Category Archives: Mathematics

Freeman Dyson

There’s a very interesting (and long) profile over at New York Times magazine:

Dyson is well aware that “most consider me wrong about global warming.” That educated Americans tend to agree with the conclusion about global warming reached earlier this month at the International Scientific Conference on Climate Change in Copenhagen (“inaction is inexcusable”) only increases Dyson’s resistance. Dyson may be an Obama-loving, Bush-loathing liberal who has spent his life opposing American wars and fighting for the protection of natural resources, but he brooks no ideology and has a withering aversion to scientific consensus. The Nobel physics laureate Steven Weinberg admires Dyson’s physics — he says he thinks the Nobel committee fleeced him by not awarding his work on quantum electrodynamics with the prize — but Weinberg parts ways with his sensibility: “I have the sense that when consensus is forming like ice hardening on a lake, Dyson will do his best to chip at the ice.”

Dyson says he doesn’t want his legacy to be defined by climate change, but his dissension from the orthodoxy of global warming is significant because of his stature and his devotion to the integrity of science. Dyson has said he believes that the truths of science are so profoundly concealed that the only thing we can really be sure of is that much of what we expect to happen won’t come to pass. In “Infinite in All Directions,” he writes that nature’s laws “make the universe as interesting as possible.” This also happens to be a fine description of Dyson’s own relationship to science. In the words of Avishai Margalit, a philosopher at the Institute for Advanced Study, “He’s a consistent reminder of another possibility.” When Dyson joins the public conversation about climate change by expressing concern about the “enormous gaps in our knowledge, the sparseness of our observations and the superficiality of our theories,” these reservations come from a place of experience. Whatever else he is, Dyson is the good scientist; he asks the hard questions. He could also be a lonely prophet. Or, as he acknowledges, he could be dead wrong.

But he’s got a pretty good track record.

OK, Now An Open Office Problem

So, I’m trying to import my Perl-generated file as a CSV into Open Office. Apparently, if the data coming into a cell is of the form “D.D.D” where “Ds” are digits, it obviously and absolutely must be a date, and it converts the incoming cell to that format.

Well, no. I wanted it to be (for example) literally “1.3.5.” Really. No kidding. It’s not 01/03/05. But it won’t let me do it.

I don’t want to have to manually go in and change the format for each cell where this happens, and even if I did, there’s no obvious way to do it and retain the original info without manually retyping the number with a single quote in front. Is there an Open Office guru out there?

BTW, I really appreciate the help with the Perl problem. It was invaluable (which means, it was very useful, but I don’t know how to pay for it, or what it was worth to those providing it).

Perl Problem

I’m going crazy with a script. Here’s the code:

$parent = @line_elements[8];
$lower_req = @line_elements[1];
print DEBUG “BEGIN \$lower_req is $lower_req, \$parent is $parent, \$req_num is $req_num.\n”;
if ($req_num eq $parent) {
print DEBUG “\$req_num is $req_num, \$parent is $parent, got a match!\n”;

And here’s the output:

BEGIN $lower_req is “2.1.1”, $parent is “1.1”
, $req_num is “1.1”.
BEGIN $lower_req is “2.1.2”, $parent is “1.1”
, $req_num is “1.1”.
BEGIN $lower_req is “2.1.3”, $parent is “1.1”
, $req_num is “1.1”.
BEGIN $lower_req is “2.1.4”, $parent is “1.1”
, $req_num is “1.1”.
BEGIN $lower_req is “2.1.5”, $parent is “1.1”
, $req_num is “1.1”.

Note that in each case, that $req_num is equal to $parent, and the line should be repeated with the statement that a match was found. Can another pair of eyes tell me why it’s not?

Fondling Balls

Iowahawk breaks out the calculator on poll reliability:

So if the sample size is 400, the margin of error is 1/20 = 5%; if the sample size is 625 the margin of error is 1/25 = 4%; if the sample size is 1000, it’s about 3%.

Works pretty well if you’re interested in hypothetical colored balls in hypothetical giant urns, or survival rates of plants in a controlled experiment, or defects in a batch of factory products. It may even work well if you’re interested in blind cola taste tests. But what if the thing you are studying doesn’t quite fit the balls & urns template?

  • What if 40% of the balls have personally chosen to live in an urn that you legally can’t stick your hand into?
  • What if 50% of the balls who live in the legal urn explicitly refuse to let you select them?
  • What if the balls inside the urn are constantly interacting and talking and arguing with each other, and can decide to change their color on a whim?
  • What if you have to rely on the balls to report their own color, and some unknown number are probably lying to you?
  • What if you’ve been hired to count balls by a company who has endorsed blue as their favorite color?
  • What if you have outsourced the urn-ball counting to part-time temp balls, most of whom happen to be blue?
  • What if the balls inside the urn are listening to you counting out there, and it affects whether they want to be counted, and/or which color they want to be?

If one or more of the above statements are true, then the formula for margin of error simplifies to
Margin of Error = Who the hell knows?

I think that the disparity among the polls is pretty good evidence of this. A lot of it, particularly the weighting is guess work, educated or otherwise. There’s only one poll that matters (though with all of the chicanery going on, even that one is going to be in doubt, particularly if it’s close on Tuesday). What a mess.