Aaron Swartz was a key figure in the successful fight against SOPA (Stop Online Piracy Act). On Thursday (August 16) he will tell how a tiny number of online activists managed to defeat a bill pushed by the entertainment industry, which had spent hundreds of millions of dollars per year trying to get it passed and believed its survival was threatened if it wasn’t passed. Aaron will speak at 7 pm at ThoughtWorks, 99 Madison Avenue, 15th floor, New York NY.

One of my friends loves her Ipad so I decided to get one. On the same day I started looking at options Microsoft announced their tablet, the Surface. It sounded good. I decided to do more research.

I went to the Apple Store in Palo Alto. The main things I will do with my Ipad are read books and magazines and watch movies. I asked to see a movie on “the new Ipad”. No movies were available, so I watched a trailer for Brave. It was not a good experience. Lots of glare. How much worse could the Surface be? I wondered.

I was curious about the new naming policy. No “Ipad 3″, just “the new Ipad”. “Why isn’t this called the Ipad 3?” I asked an employee. “Company policy,” he said. “What’s the reason for the policy?” I asked. “They don’t tell us that,” he said.

I left the store thinking I would wait for the Surface. Later that day I had lunch with a friend, Steve Omohundro, who (with colleagues) long ago predicted the tablet computer and won a design competition (possibly sponsored by Apple) for doing so. He and his colleagues gave a talk about it and Steve Jobs was in the audience. I forgot to ask him what he thinks of the Ipad and the Surface.

An article by me about how a woman figured out she had gluten intolerance is on Boing Boing today. I first learned about the story through a comment this blog. Thanks, Ginna!

If you know of a case where someone (such as you) improved their health through science (= looking at data, experimentation, collecting data — the opposite of trusting experts) please let me know. In the gluten intolerance story, the experimentation and data collection were as simple as trying a gluten-free diet and learning its effect on (a) how you feel and (b) a kidney function score.

My self-experimentation began because of one tiny thing — an article I noticed in the Brown University Science Library about teaching mathematics to college students. “The best way to learn is to do,” it started. Which made sense. To learn how to do experiments — one of my goals as a graduate student — I started doing self-experiments. Let’s imagine I had never seen that article, which is entirely possible. In this parallel universe, I become a psychology professor and then one day notice that someone else has done the personal science I have actually done. Has written “ Self-experimentation as a source of new ideas,” The Shangri-La Diet, and so on, including the experiments I’ve described on this blog. How would I react?

Many things about it would not impress me. You devised an arithmetic test to measure your brain function — so what? You measured yourself for a long time — big deal. You did an experiment — yawn. I might be slightly impressed by the experimental designs, which are simple and effective. Most experimental psychology uses more complex designs. What would baffle me would be the discovery of safe powerful beneficial treatments. How did this guy find these treatments? For example, the first experiment in “Self-experimentation as a source of new ideas” is about the effect of breakfast on early awakening. Eliminating breakfast reduced the fraction of days with early awakening from about 50% to 10%. Not eating breakfast is easy and perfectly safe. I don’t know of anything like this in all sleep research.

To a psychology professor, doing an experiment on one’s sleep is nothing. Finding something naturalistic (= not a drug) and sustainable that caused a big improvement, however, would be . . . unprecedented? Seemingly impossible? Psychology professors study everyday topics of great interest, such as memory and problem-solving and happiness, quite often. They would love to find easy safe sustainable non-drug ways of improving these things by large amounts. But I can’t think of a single example.

I thought about how hard it is to find big beneficial experimental effects (it’s easy to make things worse) when I read this post by the economist Yanis Varoufakis. He is excited about working for an online game company (Valve) because the nature of their game will allow experimental study of economics.

Econometrics is a travesty! . . . Econometrics purports to test economic theories by statistical means. And yet what it ends up testing is whether some ‘reduced form’, an equation (or system of equations), that is consistent with one’s theory, is also consistent with the data. The problem of course is that the ‘reduced form’ under test can be shown to be consistent with an infinity of competing theories. Thus, econometrics can only pretend to discriminate between mutually contradictory theories. All it does is to discover empirical regularities lacking any causal meaning. [Why is he sure they lack any causal meaning? — Seth]. . . The reason for this unavoidable failure? None other than our inability to run experiments on a macroeconomy such as rewinding time to, say, 1932, in order to see whether the US would have rebounded without the New Deal (or to 2009 to see what would have happened to the US economy without Ben Bernanke’s Quantitative Easing). Even at the level of the microeconomy, keeping faith with the ceteris paribus assumption (i.e. keeping all other things equal in order to measure, e.g., the relationship between the price of and the demand for milk) is impossible (as opposed to just hard).

In sharp contrast to our incapacity to perform truly scientific tests in ‘normal’ economic settings, Valve’s digital economies are a marvelous test-bed for meaningful experimentation. . . . We can change the economy’s underlying values, rules and settings, and then sit back to observe how the community responds, how relative prices change, the new behavioural patterns that evolve. An economist’s paradise indeed…

I find this baffling. It’s like thinking: Now I can write. Soon I will be writing stuff that the world wants to read! Okay, now he can do experiments. Good. After a few of them, I suppose, he will learn what every experimental scientist knows and confronts every working day: it is incredibly hard to do interesting experiments. The “sharp contrast” between the new setting and the old one has yet to be demonstrated.

Okay, how did I find a bunch of big beneficial safe sustainable effects? I am now finishing a paper in which I try to answer this question. To be brief: 1. As I’ve said, I believe that the distribution of surprise/observation follows a power-law-like distribution. Almost all observations, very little surprise, a tiny fraction of observations, great surprise. Which is pretty obvious. 2. The “slope” (parameter) of the distribution depends on subject-matter knowledge (more knowledge = more favorable slope, i.e., “chance favors the prepared mind”), scientific skill (more skill = more favorable slope), and novelty (more novelty = more favorable slope). I was in good shape on all three. For example, when I studied sleep, I knew a lot about sleep. Novelty is enormously important. In my personal science I could easily study treatments (e.g., not eating breakfast) and dimensions (e.g., how rested I felt when I awoke) that had rarely if ever been studied before. I could do this again and again, keeping novelty high and thus keeping the slope very favorable. (Varoufakis will get a burst of novelty when he begins experimentation (the situation is new) but forced to use that situation for all his experiments the novelty will run down, making the slope of the distribution less favorable.) 3. My cost/observation was very low and the benefit/observation remarkably high (I was improving my own health). So I was very motivated to make observations. My answer in the paper is a little more complicated but that’s most of it.

I’ve measured my fasting blood sugar (= blood sugar before breakfast) for about four years. I began out of curiosity but became alarmed when the values approached “pre-diabetic” (> 100 mg/dl, diabetic is 126 mg/dl or so). Eventually I learned that walking an hour/day put them in the 80′s consistently. Perhaps 84 is optimal, who knows.

I have used Abbott test meters and strips. They need so little blood that testing is painless. Recently (January 2012?) Abbott introduced new “butterfly” test strips that “wick” the blood. The meters stayed exactly the same. The new test strips are certainly better. I started to use them. I started using them after a gap (a month?). All of a sudden my scores were about 5 mg/dl better — for example, 84 instead of 89. I assumed this was due to lifestyle changes on my part. I was walking more, I was more muscular, whatever. These were plausible explanations. Surely Abbott had not corrected a huge mistake (given the size of the business, the importance of diabetes, and the need for accurate test strips, to be consistently off by 5 mg/dl would be a huge mistake).

Now I wonder. I recently found some old-style test strips, barely expired (2012/04). I have compared them to the new-style strips (expiration 2013/06). Here are my results:

Morning 1. New: 81, 84. Old: 99, 91, 100.

Morning 2. New: 80. Old: 96, 94, 95.

Morning 3. New: 84. Old: 104, 97, 105.

Morning 4. New: 86. Old: 101, 100, 100.

These results involve three different meters. The old strips come from three separate vials. It is clear that the old strips produce readings much higher (about 15 mg/dl higher) than the new strips.

The old strips are expired but I doubt they got 15 mg/dl worse in 1-2 months. I expect they are accurate when they leave the factory and slowly get worse. Now I have some idea of how much worse (and in what direction). Apparently there is a big increase in bias with little increase in variability. I’ve gone from batch to batch before and never noticed a difference. Only when comparing the new strips with the old has a difference been clear. The earlier comparison, with a 5 mg/dl difference, compared unexpired old strips with the new strips.

I conclude that with the old strips, deterioration with age is worse than I expected and I should pay more attention to test strip age.