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The Red McKenna series chronicles the adventures of a six-foot, three-inch redhead with an athlete's body, a mathematical-genius mind, and an independent streak a mile wide.

Months ago, I promised to alert readers of this blog when my first full-length novel Red appeared. Well, it's out. Actually, it's been out for a while in hard cover, paperback, and e-book formats. It is available through online and brick-and-mortar booksellers. Published by iUniverse, the novel introduces a unique heroine whom I think readers of this blog could relate to. She's a six-foot, three-inch redhead with a mathematical genius mind, as well as a crack-athelete's body and an independent streak a mile wide. Her soul mate is a biker who's even bigger, smarter, and more independent. Together, they harness science and advanced technology to solve riddles that life throws at them.

The idea for the story started back in early 2001 on a bitterly cold January night in Woonsocket, Rhode Island. I'd just flown in from Arizona to spend a week emptying out and closing up the house of my recently deceased father, who'd finally succumbed to cancer at age 87.

When I say it was bitterly cold, I ain't just kiddin'. The high for the day was about zero Fahrenheit, which is cold even for New England in January. For a desert rat living in Arizona, it was unbelievable!

Then, the sun set, and it got colder.

I curled up in a lotus position with the thickest quilt I could find wrapped around me, hoping the furnace would soon drive away the chill that had seeped into the walls during two weeks of the house being empty. Since the house had been empty, there was no TV. I'd been cooped up on an airplane for hours with nothing to do but read, so I was read out. My body was still on Mountain Standard Time, and I'm a night owl, anyway, so sleep was many hours away.

I just sat, and thought.

What I thought was the beginning of this story. It was going to be the adventures of two young people who made a transcontinental journey by motorcycle, visiting all the places I liked to go by motorcycle, doing the things I like to do when touring by motorcycle, and meeting the kinds of people I meet when wandering around by motorcycle.

To make it interesting, I'd have the lady be a newbie biker, who'd never been on a motorcycle tour before. Everything would be new to her, and a surprise.

What would she look like? Well, I like tall redheads who are really, really smart. My mother was tall, had auburn hair, and was one of the smartest people I've ever met. My wife is tall, has red hair, and is no slouch between the ears, either. In fact, I'm a sucker for tall redheads with lots of brains. So, my heroine would be tall, have red hair, and be really, really smart.

Since everything in an exciting fiction story must be bigger than life, she'd have to be extremely tall - like six-foot, three-inches tall - have lots of flaming red hair, and be a genius with a full scholarship in an Ivy League college studying something that gives most people phobias: mathematics.

The guy would be a veteran biker, who knew all the right places to go, and could introduce her to the most interesting people. To be able to match her, he'd have to be really tall - like six-foot, six-inches tall - more athletic, and even smarter.

They'd visit motorcycle races, camp out at biker rallies, spend hours shopping at motorcycle flea markets, and spend evenings getting plastered at biker bars. Being really, really smart would give them the wherewithal to thumb their noses at convention whenever they wanted to. They could get into stuff the rest of us only fantasize about.

It'd be a lot of fun for them, and, maybe, for readers.

In that form, however, it'd be lucky to make fifty pages long. That's a longish short story, not a novel. A novel needs a lot more. It needs character development. It needs suspense. It needs mystery.

It needed a lot of work.

Over the next nine years, the story grew. The young lady got a name, Judith McKenna (nicknamed "Red" for obvious reasons), as well as a troubled past. Her troubles, however, were not her fault, and not the fault of any character flaw. The troubles stemmed from a singular event that made building relationships difficult at best, especially building relationships with guys. That event was the untimely and mysterious disappearance of her father just at the time an adolescent girl needs a father figure most.

So, the father figure would be supplied by the mysterious biker, who takes her on a journey, which is no longer a touristy vacation, but a journey of self-discovery. Who was she, inside? How could she relate to other people? What was she going to do with her life?

One of the ambiguities she'd have to resolve could be a bit of sexual confusion. That could be fun!

The mystery, of course, is what happened to her father. Why'd he leave? Why'd he not come back?

Now, my favorite fiction genres over the past lots-and-lots-of-decades have been mystery and science fiction. And, my favorite stories have always combined both. And, my favorite author has been Rober Heinlein, who generally combined those two genres and used them to weave epic tales that explored basic human values. That's what I'd try to do.

Judith's story had a mystery, and had some serious character-development potential. It also had two young people off on their own, providing plenty of opportunities for fooling around between sheets, which will seriously spice up any story. In fact, giving her a chance to peel back layers to slowly discover who this biker was would add a second mystery, which might be fun to develop as well.

What she would find is a scientific genius who could provide technology that would make solving her other mystery - what happened to her father - possible, where it hadn't been before. He'd have built his own company in very short time, capitalizing on his inventions in aerospace technology. I know about aerospace technology. I can do that.

With all that additional content packed in, the space needed to tell the story expanded tenfold. When I finally sat down to type it out, it took a year instead of the three-to-six months I envisioned. From a simple little story about a motorcycle trip, it grew to an epic adventure.

By the way, it's still growing, with new titles coming soon. My wife says she likes the sequel even better.

I think you'll like it, too.

Imagine a beautiful photo of the St. Louis arch as seen from mid-channel on the Mississippi River. That's what we should have here. Bonnie shot the thing, which looks great postage-stamp size on her cellphone, but we haven't figured out how to tranfer it to my computer, so it isn't here.

It's been a couple of days since I last posted partly because so much has been going on that I haven't had a chance to sit down and post. This is just a short synopsis to catch up.

Let's see. The last time I posted was Thursday, and we'd just settled down in Pekin, Ill. The folks at the Pekin Boat Club were fantastic! They bent over backwards to make sure we got in, and had all the services we needed. They're not set up for boats as large as Damifino!, but managed to make do. DR, in particular, took us under his wing. He chauffeured Bonnie around to the laundry in town, and the grocery store. He found me a couple of mechanics to consult with, and even scouted up a 5/8" deep socket to use instead of my missing spark plug wrench (plugs looked good, by the way).

Once again, the points had closed up just enough to cause trouble on the port engine. On DR's recommendation, we headed to National Marine on Upper Peoria Lake (the wrong direction for the trip, but the right direction to get competent help. The mechanic there, George, looked the situation over and said: "Nobody ever lubricated the distributor cam!"

That was why the points gap kept closing -- the rubbing block (cam follower) was wearing down rapidly, so every time we set the points, the rubbing block wore down and let them close up again. I always lubricate points when replacing them, but I hadn't replaced them, just reset them. George lubricated and set the points on the port engine, and, at my request, the starboard engine. So far (fingers tightly crossed), there's been no further trouble.

Friday we found out about the long stretch of the Illinois River without fuel services. With just an eighth of a tank left, I called a halt, and pulled over to the right descending bank at a ferry crossing.

"You can't tie up here!" one of the attendants shouted, echoing the big sign that said: "Don't tie up here!"

I told him we were running out of fuel and needed to call for help. That changed his tune, and he became very helpful, as just about everyone on the river has been.

Of course, just as we started trying to call for help, the thunderstorms started up, and cellphone coverage became nil. The folks at BoatUS connected us up with Mel of Mel's Riverfront Restaurant, just a few miles downstream. Mel had a floating dock we could tie up to, and offered to give me a ride into town to buy gas. Unfortunately, the causeway from the float to the shore was under three feet of water due to flooding.

Did I mention that the entire river system was 25 ft above normal? I should mention that. It's important.

To make a long story short, by the time I got fuel on board (wading across the flooded causeway with six gallon jerry cans full of gasoline), dusk was falling the rain was picking up, and we should have just stayed there.

But we didn't. I made an executive decision to push on to Alton.

Needless to say, about a half hour later, with the fuel gauge on "E," and darkness well and truly descended, I fired up the VHF radio, and issued my first "Mayday" call. The Coast Guard guy suggested that we anchor out of the channel (Where's the d**n channel? Where's the d**n shore? Those trees look awfully close in the searchlight beam!). Coast Guard suggestions are, like those of cops everywhere, more in the nature of commands -- if you don't follow them, you're asking for trouble. So, we anchored (also for the first time since I was 15) and waited for the Conservation Cops from the Sheriff's Department to tow us into Alton Marina.

At Alton Marina -- 2:00 am -- the starboard engine quit, and wouldn't refire. Not even a click, when I hit the starter! I guess it had gotten jealous of all the attention the port engine had been getting, and wanted its share.

Alton Marina is a beautiful spot, which we stayed at through Saturday just to sort everything out. They loaned us a courtesy car to go on a snipe hunt for a starter solenoid. The service manager at the Bayliner dealership, too far upstream to be of any help other than for information, helped me locate the starter solenoid, and explained that it was a standard automotive part, so I could get a replacement at any autoparts store.

So, Sunday morning, fully refueled, restocked, and revitalized, we pushed off out of the Illinois River and into the Mississippi. We got as far as the first lock, when the lock master mentioned in passing: "By the way, have you gotten permission from the Coast Guard to go downstream?"

"I didn't know we needed permission."

"They've closed the section of river below the next lock because of wreckage in the water."

We don't like the sound of "wreckage in the water."

So, we got a phone number for the Coast Guard from the lock master, and called. Rod Wurgler took down all our particulars, and said to stand by. He'd see if we could get permission.

Some time later, he returned the call and said his supervisor put us off until 10:00 am the next (Monday) morning. We were staring at Alton Marina, just under the bridge upstream of the locks, so we called them and arranged for a slip for the night.

Alton Marina is, perhaps, the best run operation we've seen so far. Beautiful covered slips, showers, clean bathrooms, etc. After lunch at their cafe, we met Grandpa Bob, who took me on a snipe hunt for auxiliary fuel containers and a means of lashing them to the deck. It took a couple of hours, but I came home with means to increase our fuel capacity by 30 gal.

Ten o'clock Monday morning came and went. Not having great confidence in our chances, I'd gotten to work on a couple of projects that needed doing -- expecially bolting down that microwave we'd had to pick up at the last minute when the built-in unit that came with the boat went up in smoke the day before we left.

When the job was finished -- about 10:30 am -- I called Wurgler. He hadn't heard, but promised to check and call back. Sure enough, a few minutes later, he called back with our clearance. It took about an hour to rig for sea, then we were off.

The next stop was Hoppie's Marine, about mile 158 on the Mississippi. It is a de rigeur fuel stop because it's 107 miles to the next possible fuel stop after that. Gotta fill up there, or we'll be calling the Coast Guard again!

At Hoppie's a nice little old lady named Fern sat down with us for about a half hour to disgorge all the information she had available about navigating the Upper Mississippi River. Between her wisdom, a few very thick and very expensive books on the area, as well as all the navigational charts available, we've come up with a plan. I'll explain it next time, and tell you whether it worked.

At least, after a week of trying, we've escaped the clutches of Illinois -- for now.

Author C.G. Masi's forthcoming novel looks at how technology developers go about their business in a corporate environment.

Many thanks to the loyal readers of this blog, who have put up with a low posting frequency over the past few months. My excuse is that I've been trying to get my next book into production. It's nearly there, so I should be able to provide more frequent posts to this blog.

Readers who enjoy my commentaries on how technological advances affect current events will have a lot to interest them in the book, which should be in bookstores around mid-summer. Entitled Red, it is a novel whose main characters work in a private applied-physics research company. The title comes from the nickname for the central character, Judith McKenna, who is a tall, athletic, young mathematician, who tosses everything away to search for her missing father after the authorities have exhausted all conventional means of finding him. Her faltering quest is saved by Doc, her mentor and sometime lover, who shows her how to organize the scientific and technical resources she didn't even realize were available to solve the mystery.

To reach her goal, she needs to learn techniques of organization, resource allocation, team building, and decision making under uncertain conditions. If you thought such issues were dry and academic, it's because you haven't seen them played out in the emotionally charged, risk-filled environments where real-life technology developers live and work, where millions of dollars, careers, and even lives are often at stake, and any mistake can lead to disaster.

If you think that's hyperbole, take a look at what's happening right now in the Gulf of Mexico.

We're now doing the final polish edit on Red. The schedule calls for that to be done before the end of June, at which time the book will go directly into production.

Most of the work is now in the hands of others, so I will have more time to devote to looking at how technology interacts with society, which is the focus of this blog. I plan to start by sorting through the issues surrounding the Gulf oil disaster. What actually happened? Who should really be pointing fingers at whom? Are the actions contemplated by the Obama Administration likely to help the situation, or make it worse?

Hopefully, I can help make sense of it all.

Graphing historic DJIA prices on a semi-log plot shows that our financial markets experience a price wave with a 20 year period superposed on a steady long-term growth trend.

Yesterday was the first day that the Dow Jones Industrial Average (DJIA) managed to close above 11,000 in a long time. It had been flirting with that level for almost a week, now, and had crossed that level several times intraday, but never held it through the close of trading.

The media, of course, made a significant bit of noise about it - enough that my wife asked me, after reading the headlines in the local newspaper, whether it really was a good thing. Now, my lady is quite bright (she's working on her second Master's degree), but, as a humanities major, her long suit is not the kind of quantitative analysis necessary to interpret what moves in various economic metrics, such as the DJIA, mean to actual people trying to get by.

"Is the Dow over 11,000 a good thing?" she asked.

"Yes, but it doesn't really mean much," I replied. "I predicted it'd spike over 11,000 a month or so ago, then slide back. But, things are pretty much on track."

Analysis I did last fall (see image above) indicates that the DJIA is just about exactly on its long-term track. It should be just peeking above 10,000 right about now. Since we've just experienced a short spike down (You do remember we've experienced a recession over the last year and a half, don't you?). We can expect an overshoot on the recovery, then settling back to the long term trend modified by a chaotic wave with a period of about 20 years.

In the future, we can expect to see a slow rise with a long term trend of zero to a few percent for about the next five years. The trend should steepen thereafter, reaching a maximum about 2020. In the meantime, expect the DJIA to be in a trading range between 9,000 and 11,000.

The important thing to understand is that the huge price swings that many of us capitalized on over the past 18 months are unlikely to repeat, barring exigencies. Since stock traders make money by cleverly exploiting stock volatility, they won't do quite as well as they have over the past 20 years. Expect the real money to be made by investing in dividend-paying stocks. Expect portfolio returns in the 5-15% per annum range to be the norm. A good model for this investing environment would be the rather boring period from about 1965 through 1980, when the DJIA stayed essentially flat, with only short term ups, and downs.

Sorry, folks.

Signs of global warming

A flurry (pun intended) of articles in today's issue of The Wall Street Journal prompted me to drop another post about the controversy surrounding climate change research and efforts to curb global warming. Readers who have followed my posts here and in the Ask Charlie blog I wrote for Control Engineering know that I'm no fan of the IPCC report upon which most of the current nonsense is based. It's not that I think that there's anything wrong with the basic thesis that dumping loads of carbon dioxide into the atmosphere will likely ratchet up global temperatures, my problem is that so much of the so-called research, and especially the conclusions drawn therefrom, are prima facie so much politically motivated dreck (or to use the proper Yiddish spelling drek).

As I see it, there are two basic problems. First, the conclusions are based on a sophmoric physical model. Second, who ever said that higher global temperatures would be a bad thing, anyway?

The theory of global warming is based on a simple physical model - the greenhouse model - which is, in turn, based on the solid physics of radiative heat transfer. Specifically, it starts with the observation that the opacity of most atmospheric gasses is wavelength dependent. That is, while most of these gasses appear transparent to visible light, they are more opaque (sometimes very opaque) to infrared wavelengths.

So, the radiative power flux of sunlight, a large fraction of which comes at visible wavelengths, gets through the atmosphere to warm the ground. The warm ground tries to radiate that power back out at lower wavelengths (basically, the color temperature of sunlight is about 6,000 K, while that of radiation from the ground is about 300 K). The infrared, however, is absorbed by the dense lower atmosphere. Ergo, the ground and lower atmosphere, which are roughly in thermal equilibrium, get warmer. Increasing the density of the more infrared-absorbtive gasses, especially carbon dioxide, (so the theory goes) will necessarily increase the infrared absorbtion, and lead to higher temperatures.

We teach this model as an example in second-semester freshman physics. It's simple, easy to understand, and illustrates the mathematics of radiative heat transfer (which is what we're trying to do in freshman physics). The only problem is that the model is dead wrong. The real world is vastly more complicated. The difference is so extreme that any conclusions drawn from the greenhouse model are unlikely to correspond to anything in the real world.

One of the biggest problems is that meteorologists have known for decades that the weather system is chaotic. Weather patterns cannot be reliably predicted for a time scale longer than about a week. Weather, of course, is critical to radiative heat transfer, so asking a climate model that uses radiative heat transfer to predict anything beyond about a week is simply stupid. Other parts of the climate system are similarly chaotic, such as solar flux variability, making the prediction of future climate via computer models an exercise in futility. It is of academic interest, but of academic interest only.

Moving on to the second problem, who says global warming is a bad thing, anyway? The medieval warm period (look it up) ushered in an age of prosperity, cultural advancement, and generally really good times. It was followed by the the Little Ice Age, which brought with it famine, plague, and death. Who th' heck wants that?

Lessons from history, and prehistory uniformly lead to the syllogism:

cooler = bad;

warmer = good.

You do the math.

Business cycles are driven by a macroeconomic feedback mechanism that has a multi-year cycle time. Employment is one of the last economic metrics to show recovery because the process starts with unmet demand for goods and services, and only ends with jobs.

In every economic downturn, Chicken-Little pundits squawk about how we can't have a sustainable recovery until employment figures show improvement. Any investor, and here I use the word "investor" in its broadest sense to include those who put resources to work, not just those who invest in stocks and bonds, who listens to this drivel is destined to fail, and fail disasterously.

Macroeconomics - the study of large-scale economic trends affecting an economy as a whole - is based feedback loops that drive business activity. These loops describe causal relationships between economic factors affecting business. For example, an increase in production levels generally pushes employment up. Each of these causal relationships involve a time delay. So, when production levels increase, especially from a depressed level, employment does not rise until production levels exceed capacity at the current employment level. This takes time, as does the process of hiring new employees.

These delays are what cause business cycles in the first place. If we use, say, buggywhip manufacture as a hypothetical example, we might say that it takes 18 months for the buggywhip business to respond to a sudden change in the overall demand for buggywhips. So, if New York City should pass a law banning motorized vehicles, so all the Yellow Cabs in the city had to be replaced by horse-drawn surries overnight, that would ratchet up demand for buggywhips. Because it takes 18 months for buggywhip manufacturers to respond, actual sales of buggywhips would not stabilize at a level reflecting the new demand until a year and a half later.

Business cycles occur because it is not possible for businesses to precisely meet demand. In the buggywhip example, assume that there are two buggywhip manufacturers in business at the time the New York law passes. They will both attempt to grab more than their fair share of the enormous new market. Part of driving sales is assuring customers that you can actually deliver the goods ordered. So, both manufacturers will expand production faster than necessary to just meet demand. In addition, during that first 18 months, it will be clear that the established manufacturers won't be able to meet demand. Outside entrepreneurs will see this as an opportunity to jump in to the expanding market, by starting rival buggywhip manufacturing operations.

The result is that some 18 months after the new law passes, worldwide buggywhip manufacturing capacity will greatly exceed demand. Inventories of unsold buggywhips will expand. Buggywhip prices will fall. Marginal buggywhip manufacturers will fail. Buggywhip production capacity will drop. By three years into the process, we'd be back to having inadequate production capacity to meet demand, and the whole thing would start over again.

Boom and bust cycles like that are not some aberration, or the result of faulty business strategies, or some market inefficiency that politicians can erase by passing laws, it's how things inevitably work. In fact, most complex systems, such as economies, consist of multiple such cycles that operate on multiple time scales. Basically, they're all chaotic systems, which is why long term charts of practically every economic indicator - from long-term jobs trends to prices for individual stocks - look like profiles of the Andes Mountains. They're all fractals, which is the pattern most often associated with chaotic systems.

Economic expansions, recessions, depressions, and recoveries are actually just business-cycle components. As any Taoist sage could tell you, whenever the economy is expanding, you know that a contraction is on its way. Similarly, a depression always presages a recovery. It's inevitable. The Great Depression of the 1930s was, when looked at from a longer perspective, just a particularly deep bottom of the overall business cycle. The huge expansion we experienced during the 1990s was, conversely, a particularly robust phase of the overall business cycle.

This latest contraction, which started about 2005, and will probably not completely play out until 2015, was another particularly nasty dip in the more or less regular cycle. It's as inevitable as the tide.

So, getting back to jobs data, and the usual panicky predictions of a so-called "jobless recovery," the reason employment data have not significantly improved is that it's just too early in the process for it to show up. Those who ask: "How can sales recover when employment is down?" simply don't understand how the business cycle works. Sales aren't driven by jobs, it's the other way around, with a significant time lag between.

Jobs are driven by production requirements. As any industrial engineer could tell you, production is driven by inventories, not by demand. Demand is an intangible that is very difficult to predict or measure. Inventory levels, on the other hand, are easily measured and better reflect a company's ability to sell the products it makes.

In the real business world, the first thing to recover after a recession is demand. It begins to recover when end users have had their belts cinched so tight for so long, that they have no choice but to by new stuff. Demand for food starts to rise, for example, when pantries start to look bare. It makes no difference whether the family bread-winner has a job or not, when there's nothing for dinner, somebody makes a run to the store. Even if you have to beg a cup of sugar from the neighbors, that sends the neighbors off to the store for more sugar, increasing the demand for sugar. Therein lies the disconnect between jobs and demand.

Demand seems to have hit bottom about six months ago. Since then, we've been working off inventory that built up at the start of the downturn, when production still exceeded demand. Next, production has to rise (pulled by further increases in demand) until it exceeds capacity at the present depressed employment levels. Only then will employment figures begin to rise.

Don't look for employment metrics to turn up until at least the end of the first quarter 2010. The reason it hasn't happened yet is that it's just too darn early.

The 11 year solar magnetic cycle is associated with the natural waxing and waning of solar activity. On longer time scales, the sun has shown considerable variability, including the long Maunder Minimum when almost no sunspots were observed, the less severe Dalton Minimum, and increased sunspot activity during the last fifty years, known as the Modern Maximum. Source: Wikipedia. This figure was prepared by Robert A. Rohde and is part of the Global Warming Art project.

During the 1970s, I conducted an (unpublished) meta-analysis of data Charles Greeley Abbot collected from various sources in the early 20th Century to look for cross correlations between his solar irradiance measurements, sunspot index measurements, and weather patterns in various cities. The meta-analysis showed a significant positive correlation between solar irradiance and sunspot data, and a partial correlation between them and the temperature data.

Abbot, like nearly all astronomers and astrophysicists of his time, firmly believed in a negative correlation between sunspot index and solar irradiance, rather than the positive one his data showed. He noted the partial correlation between sunspot index and temperatures, but his prejudice about the correlation between index and irradiance led him to reject the effect as spurious.

By the end of the 1980s, the positive correlation between solar irradiance variations and sunspot index variations had been confirmed by satellite measurements, overturning astrophysicists' previous view. This allowed partial explanation of historically observed climatic variations, specifically the so-called "Little Ice Age" in the latter half of the second millennium, by reduction of solar activity observed through anomalies in the sunspot index, specifically the Sporer, Maunder, and Dalton minima. This research strongly indicates that solar variability is also an important input to the climate system that is certainly not under human control.

Now, it is becoming clear that the climate system is highly complex, with multiple positive and negative feedback loops, as well as a large number of independent forcing inputs, only a few of which are under human control (see "Aerosols Cloud Climate Picture," Science News, v. 176, n. 11., pp. 5-6 for a brief synopsis). These are characteristics of a chaotic system

Paleontologists and geologists have pieced together a fairly complete, though not necessarily detailed, picture of Earth's climate over the 4.5 billion years of the planet's existence. This picture shows a chaotic climate capable of varying over a wide temperature range. On short time scales, weather patterns are now acknowledged to be chaotic, with a horizon of predictability on the order of a week.

Taken together, these bits of information lead one to the conclusion that Earth's climate exhibits chaotic behavior on all time scales. It is, basically, a chaotic system.

Now, let's look at efforts to control climate change. We are attempting to use a chaotic system (global politics) to harness a second chaotic system (social, economic, and technical institutions) to control a third chaotic system (Earth's climate), when not all the forcing variables (e.g., solar irradiance, geology) are in our hands, anyway.

This sounds like a fool's errand.

I suggest that we could much more effectively apply our energies to developing means to react to climate change that is inevitable, than to the fool's errand of trying to direct it. Climate change, in any direction, has both positive and negative affects. It would be far better to direct our efforts toward engineering social systems, laws, and technologies to take advantage of the positive effects, and ameliorate the negative effects.

The Mandelbrot set is the most famous illustration of a fractal in two dimensions. The same mathematics can be used in one dimension to describe financial market price activity. Source: Wikipedia

The other day I caught part of an interview with a spokesperson for Elliott Wave International on Bloomberg TV. Elliott Wave International is an organization devoted to promoting the use of a market-analysis technique developed in the 1930s by Ralph Nelson Elliott, called the wave principle. The most important thing to come out of the interview, to my mind, was an off-hand comment by the spokesperson that stock-price movement "is a fractal."

A fractal is a mathematical waveform with a texture that is of the same form at all spatial-frequency scales. For example, we've all seen those neat illustrations hung in hotel lobbies and meeting rooms that include what appear to be strikingly realistic silhouettes of mountain ranges. Artists make those illustrations by simply tearing a piece of construction paper, and airbrushing past the torn edge. Since construction-paper tears and mountain-range silhouettes are both fractals, the torn paper edge bears an uncanny resemblance to a generic mountain range.

The most famous fractal illustration, and the one mathematicians use most often to illustrate the properties of fractals, is the Mandelbrot set. I think people like to use it as an illustration because it's amazingly beautiful. It also has the advantage of clearly illustrating fractal properties. Unfortunately, it has no practical value, so using it to illustrate fractals leaves the impression that fractals have no practical value.

Nothing could be further from the truth.

Like all fractals, the Mandelbrot set repeats itself on all length scales. It thus displays the property of self similarity. Similarly, if you take a typical stock-price chart covering a time span of, say, one month, and compare it to an intraday chart, the two will look remarkably similar, and will also bear a striking resemblance to the mountain-range silhouette, and the torn paper. They're all fractals.

Self-similar charts, such as stock price charts, all have two spatial frequency properties: they're combinations of "tones" at a wide range of spatial frequencies, and the amplitude of any one tone is inversely proportional to its spatial frequency. Tones at lower spatial frequencies - so they take up more of the chart horizontally - also have proportionally higher amplitudes - so they take up more of the chart vertically.

Comparison of stock-price charts covering a wide range of time spans shows that they also show self similarity, and are therefore fractals. Financial markets exhibit this behavior because they are chaotic systems. That is, there are a large number of independent actors making buy/sell decisions based on different interpretations of the same enormous, but incomplete, data set. Each actor moves the price of an individual financial instrument slightly, but none has enough pricing power to exercise significant control alone. Everyone has an effect, but nobody has a controlling effect.

That is what drives fundamental stock analysts bonkers. Each property of the underlying company, each news item about the company, each pronouncement by a politician, has an affect - often a noticeable effect - but none of them has a determining effect. There's just too much else going on that also has an effect.

It is important to recognize that there are varying degrees of self-similarity. The Mandelbrot set displays the strongest type of self-similarity (exact self-similarity), meaning that it appears exactly the same at all spatial scales. Stock prices, on the other hand, display the weakest type of self-similarity (statistical self-similarity), meaning that the figure has numerical or statistical measures that are preserved over different spatial scales. The fact that stock charts have only statistical self-similarity is what makes stock-price movements inherently unpredictable.

Because Elliott wave analysis starts from the observation that charts of financial-market prices are fractals, the wave principle, out of all technical analysis tools, has the best hope of making sense of financial-market price movements. Elliott's original work was hampered by the fact that understanding of fractals was limited in the 1930s, and understanding of chaotic systems was effectively nil until the 1960s. Some misunderstandings, such as the representation of waves as triangular, rather than sinusoidal, still persist, but anyone interested in learning technical analysis could do a lot worse than beginning with Elliott wave analysis.

Figure 1: Financial markets exhibit variations at all time scales with amplitude proportional to period. Based on data from stockcharts.com.

Yesterday kicked off the first day of the NIWeek 2009. This is the annual National Instruments user group meeting. NI to followers of this company, makes hardware and software for data acquisition and control applications. For general engineering and research laboratory software, the company's LabVIEW product enjoys a dominant position. The way the company has built and maintains this position is by working hard to anticipate major technology trends that will affect their users, and what their executives learn from this ongoing exercise comes out in comments during keynote speeches at NIWeek.

During NI Chairman James Truchard's opening comments this year, he expressed the opinion that some of the economic difficulties we have experienced globally over the past year can be traced to inappropriate use of mathematics in finance

"... Mathematics, the way it has been taught in business schools the last few years," he said, "has a serious problem ..."

Dr T, as he is known to his friends in the industry, also trotted out the oft-quoted comment by Warren Buffet: "It seems like the higher mathematics, with more false precision, should help you, but it doesn't."

This reminded me of a couple of themes I've been studying over the past few years chaos and fuzzy logic as they apply to things in general, and financial markets in particular. These are surprisingly easy to understand, easy to explain, and universal in application.

Chaos has bad connotations in western philosophy, which guides most of what we do for better or worse. In western thought, chaos is the enemy of order, and one of western civilization's stated goals is maintaining order. Eastern philosophy, on the other hand, seeks to balance the "forces" of yin and yang. These are complex concepts, but the important point for this discussion is that, among other things, yin is an order-seeking force, and yang is a chaos-seeking force. The theory is that both yin and yang are necessary for good health, and specifically must be maintained in an dynamic balance.

Mathematically, chaotic systems can most easily recognized by being in constant motion with cycles represented at all time scales, and the amplitudes of these motions are roughly proportional to their periods. Thus, weekly fluctuations tend to have an amplitude 5-10 times larger than daily fluctuations, with the largest amplitude movements appearing on decades-long time scales.

What makes chaos important is that it can be shown to govern the operation of virtually every complex system, from ocean waves to all human history. If it's a large dynamic system with lots of independently moving parts, its behavior most likely is chaotic.

Fuzzy logic, on the other hand, seems unmathematical for a different reason. The term sounds like a synonym for "fuzzy thinking," which people associate with foolishness and ignorance. Mathematically, however, fuzzy logic is a way of dealing logically with things that cannot be precisely quantified.

Humans, in fact, make nearly all personal decisions - especially life-and-death decisions - using fuzzy logic.

When you merge your car into a busy traffic lane, for example, you don't get out a radar gun to precisely measure the speeds of vehicles already in the lane, then analyze video images of the spacings between cars, then do a calculation based on the measured data plus known performance of your vehicle at various settings of throttle and steering controls. You don't have the equipment available to make the measurements, and you wouldn't have time available to make the calculations if you did.

Instead, your brain converts sense impressions and memories from previous encounters into more-or-less broad categories. Spaces between other cars, for example, are "tight," "okay," or "really big." Their speeds are "slow," "medium," or "fast." Your car "accelerates fast," "moves out okay," or "handles like a dead whale." Instead of a dozens of variables with 12-bit precision, your brain reduces the data set to a handful of variables with at most two-bit precision. It also doesn't use arithmetic to work out the answer, but a fuzzy logic algorithm that resembles a decision tree. The answer comes back as "it's safe - go for it;" "gee, it's borderline," or "that looks like a trip to the emergency ward." The final fuzzy calculation says "no" in the last instance, causes you to look further in the middle instance, and spurs you into action in the first instance.

That's fuzzy logic. It's the fastest and most reliable way to make difficult decisions when time is limited (as it always is). It's also virtually the only way to deal with chaotic systems, which, as I said above, almost all real systems are.

Probably (in a fuzzy-logic sense) the way to solve the problem Truchard and Buffet identified, is by mentally taking chaos and fuzzy logic out of the "advanced mathematics" cubbyhole, and teach it to high-school freshman. If we did that, we'd quickly have a generation with the mental tools needed to analyze and deal with the real world, rather than a hypothetical world of mathematical abstractions.

As with so many terms bandied about in mass media, "Smart Grid" is a cutesy umbrella term that allows politicians, analysts, and newscasters to vaguely refer to a collection of technologies that neither they nor their audiences fully comprehend, with advantages that are easily stated, and of uncertain measurability.

While that sounds pretty negative, let me point out that nothing in the above paragraph says anything against the technologies themselves, or their value, but merely pans vague marketspeak terms in general, and the folks who rely on them for ... anything.

Smart grids are part of a general technology trend toward incorporating embedded microcontrollers and data-communication capabilities into all sorts of previously existing devices. For those unfamiliar with them, a "microcontroller" is an integrated circuit that includes a microprocessor and peripheral circuits that allow the microprocessor to sense conditions and events in the external world (data acquisition) and put out signals to drive actuators in the external world (control).

Perhaps the first "smart" devices were automobile engines, which came under microprocessor control during the late 1970s, long before the term "smart xxx" became current. Such engine control modules (ECMs) sensed such variables as outside air temperature and throttle position, and used that information to control such parameters as fuel/air ratio and spark timing. Later, ECMs gained the ability to communicate with additional embedded microcontrollers managing such functions as anti-lock braking systems (ABS) and alarm systems. Modern automobiles now contain dozens of networked microcontrollers operating nearly all functions.

Today, most significant appliances operate under guidance of microcontrollers. Microwave ovens, dishwashers, clothes dryers, televisions, and home thermostats are familiar examples. The extent to which manufacturing operations rely on "smart" technology is even more profound.

Electricity generation and distribution networks, however, are far behind other industries in incorporating smart technology. That is the impetus behind all of the noise and fury about "Smart Grids" in the media.

To be fair, there are significant barriers to incorporating smart technology into electric-power infrastructure. Most significantly, it is imperative to keep the system operating reliably while applying new technology to it. Second, the cost of upgrading existing equipment that was never intended to be part of a computer-integrated system is, shall we say, large. There are many additional issues to be considered when making the move to smart utility grids.

The motivation to incorporate computer control and networking technology into the electric power system is not just to make it more "modern." The concept avoids Scheiber's Rule (Just because you can doesn't mean you should.) by solving a number of present and future problems arising from electric-utility development trends.

The first issue is the fact that the present distribution grid developed from early systems where a single generating plant distributed power to an isolated netword of loads. That placed the responsibility for maintaining voltage, frequency, and phase of the provided electricity squarely on one generating facility. Such installations are amenable to simple closed-loop control.

Later, but still quite some time ago, outputs from multiple generating plants were combined to supply power to the user network. That created the issue of coordinating the output levels and phases of the sources. At least, the sources on a given network were controlled by a common authority capable of centrally guiding the generators via more complex closed-loop control.

Problems became serious when power-distribution networks were interconnected to allow power sharing between sources operated by separate authorities. This makes simple reactive closed-loop control problematic. When you have multiple agents independently providing control inputs in response to observed conditions, the system becomes chaotic. This is not a slam on the engineers who designed and operated the system. It's a fact of life dictated by mathematics. Voltage variations, unpredictable frequency and phase shifts, and seemingly random catastrophic failures ensue.

Happily, all the folks on the supply side of the system were highly intelligent professionals who realized that the only solution was to co-operate their power-generation controls. We'll call it meta-control, where individual operators don't blindly react to every movement of the controlled system, which is what drives the system into chaotic behavior. Instead, when they observe a departure from nominal status, they first communicate among themselves, and devise a coordinated response that brings the entire system back toward nominal.

You can do that when there are relatively few operators. As the number of operators grows, the time needed to communicate and devise a coordinated strategy becomes longer, while the frequency and severity of divergences become more severe.

In the past, the economics of power-generation have favored large generating stations because they can be made more efficient. Costs for fossil fuels and nuclear power scale more slowly than generating plants' output. Emerging energy sources, such as photoelectric and wind power, have been billed as "free energy sources," although they are nothing of the kind, so power-plant efficiency figures less in the installation decision. Thus, we expect to see many more smaller plants. With more small plants, the number of sources that need to be coordinated will rise dramatically, and system-control cost and difficulty will increase.

The assumption is that increased deployment of smart-grid technology will make it possible to maintain system control in the face of increased chaos. High-speed data sharing is to improve coordination while expanded computer automation improves the speed and quality of meta-control decision making.

According to Wikipedia, support for smart grids became federal policy with passage of the Energy Independence and Security Act of 2007. The law, Title13, set out $100 million per fiscal year in funding for fiscal years 2008-2012, established a matching program for states, utilities and consumers to build smart grid capabilities, and created a Grid Modernization Commission to assess the benefits of demand response, and recommend protocol standards.

The Act directs the National Institute of Standards and Technology (NIST) to coordinate the development of smart grid standards, which the Federal Energy Regulatory Commission (FERC) would then promulgate through official rulemakings. Smart grids received further support with the passage of the American Recovery and Reinvestment Act of 2009, which set aside $11 billion for the creation of a smart grid.

Progress has been swift, as it needs to be. Federal Energy Regulatory Commission (FERC) issued a proposed policy statement and action plan on 19 March 2009 for standards governing the development of a smart grid. However, FERC noted that the electric industry started moving ahead with smart grid technologies prior to these government initiatives. The Commission is proposing to establish some general principles that the smart grid standards should follow.

We have known for some years that the trend was toward more numerous smaller power plants. The handwriting has been on the wall since the introduction of a feed-in tariff (FIT) system in 1978. A feed-in tariff is an incentive structure to encourage the adoption of renewable energy through government legislation. The regional or national electricity utilities are obligated to buy renewable electricity (electricity generated from renewable sources, such as solar photovoltaics, wind power, biomass, hydropower and geothermal power) at above-market rates set by the government. The higher price helps overcome the cost disadvantages of renewable energy sources. The rate may differ among various forms of power generation.

FIT means that any Tom, Dick, and Harriett with access to enough cash can set up a generating station, then sell the power to utilities, which are obliged to buy it. This model works well for facilities, such as hospitals and certain manufacturing operations, that need to maintain back-up power generation plants in the event of power failure. Most of the time these generators stand idle. FIT allows their owners to defray some of their cost by running them during peak periods, when demand may exceed fixed-power plant capacity and electricity costs (and FIT repayments) are largest.

The unintended consequence, of course, was a more chaotic electricity environment. Specifically, since a hallmark of chaotic systems is scale invariance, departures from nominal expanded to higher spectral frequencies with smaller amplitude signals (amplitude varies inversely with frequency. While these departures are smaller, their higher frequency translates into the need for faster response. Utilities began experimenting with smart-grid technology in hope of reigning in chaos over a much larger bandwidth.

ADDITIONAL RESOURCES:

U.S. Department of Energy Smart Grid

IBM Smart Grid

American Superconductor Smart Grid: It's More than you Think