Let’s suppose everything you have heard about global warming is true.
It really will warm by a half-degree Centigrade by 2050, or even by three-quarters of a degree, or whatever. Some areas will have slightly warmer nights, just like you’ve heard. And some places will find themselves just a bit wetter, others a bit drier.
Very well, global warming is true. Now what?
Well, not much. To understand why, we’ll have to understand conditional probability. But hold off on that for a moment.
Because first we have to comprehend the 2000 scientists who signed a letter demanding the Senate “take action” on climate change. Presumably, by “climate change” they meant global warming. But never mind.
Eight of the signers were “Nobel laureates.” Not Nobel winners in meteorology, climatology, or oceanography, of course, because old Alfred was unaware of those subjects. One Nobeler was Leon Lederman, 88, a retired neutrino physicist, who said, “if anything, the climate problem is actually worse than reported earlier.” Scary stuff.
Of most interest is that some of these “scientists” were not scientists. Many—I don’t know how many—were economists. And that is why we must understand conditional probability.
What do economists know about long-range climate forecasts? Nothing. Or, rather, nothing that the average citizen doesn’t know. In their training, economists learn the best way to disparage the Laffer Curve, but the equations of motion and radiative transfer never appear on their syllabi. So why did they sign?
Economists are like the Congressional Budget Office: they have to believe what they are told. Their models are conditional on assumptions given them from on high. So, if a climatologist says, “It will be a half-degree Centigrade warming by 2050” the economist has no choice but to believe him.
The economist says, “OK, it’s going to be slightly warmer. How does that affect my bottom line?” And off he goes, builds his models, and concludes “More study is needed. I’ll need more research funding.”
No, only kidding! They sometimes make definite, testable predictions which are rarely wrong.
But enough bad jokes. Time to understand conditional probability. Suppose we want to understand how global warming will affect something of interest to an economist, say, Social Security funding. We first have to accept that global warming is true, as we did at the beginning.
We then have to accept a whole string of assumptions of how slight changes in temperature will affect Social Security. We might theorize that people will live slightly longer in warmer weather: consider all those folks who head to Florida once their odometers pass 65. And longer lives lead to greater burdens on the Social Security trust fund. Makes sense, right?
Since this year is the first in which outlays are greater than income into that fund, people are living longer, and Lederman claims global warming is worse than we knew, this is certainly a plausible theory.
With theory in hand, the economist can then use probability to estimate the likelihood of various Social Security budgets, given the belief that global warming is real and given the string of assumptions tying Social Security to climate are true. This is a conditional probability estimate, and the conditions are those “givens” just mentioned.
He can then use the conclusion that, once global warming hits, higher payouts are likely. And after becoming bothered about this, he’ll sign a letter to the Senate.
The point is obvious: because an economist’s model says “Social Security in trouble because of global warming” it is not true that Social Security is in trouble because of global warming. It might not even be likely to be true.
Conditional probability works in a nested fashion. In order to fully quantify the probability of a theory, we have to string out the list of conditional statements, assigning separate probabilities to each assumption as we go along, and multiply them all together. Since probabilities of these kinds of events are numbers between zero and one, multiplying strings of probabilities together results in a much smaller chance the entire theory is true.
Even the IPCC says there is only a 90% chance that global warming is true. That’s our first conditional. Then we have to assign probabilities to the other assumptions in the economist’s model. People living longer will lead to higher Social Security payouts is true, but only conditional on payouts remaining at their same levels. And what’s the probability of that? Not high, even given no effect of climate of length of life. Say it’s only 40%.
With just these two estimates, using our multiplying rule, we have 90% x 40% = 36%. That’s the upper limit on the truth of the economist’s theory. And we haven’t even considered his other assumptions, all of which lower the probability that Social Security will be affected by global warming.
Most studies which claim “X will happen given global warming strikes” are in this boat. They all are subject to the rules of conditional probability. And the likelihood they are true are almost always overstated because the theory holders never multiply their probabilities out like we have.
This is much too complicated for one post: Next time, we’ll sort this all out.