The Crash Course - Chris Martenson [51]
1 Mathematicians and other scientists have a means of assessing how accurately an observed event conforms to, and therefore can be explained and even predicted by, a mathematical formula. For example, if you saw the number series 1, 2, 3, 4, 5, and 6, you would instinctively predict that the next number in the series would be 7. The way a mathematician would approach this would be to arrive at the number seven by converting the number series into a formula, y = × + 1, and then testing this formula against the number series using a statistical method that calculates the variance between observed and predicted results.
The term for this method is “goodness of fit,” which means just what it sounds like: How close of a fit was there between the measured variables and one’s formula? In this example, the formula perfectly matches the observed number series and because it does, it is said to have a perfect “fit,” which means it is assigned a value of “1.” An utter lack of “fit” would be assigned the value of “zero.” By convention, a perfect fit, as in our number example above, is said to have a value of 1.0, while a formula with a no descriptive or predictive power at all would have a “fit” of zero. In my science days investigating messy, real-world biological systems, a fit of 0.80 or better was a very good fit, meaning that a real and predictable (and therefore understandable) process was being studied and the scientists involved would get excited like hounds on a strong scent. But a “fit” of 0.90 or better? Practically unheard of for the systems I studied. Experimental noise and biological complexity conspired against such robust readings.
Now let’s imagine another system designed and run by biological creatures with enormous, nonlinear complexity built into it, which we seek to similarly describe and understand by “fitting” it with a mathematical formula. I’m speaking of a system composed of millions of moving parts arising from billions of individual decisions and totaling in the trillions of dollars. The entire system is in constant flux, with many of the parts interacting with each other in a delicate, chaotic symphony of positive and negative feedback loops. From a ground-up perspective, such a massive and complicated bit of machinery would seem to defy easy characterization and offer slim hopes for getting a good “fit.” The system I am speaking of is the entire, massive, complex credit market of the United States (although other countries would work equally well in this example), which is composed of every manner of type of debt you can imagine, spanning multiple decades with wars, recessions, booms, and bubbles interspersed along the way. Mortgages, derivatives, federal debt, auto loans, municipal debt, student loans, and dozens of other types of debt are all mashed into one, gigantic market. What’s your prediction for how well we can describe the growth in this market over the past 50 years? How good will our “fit” be? Will it be a horrid 0.50 or less, a respectable 0.65, or perhaps something higher? The answer surprised me enormously when I performed the test; our total credit markets are described by an exponential function with a “fit” of 0.9937 (!!). That’s as close to perfect as you can get without actually being perfect. It is powerful evidence that our credit markets operate exponentially.
2 Overnight money is also known as the Federal Funds Rate. When you read about the Federal Reserve raising or lowering the interest rate, it is this rate to which they are referring.
CHAPTER 12
Like a Moth to Flame
Our Destructive Tendency to Print
The twenty-teens will be marked by the collapse of sovereign debt. When the Great Credit Bubble first began to lurch about unsteadily in 2008 as the consumer withdrew, most governments of developed nations predictably turned to Keynesian stimulus to try