20
Dec 11

## Candidate Match Game

USA Today has an interesting quiz you can take that will match you up with a GOP presidential candidate. It’s here.

I didn’t find the particular questions and answers satisfying, but I imagine they’ve tried to match these up as closely as possible with the candidates’ positions. There are some interesting features to the quiz. For starters, it’s a nice piece of information architecture. The colors make it easy to track how each answer is reflected in each of the candidates’ rankings. The sliders, which let you set importance of the issues, are fun to use, and you can adjust any one of them at any time, so you can see how varying your weights effects each candidates’ score. I like that it also shows candidate “you”: this gives a feel for how closely you match up with the candidates in general. The closer the top candidate is to your bar, the closer the fit. Another nice touch: the way that the candidates are obscured by only showing silhouettes until you are done filling out the quiz. I would imagine this makes it less likely that you will try and tweak your answers and weightings to favor your favorite candidate, though if you look closely enough you can make out at least a couple of the candidates just from their silhouettes.

One final interesting note. The weightings are linear and additive. Another way to do the quiz might be to find the candidate with the best weighted geometric average. Depending on how the sliders were done, this could give you a “kill switch” to eliminate candidates who took a position opposite from yours on a single, vital issue (ie abortion). On the other hand, I suppose if you are a single issue voter you already know how each of the candidates stand on that issue.

6
Dec 11

## My oh my

Noted without comment, visit Biostatistics Ryan Gosling !!! for more gems like the one above.

3
Dec 11

## The first thing you learned about probability is wrong*

*or dangerously incomplete.

I’ve just started reading Against the Gods: The remarkable Story of Risk, a book by Peter Bernstein that’s been high on my “To Read” list for a while. I suspect it will be quite interesting, though it’s clearly targeted at a general audience with no technical background. In Chapter 1 Bernstein makes the distinction between games which require some skill, and games of pure chance. Of the latter, Bernstein notes:

“The last sequence of throws of the dice conveys absolutely no information about what the next throw will bring. Cards, coins, dice, and roulette wheels have no memory.”

This is, often, the very first lesson that gets presented in a book or a lecture on probability theory. And, so far as theory goes it’s correct. For that celestially perfect fair coin, the odds of getting heads remain forever fixed at 1 to 1, toss after platonic toss. The coin has no memory of its past history. As a general rule, however, to say that the last sequence tells you nothing about what the next throw will bring is dangerously inaccurate.

In the real world, there’s no such thing as a perfectly fair coin, die, or computer-generated random number. Ok, I see you growling at your computer screen. Yes, that’s a very obvious point to make. Yes, yes, we all know that our models aren’t perfect, but they are very close approximations and that’s good enough, right? Perhaps, but good enough is still wrong, and assuming that your theory will always match up with reality in a “good enough” way puts you on the express train to ruin, despair and sleepless nights.

Let’s make this a little more concrete. Suppose you have just tossed a coin 10 times, and 6 out of the ten times it came up heads. What is the probability you will get heads on the very next toss? If you had to guess, using just this information, you might guess 1/2, despite the empirical evidence that heads is more likely to come up.

Now suppose you flipped that same coin 10,000 times and it came up heads exactly 6,000 times. All of a sudden you have a lot more information, and that information tells you a much different story than the one about the coin being perfectly fair. Unless you are completely certain of your prior belief that the coin is perfectly fair, this new evidence should be strong enough to convince you that the coin is biased towards heads.

Of course, that doesn’t mean that the coin itself has memory! It’s simply that the more often you flip it, the more information you get. Let me rephrase that, every coin toss or dice roll tells you more about what’s likely to come up on the next toss. Even if the tosses converge to one-half heads and one-half tails, you now know with a high degree of certainty what before you had only assumed: the coin is fair.

The more you flip, the more you know! Go back up and reread Bernstein’s quote. If that’s the first thing you learned about probability theory, then instead of knowledge you we’re given a very nasty set of blinders. Astronomers spent century after long century trying to figure out how to fit their data with the incontrovertible fact that the earth was the center of the universe and all orbits were perfectly circular. If you have a prior belief that’s one-hundred-percent certain, be it about fair coins or the orbits of the planets, then no new data will change your opinion. Theory has blinded you to information. You’ve left the edifice of science and are now floating in the either of faith.

1
Dec 11

## Wasting away again in Martingaleville

Alright, I better start with an apology for the title of this post. I know, it’s really bad. But let’s get on to the good stuff, or, perhaps more accurately, the really frightening stuff. The plot shown at the top of this post is a simulation of the martingale betting strategy. You’ll find code for it here. What is the martingale betting strategy? Imagine you go into a a mythical casino that gives you perfectly fair odds on the toss of a mythically perfect coin. You can bet one dollar or a million. Heads you lose the amount you bet, tails you win that same amount. For your first bet, you wager \$1. If you win, great! Bet again with a dollar. If you lose, double your wager to \$2. Then if you win the next time, you’ve still won \$1 overall (lost \$1 then won \$2). In general, continue to double your bet size until you get a win, then drop your bet size back down to a dollar. Because the probably of an infinite loosing streak is infinitely small, sooner or later you’ll make \$1 off of the sequence of bets. Sound good?

The catch (you knew there had to be a catch, right?) is that the longer you use the martingale strategy, the more likely you are to go broke, unless you have an infinitely large bankroll. Sooner or later, a run of heads will wipe out your entire fortune. That’s what the plot at the beginning of this post shows. Our simulated gambler starts out with \$1000, grows her pot up to over \$12,000 (with a few bumps along the way), then goes bankrupt during a single sequence of bad luck. In short, the martingale stagy worked spectacularly well for her (12-fold pot increase!) right up until the point where it went spectacularly wrong (bankruptcy!).

Pretty scary, no? But I haven’t even gotten to the really scary part. In an interview with financial analyst Karl Denninger, Max Keiser explains the martingale betting strategy then comments:

“This seems to be what every Wall Street firm is doing. They are continuously loosing, but they are doubling down on every subsequent throw, because they know that they’ve got unlimited cash at their disposal from The Fed… Is this a correct way to describe what’s going on?

Karl Denninger replies. “I think it probably is. I’m familiar with that strategy. It bankrupts everyone who tries it, eventually…. and that’s the problem. Everyone says that this is an infinite sum of funds from the Federal Reserve, but in fact there is no such thing as an infinite amount of anything.”

Look at the plot at the beginning of this post again. Imagine the top banking executives in your country were paid huge bonuses based on their firm’s profits, and in the case of poor performance they got to walk away with a generous severance package. Now imagine that these companies could borrow unlimited funds at 0% interest, and if things really blew up they expected the taxpayers to cover the tab through bailouts or inflation. Do you think this might be a recipe for disaster?