Call me a heretic. Maybe I’m just a tad bit more skeptical than others. But I’ve been lumped in to a category of miscreants, rabble-rousers, and wholesale kooks because… well… I’m skeptical. And it is funny with respect to the fact that you might usually place me on the side with the so-called enlightened, liberal progeny. But the global warming issue is still chapping my hide, years after the science world reached “consensus” on the “facts”.
I don’t argue that man has no impact on the nature surrounding him. Man is a very key part of the natural world. I might concede that man is a key contributor of CO2 gasses in the atmosphere. It would be, to this mind, a specific, unseen cost associated with our activities here on the earth.
I might concede that CO2 is a significant greenhouse gas in the atmosphere. But where I diverge from the pack comes when we start seeing scientists whip out their crystal balls, begin having alternative science sessions and dictating the very real events that will take place tomorrow.
Somewhere between hurling insults at audiences, the green movement turned into little more than a cargo cult religion. You can’t be agnostic, or even have a smidgen of atheism when global warming and ecology are your gods du jour. All it takes are some whiz-bang statistical models.
And I have a problem with statistical models.
Look at the stock market. Here is a system that represents a very real, complex system that has been around for more than a hundred years. Some of the best and brightest minds in the world have thrown the most complex mathematics at untangling the web of human action only to walk away empty handed. Even the best statistical models designed for statistical mimicry are only good for a limited amount of time. Why?
The short answer is that the system itself is extremely complex. Just like weather and climate change, we can only produce close approximations of what we think might happen under a finite number of possible scenarios we can muster up. The variables, as of this writing, are seemingly infinite (infinite minus one or so). While we have boundless technologies to assist in the prediction of future events in the stock market, the capacity to represent every possible variable is far too small.
Part of the idea I’m trying to convey here can be found in the foundations of chaos theory in mathematics. A weatherman, running a computer and printing out pages of data and graphs, runs into a computer problem. In order to save time, he pushes the data through the computer again, trimming off a decimal place or two to save time. When he goes back to look at the results, the image produced varies wildly from the image produced on the first run. Why? In a case where you might think lopping off a couple decimal places would make no difference, those little decimal places have a significant impact elsewhere.
I’ll venture out on a limb to get a good place at the guillotine when I say this: If finite mathematics are beholden to precision, statistical mathematics are beholden to imprecision. Coupling imprecision as a means to predict a finite future is no more than trying to lop off a few decimal places and attempting to convince the world the results are the same (within one standard deviation).
The best statistical models will tell us nothing about what is, but rather what could be given a fixed set of weighted variables assumed to represent the bulk of possibilities. Do you see the problem with that?
This is why I believe there is ample room for discussion on global warming as an issue to be dealt with in the public policy realm. The science, let alone statistics, of this field are very far from settled. We can only assume models are correct if the inputs are correct. However, just as with the stock market, modeling anything complex will continue to be a significant challenge.
Maybe I’m the one with a warped view of statistics. But it is dangerous ground to shape public policy based on statistical representations and forecasting of highly complex and evolving systems. We are now painfully aware (again) of what Nassim Taleb calls “black swans” – the improbable, statistically impractical, occurring. Our economy hit a so-called black swan event that only serves to remind us that the improbable still happens.
I like to illustrate this problem by using a simple analogy. The odds of hitting the lottery are extremely small. The odds of being struck by lightning are far better. But somehow people still manage to win the lottery. And I’m sure that given time, someone struck by lightning will also win the lottery.
We do recognize that these moments in time, place, and history are built on the improbable. Our planet rests in a narrow habitable zone around a star at a given point in its life. We appear to have a perfect mixture of elements bound together in a way that permits life to spring up. All of these things are tiny specks in the ways of time and space – highly improbable, but happening nonetheless. We are only now (relative to the evolution of man) beginning to realize that the probability of life outside of our solar system is likely much greater, the odds much better, than we have ever thought before.
Even within the context of evolution, we must admit that had there been very slight alterations in the mutation of DNA hundreds of thousands of years ago, we would likely not be sitting here discussing this very topic in our current form. If there were a God with statistics, his models may have predicted with a certain degree of precision that intelligent life would spring up somewhere, at some time, with blue eyes and blotches of hair on its body. He may have been convinced that we would all have six fingers and toes. While we may have fingers and toes, we do not have six of them.
We must recognize that statistical models are only mathematical guesses based on historical data. In making these guesses, we have to take a few shortcuts in order to arrive at some prediction. The more complex the model, the greater degree of uncertainty we have in the results of our calculation. Yet it seems with climate science we have somehow managed to step over the complexity question, accepted the model’s prediction and buried our heads in the sands of catastrophe.
Let us assume for a moment that temperatures will rise one or two degrees over the next century. Let us assume the catastrophic visions put forth by these scientists and followers are true – how can we truly know the full impact on the entire ecology of the planet? Temperatures in Africa might change to some degree as to suddenly kill off the plague of crop-destroying locusts. The land becomes fertile again, and suddenly a tiny change in the cycle of life in Africa becomes an exponential variable in the future of life here on earth. The longitudinal problem occurs because the actual weight of the entire system of input variables is entirely variable.
So it might seem reasonable for scientists to admit that one little thing they seemingly have a hard time admitting: they really don’t know what the future will hold. They can only look to the past and make an educated guess based on the relevant historical data they hold. The key is that it is a “guess”.
To test a CGWA (catastrophic global warming adherent), we only need to take their uncanny knowledge of the ecological future and re-apply their skills where it might be entirely possible to create a vast fortune. If climate scientists are capable of determining the future of the earth’s temperatures with such precision, ask yourself why they are not currently trading in the stock market. The complexity of knowledge and modeling are very similar. Ask yourself why they have not put their entire life savings into a proprietary model developed with their own modeling abilities with a single, long-term investment that can not be changed. Most of them would be quite successful – or should be given their dogmatic insistence on the viability of their modeling abilities. So, therein lies the challenge to the scientists: put your money where your mouth is. I’d lay wager that most of the CGWAs will, invariably cringe at the notion. They’ll stake their entire scientific careers on their work, but not their money.
