By now some of you have seen my "Electric Money" show. Maybe you liked it, maybe you didn't. If you didn't like it, the reason was probably that the show seemed superficial — too much like, well, television. That's the problem with TV. We jazz it up to keep even the most casual viewers from falling asleep, but in the process leave out many details. So I'll give you a few of those here, though doing so holds the opposing risk of annoying the academics. That's a risk I'll just have to take.

I'm sure economists are very nice people, but having so far interviewed five Nobel laureates in the field, I have to tell you they are not laugh-a-minute fellows. And why would they be? After all, what they purport to do is to explain the apparently unexplainable, plumb the depths of unfathomability, to make sense of money. It sounds a bit thin compared to finding the cure for diseases or inventing those dimples that make golf balls fly farther, but I am sure it must have some value. Economists don't get a lot of respect from the other sciences primarily because physicists and chemists and even mathematicians aren't sure economics even is a science. Nobel didn't bother to come up with a prize for it, leaving that to the Bank of Sweden.

The problem with economics is that what it studies is not some natural system like DNA or igneous rock, but the economy. And the ugly truth about the economy is that it is all a game. We make it up, sometimes as we go along. There is no element called "dollar," no natural phenomenon called "interest." Economics is at best a series of behaviors, several of which are specifically proscribed in the Bible. Economics gets no respect even from God.

God didn't say there shall be a five-dollar bill, and Lincoln shall be upon it. We said that, just as we apparently decided that Bill Gates would be richer than me, though not nearly as cute. Yet we also pretend it is real when it isn't. Why is Bill Gates worth $60 billion and I'm not? Because somebody says so is why. That's pretty flimsy, yet it is the basis of a world financial system that's been running more or less successfully for hundreds of years.

If it is all a game, then why do we get so worked-up about money? Why do the mothers of Texas high school cheerleaders sometimes try to take out murder contracts on their daughters' rivals for the varsity squad? I don't know. It's an emotional thing. And now that we are several hundred years into this current version of the game called money, we pretend it has existed forever and even that it has some right to exist. But it doesn't. The Spanish, when they ruled much of the western world in the 16th century, didn't feel like their version of the money game had to go on forever. When things went poorly for the Spanish, they just repudiated their debts and started over. But these days we don't do things like that, insisting instead that the books at least appear to balance. I think it comes down to a fear of losing: If the game never ends, it is never lost.

To the purest of stuck-up scientists, then, the study of economics is right up there with the study of any other aspect of modern culture — like, say, bridge, or old episodes of "The Brady Bunch."

While they may disdain economics as a field of study, even the physicists with car loans are affected by it. That's because with the exception of certain Amazon tribes and Amish folks from Wayne County, Ohio, nearly all of us are stuck in the money game, 24 hours per day. And since we are stuck in the game all day, every day, we make a science of its study, or perhaps it is a religion. There are strong feelings about economics, strong enough to have split the world in two for half of the last century. So of course it is too complex and too sensitive a subject for an idiot like me to explain, right? Wrong. I'm going for it.

This is where I start to really annoy the experts.

There are two approaches to the study of economics — understanding its inner workings and predicting the outcome of those workings. These may look very similar, but they aren't. It is much easier to predict a behavior, for example, then to understand it. When it comes to economics, our real goal is nearly always to predict, not to understand, so it amazes me why we even try to do the latter. In many respects, I think it is just a habit held over from an earlier time when we had fewer tools for accurate prediction. Then we had to settle for what we hoped was understanding — rules of thumb that just might give us a better chance of guessing right in the absence of having a really big computer.

About 30 years ago, a part of my academic training was how to aggregate individual experiences into a theory, called extrapolation or inductive reasoning, how to apply a theory to an individual, called deductive reasoning, and how to derive the statistics so my reasoning was sound. The extrapolation of individual economic events to money markets is the field of finance. The deduction of major economic trends to the individual is economics, econometrics, stochastic modeling and a few other names that have been in vogue over the years. The intersection of finance and economics is chaos because while they deal with the flip sides of the same coin, there is no real intersection of the practical with the theoretical. They mostly say the same things in a different language, and therefore, no real communication takes place. That's when it is nice to have that really big computer.

We weren't in a position, back then, to apply much mathematical firepower to economic questions, so instead we tried to discern patterns and derive formulas from those patterns. Supply and demand, yield curves, money supplies, how many angels could dance on the head of a pin, ideas like those populated the blackboards of economics departments throughout the world. But what they didn't do, with any great certainty, was tell us when to sell AT&T or what was a good price for winter wheat.

To understand how we find the good price for winter wheat, let's first design a pair of eyeglasses. Optics is a science, even if economics isn't, and the science of optics was well understood by the mid-19th century. Choosing eyeglasses, then as now, was a subjective comparison of lens A versus lens B until the best fit was found. But defining mathematically the difference in curvature between A and B was very complex. Equations had to be written so that standardized lenses could be designed and built. Good glasses were expensive as a result.

So how do we design glasses today? Do we use our computers to run the calculations quicker, to derive the optical formulas to even greater levels of precision? No. We use a completely different method, one that would have been impossible to do in the old days but makes the best use of modern computers. Having measured the eye that needs correction, we determine the spot in the eye where we want light to focus. This is easy. It is also the outcome we want to predict. Then, working backwards, taking into account the index of refraction of the lens material, we plot a few thousand points of light coming through the lens, being bent or refracted by the lens material and landing right at the point we designated. What comes from the design computer are coordinates of where those points of light enter and exit the lens on their way to the eye. Carving the lens is just a matter of using the same computer to grind each of those points down to the right thickness on both faces of the lens blank. No optical formulas are required, just a few thousand simple calculations that would have taken weeks to do 50 years ago, and today, are done in less than a second. The computer not only designs the lens (predicting where the light will go through the lens), but it makes the lens, too. As a result, a modern optician wouldn't recognize an optical formula if he or she saw it.

Finding a good price for winter wheat is a similar task of making a few thousand calculations based on available market data to define the best price, which can vary depending on whether you are a buyer or a seller. Like the lens, the best price for wheat is a multidimensional path through the data field.

You don't have to try this at home, but what you do have to understand is that it is doable. Given good data with which to work, it is possible to predict economic behavior. Given near total command of data, it is possible to infer from economic events an accurate model of the economy. None of this should be surprising. After all, it has more to do with a game of Monopoly than most economists would like to admit. It's just that Monopoly comes with a printed set of rules that don't change unless your little sister cheats. In the real economic model, we have to guess the rules from their reflections and accept that from time to time the rules we define will just up and change on us.

The problem with all this prediction stuff isn't the calculations, but the data gathering. Computers are cheap and powerful, but clean data is scarce and expensive. The better the data, the better the information used to make upcoming decisions. Perfect data would be to know in advance what was coming and so react to events before they happened. And that's exactly what does take place. Markets typically react in anticipation of important news, somehow knowing whether to go up or down as a result. When J.P. Morgan died in 1913, the financial markets rose, but that was because they had already fallen the day before, reacting in advance of the great man's death.

This very assumption that the market has intelligence and that intelligence is close to perfect means all by itself that the economy is describable given enough good observers. That means accurate prediction is possible, too, no matter what the circumstances, though the quality of real world data hasn't typically been good enough to prove this. The very fact that the market has intelligence makes it, in itself, a giant analog computer. It just takes all the money in the world to keep the computer running.

Here is where we touch on the liquidity of money, which has always been a function of the information available about its alternative uses. When a bank in Glendive, Montana, made or didn't make a loan, it first assessed the borrowers' capabilities, but also assessed alternative uses for those limited funds in the context of Dawson County. That was the information that was available. Today, that same banker can place his funds anywhere in the world both because he has the tools and because he has the information.

Tools beget faster, broader information; information begets broader decision alternatives which means greater liquidity, that liquidity is represented by the newer, better tools as instantaneous information ad infinitum. For the individual to get the funds he needs, all he has to do is pay an economically reasonable return in relation to the fund-owners alternatives. Therefore, maybe, availability for the individual is less a function of begging George Johnson at the Exchange State Bank and more a simple economic decision of price.

So what do we do with this knowledge that the economic world can be modeled at least to the point of being predictable? Well, that's what my show is all about. The 1990s saw something change in the way our economy works, and I intend to explain that effect in different terms than you've probably heard before. This show deals with five historical concepts, taking each in turn and creating by the end a complete picture of how and why the world economy has changed in the last 50 years, and where it is going. Remember, none of this is by design. It just happened.

The first concept is automation. Automation is the application of information technology to doing actual work. In the case of financial systems, the actual work is just the shifting around of numbers to reflect recent transactions and the new state of accounts, whether it is for an individual or a nation. Automation brought computers to the bank, linked those computers together into networks, then created credit cards, debit cards, automatic teller machines, home and Internet banking. Automation, working with a telephone system that was also run by computers, created discount brokers and money market funds. Automation and communication are what allow money to travel the world as fast as it does. It is not just that automation makes these tasks cheaper or faster, but that it makes them possible at all. Automation is what makes this story happen.

The second concept is securitization, the creation of one financial instrument from many others. Mutual funds became popular in the 1960s not just because people were ready to start investing small, steady amounts in the stock market, but because the funds couldn't have operated at all without computers. There was simply no way without computers to calculate the value of a security composed of thousands of other securities in short enough time to make the mutual fund effectively tradable. So computers and automation made it possible for regular people to put $50 per week into an investment that represented a significant portion of the broader market. So, too, the securitization of home mortgages by companies like Fannie Mae and Ginnie Mae. Securitization — wrapping many little bundles of risk into one large bundle with a reduced level of risk, powered much of the 1960s and 1970s.

The third concept — hedging and the creation of synthetic securities — took reduction of risk to an even higher level. Starting with the work of Harry Markowitz and extending to Myron Scholes, the economy moved from creating master securities out of slave securities to creating so-called synthetic securities out of pieces of other securities. These products were crafted by brokers from a portfolio of securities borrowed from their customers. Leveraging a theorem from thermodynamics to reduce to zero the level of risk in an option was the beginning of a whole new industry that today reduces many types of financial risk for nearly all businesses (except mine).

The fourth concept is market liquidity, the stuff that oozes out of financial systems as a result of concepts one to three. With the reduction of risk and attraction of new investors to supply capital, that capital had to go somewhere. Somewhere proved to be the economy, itself, though it took a very, very long time to do so. If you think it is hard to borrow money now, imagine doing it 50 to 100 years ago, when a concentration of wealth made it possible for J.P. Morgan, himself, to raise enough money to finance almost any purchase, while our grandfathers couldn't borrow money unless they didn't need it. After a century, it is much easier to raise money than ever before and becoming easier still.

Finally, there is venture capital, which takes something that isn't even a financial instrument — it's just an idea in most cases — and turns that idea into an instrument of change. VCs come in to the risk picture so early that their returns are proportionally greater. Early capital drives early success or failure, which drives more capital into more companies with weird-sounding names. It accelerates change with no accompanying increase in risk exposure for the rest of us.

What's taking place here is a relentless and technologically-driven convergence on value — the real worth of some piece of property. Value was always thought to be subjective and highly dependent on context. In the desert, water is more valuable than it might be in a rain forest. And the determination of value over time is what makes angel investors happy and rich as the rest of the world comes to understand the underlying value of their little company. Now we've found ways to standardize, normalize, and control for the variation in context until true values can be both calculated and compared. That's the essence of what an efficient economy is about. It simply knows its business better than an inefficient economy, and we all benefit as a result through lower interest rates and great ease of qualifying for financing.

And that brings us to today, right at the end of the biggest economic expansion ever for our country and the world. This is a time when the productivity increases of automation, promised 50 years ago by the early computer experts, finally came to pass. And now we all have to vote with our wallets and decide how we feel about it.