This week’s best statistics joke: median rent.
Here’s an interactive visualization showing state-by-state migrations within the US, by Chris Walker.
It’s not possible to reconstruct all migrations between states from this chart. The data are available in a spreadsheet that the American Community Survey (part of the Census Bureau) puts out.
In case you’re wondering, the (ordered) pair of states with the most movement is California to Texas. Tyler Cowen would have forecasted that, but it’s worth pointing out that this is hardly surprising as California and Texas are the states with the largest population. Relative to the population of the target state, Californians are most likely to move to Nevada, Washington, Arizona, and Oregon; Texans are most likely to move to Oklahoma, New Mexico, Louisiana, and Arkansas. For non-American readers, I just said “people are most likely to move to nearby states”, which is the sort of thing that it’s easy to lose track of in my position living in San Francisco and generally surrounded by transplants from far away.
If I could spare the time I’d try to visualize this – which pairs of states have greater flows between them than would be expected from their populations and the distance between them? The prototype here would probably be the flow from the northeastern states to Florida.
Jeremy Kun on the UCB1 algorithm for the multi-armed bandit problem. (Incidentally, none of the authors of the paper introducing the algorithm were affiliated with UCB – it stands for “upper confidence bound”.)
What is the sound of sorting?
A friend of mine just got a job at Swapdom, which organizes multi-way swaps of clothes among people who don’t want them anymore and want to rejuvenate their wardrobes. You can search the community and point to things you would like and things you’d happily give up in exchange for them, and they find swaps that actually work. In particular they orchestrate multi-way swaps (A gives to B, which gives to C, which gives to A, or even longer cycles).
If you’re Alvin Roth, you can win a Nobel* Prize for this stuff. At least if it’s kidneys being traded instead of clothes. The Nobel foundation has both popular and technical expositions of his work in market design; the largest kidney swap in history, a few months ago, involved 28 kidneys.
(Disclaimer: I don’t actually know what’s going on behind the scenes with Swapdom’s algorithm; my friend is not a technical person.)
Cloudflare (a web security company) has a primer on elliptic curve cryptography and its uses for privacy and security online. Quick version: RSA cryptography relies on the fact that multiplying integers is easy but factoring them is hard. Elliptic curve cryptography relies on the fact that there’s a group law for elliptic curves over the integers mod n, and applying that group law repeatedly (exponentiation) is easy but determining how many times that law was applied (taking the discrete logarithm) is hard.
I’ve been on a bit of an inadvertent hiatus from blogging – stuff picked up at my day job and then I got married. Time to get back into the swing of things.
I went to the DMV this morning. (That’s the “Department of Motor Vehicles”, for non-American readers; this is the government agency that handles vehicle registration and driver licensing.) It was “fun”, by which I mean not fun, but I was reminded of this clip from Seinfeld (transcript here):
JERRY: Elaine, what percentage of people would you say are good looking?
ELAINE: Twenty-five percent.
JERRY: Twenty-five percent, you say? No way! It’s like 4 to 6 percent. It’s a twenty to one shot.
ELAINE: You’re way off.
JERRY: Way off? Have you been to the motor vehicle bureau? It’s like a leper colony down there.
ELAINE: So what you are saying is that 90 to 95 percent of the population is undateable?
ELAINE: Then how are all these people getting together?
Jerry is implying here that the people at the DMV are a good sample of the general population. This seems like a reasonable assumption, although of course we can quibble:
- rich people are more likely to own multiple cars, which means they have to go the DMV more often to handle car-registration-related business;
- similarly, poor people are more likely to not have cars at all and perhaps not even be licensed drivers. Since Seinfeld is set in New York, which has low car ownership rates, this is especially true, although it might be counteracted by the fact that public transit is better in the more wealthy parts of that city;
- results should vary by time of day, day of week, and DMV office
Still, it kept me amused while I waited.