Geekonomics

Many people have been confused as the American economy has recently gone further down the drain and the cost for the wreckage keeps rising. At first, the leaders proposed a $700 billion bailout package. But recent estimates have put the final price tag closer to $7 trillion. That's $7,000,000,000,000.

Is the economy really in such bad and unknowable shape that the cost to repair it could be off by an order of magnitude or more, and that the final price tag might be more than the GDP of the entire galaxy?

Fear not: the experts know, as they always do, exactly what they're doing. They are using a monetary technique that comes from computer science called the "MAXINT Effect".

In computers, all numbers are represented in binary form and stored as 1s and 0s. Each number takes up a certain number of 'bits', or places that hold a one or a zero. For example, an short integer typically uses 16 bits of storage, which can store unsigned numbers of up to 65,535, or half of that if the number can be both negative and positive.

Since these numbers have only that storage space given to them, they can only reach that maximum value. For integers, the value is referred to as MAXINT. If you insist on adding more to the number, the bits will carry the one right past the end and it will be lost forever like socks in the dryer, or parental advice to kids. The number, meanwhile, will be essentially start over again.

For example, adding 1 to 65,535 in a 16-bit storage area will result in 0.

Let me say that again, because it's an important fact and one that lies at the heart of every economist's graduate degree: Adding 1 to a really big number can sometimes result in that number being equal to zero. These people go to incredibly boring classes for years to learn this, and now you know it.

Meanwhile, another important principle in computer science has important bearing on the problem. All numbers, and all information in general, is represented in computers in blocks of 8 bits, which is called a byte. It's not clear why this is, although historians suspect that the first computer scientists lacked opposable thumbs and therefore counted more easily to 8 than to 10.

Any number represented in a computer will, by convention and necessity, will have storage space equal to some multiple of 8 bits.

Now, let's go back to our economy and think about the problem again with our new-found geekonomic perspective.

The old bailout price tag was a measly $700 billion, or in binary form, 10100010 11111011 01000000 01011000 00000000, which is represented easily in 40 bits of storage. But if we push the price just past a trillion, or $1,099,511,527,776 to be exact, we will need 41 bits to represent the number, or 1 00000000 00000000 00000000 00000000 00000000.

Note that we just pushed past an 8-bit multiple, and now require 6 bytes to store the new number.

Now I don't know what your experience is, but I find that the government usually provides just enough to get by, without much left over for slack. And since we're talking about numbers here that are much larger than anyone conceived of before, it's likely that there was no need for such large storage for the numbers, and thus they simply found a number of bytes that would probably do the trick. That 5 bytes of storage had served our country well throughout our history, why change it now?

Economists realized this 5-byte limit. So when we were already talking about a debt of $700 billion, it was much easier to plan on spending more than that than it was on holding the costs down. And by spending just a wee bit more, just over a trillion, they could roll that debt number right over to zero.

So when the experts talk about $7 trillion, they're just putting the number so high that we'll miss the fact that as it passes $1 billion, our economic woes will be gone.

You may be thinking that this is new and radical thinking, but other computer techniques are used elsewhere in our economy. For example, companies that cannot maintain revenue, products, and basic business plans often see their stock dive. At a certain point, the executives realize that it is far easier to make the stock go below zero than it is to raise it up again. This strategy uses the inverse of the MAXINT effect, commonly called TNIXAM, causing the stock price to wrap around from zero to some absurdly high number.

TNIXAM works because stock prices cannot go negative. A negative stock price would mean that the company owed shareholders something, which is clearly not how business works. So if a company's stock price ever goes past zero, it will do so by wrapping around to a large positive number instead.

This technique is why you often see companies floundering as their stock price gets lower and lower. One would naturally think that the companies should change direction, leadership, products, marketing, or anything in a desperate attempt to start selling things again and get that stock price up. But it's far easier to just keep doing the same thing and drive the price right into the ground and back out the other side.

There is some who think that stock prices use the 'bounce' technique instead, which says that if they hit a price of $0 hard enough they will bounce up higher than before. But that theory is ridiculous and not based on any solid, numerical principles like TNIXAM.