Ascription is an Anathema to any Enthusiasm

It is conventional to sing the praises of private schools while dismissing public schools as yet another example of Government’s ineffective nature. That rhetoric is part of the Republican campaign to undermine voter confidence in state based solutions. It’s a natural outgrowth of the Republican party’s real goal: to lower taxes and increase wealth disparity. You can’t achieve that goal if most of the public likes the services the government provides; i.e. health, transportation, safety, education.

So it’s with some amusement that I read this item from the New York Times.

WASHINGTON, July 14 — The Education Department reported on Friday that children in public schools generally performed as well or better in reading and mathematics than comparable children in private schools. The exception was in eighth-grade reading, where the private school counterparts fared better.

The report, which compared fourth- and eighth-grade reading and math scores in 2003 from nearly 7,000 public schools and ore than 530 private schools, found that fourth graders attending public school did significantly better in math than comparable fourth graders in private schools. Additionally, it found that students in conservative Christian schools lagged significantly behind their counterparts in public schools on eighth-grade math.

Conventional wisdom is wrong it is just Republican Propaganda. Public schools may not be as good as we’d like, but private schools aren’t any better.

A fun article in a recent the New Yorker about a radio station in New York City mentions in passing that Eliot Spitzer the NY attorney general is currently investigating a bunch of radio stations for taking payola. I’m kind of glad, if surpised, that there are still consumer protection laws on the books about that.

Meanwhile bloggers can now take payola, see over here.

This article about blogger payola is wonderfully ironic once you notice the amazing assortment of advertising techniques in use by it’s web site.

I’ve been awaiting this book for months; and I finally got a copy. Polarized America: The Dance of Ideology and Unequal Riches by Nolan McCarty, Keith T. Poole, and Howard Rosenthal. I paid full price, which is both totally out of character and an indication of how very important I think this book is, but you don’t have to.

This book’s big picture is that very key statistics have all move near lock step very rapidly over the last 25 years in: income distribution, political polarization, percentage foriegn born, declining local government services, and others. These are connected in a complex dance.

Here’s something I didn’t realize. While the Republican party has been moving rapidly right; the Democratic party has on the one hand abandoned their advocacy of general welfare platforms and shifted toward a what they notably refer to as issues of ascription. These are direct decendents of the civil rights movement. These are issues about ascriptive characteristics (race, gender, sexual preference) of individuals. We should reclaim the general welfare issues!

Here’s some more fun stuff gleaned from the book I’m reading.

One of the canonical reasons for poor judgement is known as availablity; i.e. we think and work using the tools that are easily at hand. If a tool is beyond reach we are far less likely to apply that tool. There are lot of examples. The folks that plan for floods, for example, rarely plan for floods other than those they have experienced. If your asked a mess of people to estimate how many words start with the letter r, like road, v.s. how many end with the letter r, like tour they will get the wrong answer, begins with, because your noodle is set up to recall words by their first rather than their last letter. I love the variant of that example; people will guess that abstract nouns, like love, hate, truth, are occur more commonly than concrete knowns, like door, street, bicycle. Apparently people have an easier time recalling abstract nouns v.s. concrete ones. Who knows why, possibly because the abstract nouns cover broad swaths of experiance.

In reading about availablity, and the other insights outlined in this book, I find myself thinking about how they get mixed into the ways people market products, debate issues, manage planning, and even how you could create carnival games out of them. One reason a component manufature has to give way a huge number of free samples is to assure that his components are highly available at the moment when some random designer is reaching out to pick something up to solve his problem. That’s one reason Open Source works; it adopts a r-Strategy.

This example of availably is particularly thought provoking. A reasonably standard formal way to estimate risk is to create a fault tree. You can view lots of examples at Google Images. The process of making a fault tree is lot like the process people use in making any plan. You attempt to collect all the contengencies and then sort out their likelyhood and dependencies. For example in thinking about why the car might not start you might include dead battery and left headlights on by mistake. Professional planning and fault tree work involves getting experts to help. You might do that by doing surveys and investigations of various car starting falures; or you might interview experts (say are mechanics) and have them help generate contengencies and probablities of those contengencies.

Because of availablity asking the experts does not work.

Say you have methodically assembled a large and nominally complete fault tree and because you doubt you’ve thought of everything you include a node for “other causes.” At that point your fault tree covers a 100 percent of the failure scenerios. You go to the experts and ask them to help by assigning 100 points (i.e. percentage chance) across the perimeter of the tree.

Why doesn’t that work?

It doesn’t work because studies show that you get radically different answers from the experts if you check their work by varying the fault tree you present. If you have a tree with 10 nodes and you collapse 2 or 3 into the “other causes” node the experts will then raise their estimates for all the remaining causes. Because the nodes you merged into “other causes” are no longer easilly available.

Of course what’s in that “other causes” node is the long tail of contengencies.

Here’s a thought provoking intersection between behaviorism and statistical thinking; plucked out of this book.

Here’s a very naive model of behaviorism: If the animal behaves well we reward him, and if he behaves poorly we punish him. This primitive version of behaviorism is fraught with problems; but it will do for this discussion.

A very naive statistical model of behavior breaks into two parts; with average behavior and random behavior around that. Animal trainers are experts in leveraging that random bit. If you want to train a goldfish to swim clockwise then before you feed him then you wait for the random clockwise turn before feeding.

Of course animal training is a two way street. Pets expend a great deal of effort attempting to train their owners to feed them. It’s fun to walk in front of the tanks at a large pet store wearing the same color shirt as the staff an watch the fish attempt to trigger your into feeding them.

The hope of training is that you shift the average; but that takes time and in the meanwhile random variations will generate occasional good and bad performances relative to the mean.

So here’s the rub. If the animal behaves in an exceptionally good or bad manner it is likely that in following period his behaviors will return to the previous average behavior. In the jargon of statistics this is an example of regression to the mean.

Regression to the mean is enough to train a bad behavior in the trainer! Consider this scenario: the animal behaves well, the trainer rewards him, and then in the following training rounds the animal is certain to regress back toward his average behavior. The trainer learns from that the reward triggered the regression. The trainer learns not to reward. Which is bogus.

But it get’s worse. Consider this scenario: the animal behaves badly, the trainer punishes the behavior, and then in the following rounds the animal’s behavior randomly regresses back to the mean. The trainer learns that punishment precedes behavior improvements and; not because the animal is learning anything but because the statistics say so. The statistics alone are enough to train the trainer to punish bad behavior but not reward good behavior.

That is very bad! It’s a fundamental insight of sophisticated behaviorism that punishment is far less effective than rewards; so much so that punishment often doesn’t work at all! Only a stupid or a captive animal will put up with training based on punishment. This problem is redoubled because if you punish the animals become more cautious; which reduces the not just the random variance (which you need) but also suppresses the smart active searching you want the animals doing. Punishment doesn’t work on smart animals and when you can get away with it makes the animal become stupid.
What a mess! Notice that you don’t need an animal to teach the trainer this bogus behavior pattern of never reward, but do punish. If you had the trainer work to train a random number generator he’d learn the same lesson. There really isn’t a more stupid beast than a random number generator. Which I think goes a long way toward explaining why fans of punishment often describe the animals they are trying to train stupid.