Climategate: The Perils of Global Warming Models
Everyone readily admits that things aren’t always what they seem. But are we really applying this knowledge in our daily dealings? Are we consciously ferreting out the illusory from the reality? I think not.
For instance, despite overwhelming evidence to the contrary, we aren’t really being run by pandering politicians, self-serving lobbyists, fanatical environmentalists, and greedy Wall Street manipulators. They are the illusion.
There is another even more powerful (but much less visible) agent behind all of these puppets.
The person behind the screen is the computer programmer. And, just like in the Wizard of OZ, they do not want you to look at this real controller.
I’ll probably have to turn in my membership card, but as a computer programmer (and physicist and environmental activist) I’m here to spill the beans about the Wiz.
The first hint of trouble is spelled out in Wikipedia’s explanation about computer programmers:
The discipline differs from many other technical professions in that programmers generally do not need to be licensed or pass any standardized (or governmentally regulated) certification tests in order to call themselves "programmers" or even "software engineers."
My layperson explanation is that computer programming is all about making assumptions, and then converting these into mathematical equations.
The big picture question is this: Is it really possible to accurately convert complex real-world situations into ones and zeros? Hal may think so, but higher processing brains say no. Yet this is continuously attempted, with very limited success. Let’s pull the screen back a bit more.
We’ll start with an example about how such a model makes assumptions.
One of the computer programs I wrote was for debt collectors. A typical scenario was that a debtor was given a date to make a payment and the collection company didn’t receive it on time. What response is then appropriate?
In such a circumstance the computer program typically makes an automatic contact with the debtor. (Remember there are thousands of these debtors, and it would be prohibitively time consuming for an agency person to manually check into and follow up each case.)
So what to say in this correspondence to the debtor? Well, it comes down to the assumptions made by the computer programmer.
The programmer tries to simplify such situations into mathematical options. In this case they may decide that the question is: “Does the debtor have the money to make this payment: yes or no?” This relatively basic choice then leads to a Boolean progression within the program.
How does the programmer (model) decide on yes or no? Well, other indicators would be used (e.g., were prior payments made on time) to come up with a statistical probability.
Of course, any computer model is not one set of choices, but rather a whole series of yes/no (if/or) calculations that lead to a conclusion. In a complex situation (e.g., debt collection, climate change, or financial derivatives) there could easily be a hundred such choices to deal with.
To understand the implications of that, let’s just consider the case where there are ten such decision points -- each with a "yes" or "no" answer. At the end of such a pipeline, that means that there are 210 (i.e., 1024) possible results. That’s a lot of different potential conclusions.
Unfortunately, there are actually many more possibilities! The assumption that this debtor situation could be condensed down to a "yes" or "no" answer is not accurate. There are several other real situations that fall outside of "yes" or "no."
For instance, what if the debtor never got a notice in the first place that the amount was due by the date the agency is monitoring? Or what if the debtor sent the money and it got lost in transition? Or what if the debtor made the payment to the original person they owed, rather than the collection agency? Or what if the debtor sent in the money on time, and the collection agency incorrectly didn’t credit the debtor for the payment? Etc., etc.
For the computer program (model) to be accurate, all of these scenarios need to be able to be handled properly (legally, timely, etc.). Can you begin to see the complexity here, just with this very simple example of a payment not being received on time?
There is still another significant factor (we’re up to #4 now) not mentioned yet. What about the situation where the debtor hasn’t paid, but it’s because his child has MS, and he has no insurance? How does a computer programmer write code for more abstract concepts, like “fairness”? In other words, can ones and zeros be arranged in such a way to represent intangibles? I think not.