of one frazzled woman with a dozen dogs packed in her house. I’ve had him for 5 months now and he’s turned out to be perfect: friendly, calm, gentle, clean, smart and playful. And when we’re walking, he scares people so they move out of our way.
I am lucky to have gotten this dog. Because I’ve never had a dog and I live in an apartment, the shelters I contacted from PetFinder either turned me down immediately or, in most cases, failed to respond. I’ve since met many people who also ran into this resistance from shelters and opted to get a dog from a breeder instead. The only reason I got Elvis is because the woman who runs this shelter was distracted by personal problems and allowed her much more reasonable and level-headed friends handle the adoption. They said it was common for shelters (including this one) to turn down lots of good homes, hoping to find a perfect home for their dogs. The people who run shelters think with their hearts, not with their brains.
The problem is that the number of surplus dogs & cats vastly exceeds the total capacity of all shelters to house and feed them. Consequently, the local animal control office is forced to euthanize thousands of animals. If a shelter can find a home for a dog, it frees up a spot to save one more dog. When these shelters hang on to their dogs, they are condemning another to its death. Worse, if a person grows frustrated and turns to a breeder, it sends an economic signal to continue breeding more dogs. The goal is to place as many dogs in good homes as possible. Obvious, yet it isn’t happening.
I’d like to build a website to help manage animal shelters. Sort of a “SAP for shelters”. It should track their inventory of pets and streamline communication with customers. It should manage volunteers and foster homes. It should monitor food, medicine, and supplies. It should keep track of finances, donations and lots of other stuff. But first I need to learn more about how shelters operate. If Once I get going, I hope to make this all open-source and encourage other, smarter web developers to fix my mistakes. This won’t solve all the problems faced by animal shelters, but it will help smooth the path to those solutions.
There is finally a growing recognition that the industrialized production of meat is bad for people and the planet. When something has a significant negative externality (e.g. pollution) a Pigovian tax is used to add the social costs to that product’s price. That’s why many economists support a tax on gasoline. The negative externalities include production of greenhouse gases and undermining our security interests by sending too much money to unfriendly countries. For meat, there are several negative externalities.
- Grazing animals produce more greenhouse gases (belching and farting) than cars.
- Industrial farms seriously degrade water quality because of large amounts of feces.
- It’s an inefficient use of water and arable land, both of which are subsidized by the gov’t.
- Over-consumption of meat leads to poor health, which adds to our total health care costs.
By adding a tax and/or removing agricultural subsidies for meat production, it raises the price to match the real total cost of a double cheeseburger. Though it is impossible to expect people to suddenly become vegetarians (I’ve struggled for 10 years), it is trivial for people to reduce the amount of meat they eat. Americans are grotesquely obese because they eat too much of everything. Reducing total food consumption means less meat consumption which leads to lower rates of obesity. Raising the cost of meat should hopefully push people to eat more fruits and vegetables. I think the biggest problem is America’s meat & potatoes food culture. People just don’t know what to do with vegetables. Incidentally, I asked Greg Mankiw, Bush’s economics advisor, about this and he agreed with a Pigovian tax on meat. (Isn’t the Internet great?) So you could likely get bourgeoisie support, but the overweight proletariat will riot to get cheap bacon. Mmmmmmm, bacon.
I first ran across Dunbar’s number in The Tipping Point. Robin Dunbar estimates that humans can maintain, on average, a group size of 150 people. Beyond that people are more like strangers. The Economist interviewed Cameron Marlow from Facebook, who has analyzed how people socialize on Facebook. The numbers are better summarized here. The key point is that people appear to have a close relationship (measured by regular communication) with only 5% of their overall group. The average number of friends on Facebook is 120 (though a big range), but people regularly communicate with only around 6 of those people (women have a few more close friends than men). I don’t think you can equate Facebook friends with Dunbar’s number because it’s too easy to add random people and never think about them again. But it’s interesting that the average is around the same.
I just got my vision tested 6 months after LASIK surgery. I think my prescription lenses were at -2.5 diopters and almost no astigmatism. I can now see at 20/15, which is better than “normal” vision. No dryness, itching, halos or blurred vision. There was mild dryness for 2 months after surgery, but one morning it completely disappeared. When I was researching LASIK I ran across lots of scary stories on anti-LASIK forums. But the studies I read showed a very high success rate, especially for my simple prescription. The neighborhood kids no longer call me a “four-eyed, skinny dork”. Now it’s just “skinny dork”, but I’m working on that.