Updated to compare the put clean dishes on bottom option!

I put dishes away a lot – every day, it seems. From time to time I wonder – should I put the clean dishes on the bottom, in order to make sure I use them evenly? Or does it really matter – I do use all of them from time to time, so maybe that mixes them up enough?

Since my math is far too poor to do this analytically, I built a simulation. And since my primary “language” appears to be web pages, I built it in javascript on a web page.

It appears from running a few variations of the parameters that you can end up with some relatively unused plates even after several years worth of wash cycles, especially if you model the dish usage on a normal distribution. Uniform distributions recycle things pretty efficiently after 100 days or so. As one would suspect, putting the clean ones on the bottom gives you almost perfect uniformity!

Time Wastes Too Fast – And the Pursuit of Happiness Blog – NYTimes.com.

What would you do with a $40 Linux computer the size of a three-prong plug adapter?

The article goes on to imagine what would happen if you had an array of little computers everywhere because they were so cheap. Some ideas: file servers, e-mail filtering/processing gateways, in your TV, DVD, your digital photo frame, etc.

This misses the point. My problem isn’t that I don’t have enough computing power. I have a couple of laptops, a little NAS file server, a couple of media players (Roku Netflix box and Soundbridge), as well as the more special-purpose things in my DVD, Wii, etc.

My problem is that I have inconsistent and incoherent input/output devices. I have more remotes than I know what to do with, plus keyboards and mice, and I have screens everywhere – TV, computer monitors, that little fluorescent screen on the Roku Soundbridge – plus audio output in various speakers in various places. And I don’t even have that complex a setup (no home theater, no DVR). Mix in the storage issue (local hard drives, cloud storage, NAS storage, optical drives) and life sucks even a bit more.

A couple of observations. First is that many of the input devices are tied to a single device – the very nice 8 (9?) Netflix player remote, or the less nice credit-card sized DVD remote with 40 or so equally sized buttons). A lot of devices have single dedicated outputs as well – goes to the TV screen, or its own monitor. Efforts to make better remotes (ones with LCD screens, for instance) are heroic multi-node integration projects in the absence of standards (or are proprietary themselves).

My fantasy is a world in which input devices,  output devices, storage and computing power are all networked, but decoupled from one another. What is a computer, anyway? it’s computing power plus input device + screen + storage. Wouldn’t it be nice to “assemble” a computer dynamically in your home. Take your TV screen + a thumbwheel remote + some computing power and storage, put them together and surf the web. Oh, I want to blog on that? Put the keyboard in the mix. Now you’re watching a youtube, but need to go to the other room? Send the video to a different display & carry the remote with you, and keep on working. The video decoding is taking place in one of those $40 computers somewhere.

From the ads, at least, it sounds like the DVR world is getting there with multiroom playback, but this misses a much larger universe. I don’t want another computer. I just want screens & input devices that tap computing power somewhere.

Plugging In $40 Computers – Bits Blog – NYTimes.com.

I then built a regression model that looked at a series of political and demographic variables in each of these states and attempted to predict the percentage of the vote that the marriage ban would receive.

It turns out that you can build a very effective model by including just three variables:

1. The year in which the amendment was voted upon;
2. The percentage of adults in 2008 Gallup tracking surveys who said that religion was an important part of their daily lives;
3. The percentage of white evangelicals in the state.

These variables collectively account for about three-quarters of the variance in the performance of marriage bans in different states. The model predicts, for example, that a marriage ban in California in 2008 would have passed with 52.1 percent of the vote, almost exactly the fraction actually received by Proposition 8.

Unsurprisingly, there is a very strong correspondence between the religiosity of a state and its propensity to ban gay marriage, with a particular “bonus” effect depending on the number of white evangelicals in the state.

Marriage bans, however, are losing ground at a rate of slightly less than 2 points per year. So, for example, we’d project that a state in which a marriage ban passed with 60 percent of the vote last year would only have 58 percent of its voters approve the ban this year.

All of the other variables that I looked at — race, education levels, party registration, etc. — either did not appear to matter at all, or became redundant once we accounted for religiosity. Nor does it appear to make a significant difference whether the ban affected marriage only, or both marriage and civil unions.

It’s intriguing that education, race, and party aren’t particularly useful explanatory variables. I do get a little nervous when there’s a time variable introduced – there’s no mystical force pulling the political transformation along. And students of Roe v. Wade should recognize that these kinds of tides do turn, change course, etc.

via FiveThirtyEight: Politics Done Right: Will Iowans Uphold Gay Marriage?