#Monetary Sovereignty – Mitchell

Mitchell’s laws: The more budgets are cut and taxes increased, the weaker an economy becomes. Until the 99% understand the need for deficits, the 1% will rule. To survive long term, a monetarily non-sovereign government must have a positive balance of payments. Austerity = poverty and leads to civil disorder. Those, who do not understand the differences between Monetary Sovereignty and monetary non-sovereignty, do not understand economics.
==========================================================================================================================================

You can find many parallels between meteorology and economics. The most striking: They both involve huge masses of data and they both are extremely chaotic – small changes can cause large effects – which makes prediction extremely difficult.

Modern Monetary Theory (MMT) and Monetary Sovereignty (MS) are quite good at describing the economy as it is, but rather poor at describing the economy as it will be. Yes, I can tell you, with some assurance, that running a federal surplus will lead to a recession or a depression, (See: Items 3 and 4), but I can’t tell you exactly when this will happen, nor exactly what will trigger it.

It took a 10-year federal surplus to cause the Great Depression, but a mere 3 years of surplus caused the recession of 2001, and most recessions have not been associated with federal surpluses. They have been associated with reductions in deficit growth.

In each case, there needed to be a trigger(s), something in addition to surpluses or reductions in deficit growth, to push the economy past the tipping point. (“What triggers recessions and depressions?”)

And therein lies the rub, because although federal surpluses and deficit growth reductions are somewhat predictable, the triggers are much less so. And that is why I beg the National Center for Atmospheric Research, or any other federal agency, to help us:

New Wyoming supercomputer expected to boost atmospheric science
By Scott Gold, Los Angeles Times, 6/10/12

The National Center for Atmospheric Research’s machine, called “Yellowstone,” is one of the fastest computers ever built, its sheer speed designed to burst through the limits of chaos theory.

CHEYENNE, Wyo. — This month, on a barren Wyoming landscape dotted with gopher holes and hay bales, the federal government is assembling a supercomputer 10 years in the making, one of the fastest computers ever built and the largest ever devoted to the study of atmospheric science.

The National Center for Atmospheric Research’s supercomputer will have 100 racks of servers and 72,000 core processors, so many parts that they must be delivered in the back of a 747. It will be capable of performing 1.5 quadrillion calculations — a quadrillion is a 1 followed by 15 zeros — every second.

The study of climate and weather patterns has always been hamstrung by volatility — by elements of chaos in the seas and the air. That challenge is most famously summed up by the “butterfly effect,” the idea that the flapping of a butterfly’s wings on the coast of Africa can determine whether a hurricane will strike New Orleans.

Rather than warning of a tornado risk in the central U.S. between noon and 9 p.m., scientists might one day warn of a tornado risk in Woodson County, Kan., between 1 and 3 p.m. Rather than warning of a hurricane striking the coast of Texas, they hope to be able to warn of a hurricane striking the town of Freeport, with a top wind speed of 90 mph and a tidal surge of 4 1/2 feet.

That regional accuracy is particularly critical in the study of climate change. “The disaster of climate change happens on a regional scale,” Loft said. “Everything is connected.”

For example, once scientists use Yellowstone to help predict the melting of ice at the North Pole, which means significant change in nearby waters, they can better predict the patterns of storms that form in the Gulf of Alaska. Then Yellowstone can help predict how those storms will deposit snow atop the Sierra Nevada, down to precise changes in elevation on individual faces of mountains.

That snow will melt, and the water will run downhill — which means Yellowstone can help predict how much water California will have to drink, even the most efficient locations to build the state’s reservoirs.

Yes, predicting the weather is important, because it will allow us to react sooner and better. But, I argue that predicting the economy is even more important. As Mark Twain famously said, “Everyone talks about the weather, but no one does anything about it.” While we must react to weather, we have the every day power to bend the economy to our will. We can do something about our economy.

We’re a long way from preventing or turning off a hurricane, but we already know how to prevent and turn off a recession — if given the correlated data.

The computer will be housed in a futuristic, $70-million compound west of Cheyenne. The National Science Foundation, which funds NCAR, is paying $50 million of the tab.

An investment of only $70 million dollars — that’s less than a rounding error in the federal budget — to get a machine that will help us predict and change the world’s economies. Is it worth just $70 million to be able to predict and prevent the every-five-year economic crises that beset us? How many billions has the recession cost us — a recession that could have been prevented — if the data were assembled and correlated? How many ruined lives? Is a paltry $75 million a worthwhile investment to help prevent all that misery?

Yellowstone will replace NCAR’s Bluefire system, a supercomputer in its own right, though this one will have roughly 30 times the throughput of the old system.

Hey, if you don’t want it, we’ll take it.

Yellowstone will hold 600 sets of atmospheric data in its vast memory bank — temperatures, humidity, wind motion, rainfall. Information gleaned from the world’s data-collection systems — buoys in the ocean, wind monitors fastened to the top of telephone poles — will be added to the archive.

How about an economics computer that will correlate such world data as debts, imports/exports, salaries, savings, agriculture, manufacturing, exchange rates, population shifts, inflation, wars, technology changes and yes, world weather. Today’s economists are able to focus on only a handful of data at any one time. In essence, we try to predict the weather in Florida based on last year’s rain in France.

(The machine will be open) to researchers from across the nation, probably in August. Scientists will make proposals to book an “allocation” on the computer, similar to using minutes on a cellphone plan. Most will access the computer remotely.

Some hope to predict migration patterns of animals, others the success and failure of certain farm crops, others specific hillsides that would be the most efficient spots for wind turbines.

Think of how valuable this would be for economics.

NCAR scientist Michael Wiltberger studies solar flares, superheated gas that emanates from the sun, with the potential to be enormously disruptive on Earth.

“Right now, we don’t know why a particular configuration of the magnetic field of the sun is going to erupt,” Wiltberger said. “We need to know — and now we can run millions times more models to provide meaningful predictions.”

Armed with better predictions of what will happen when solar flares reach Earth — and where, precisely, they will occur — scientists could warn energy companies to protect against power surges. Global positioning systems could be disrupted, so farmers that use GPS to map crops could be warned to suspend planting operations.

Hey, some of this is economics stuff. And, is it more important to predict solar flares or the next depression?

NCAR senior scientist Morris Weisman specializes in a tricky corner of science: severe, high-impact weather events, which are by definition so rare that they are difficult to predict. “Scientifically non-satisfying” is how Weisman puts it — but with such a leap in computer modeling, he said, scientists could theoretically predict an extreme weather event “within an hour, within a few kilometers.”

Or the date and cause of a war. Or the economic implications of planting more wheat and less corn. Or the effect of opening the border between the U.S. and Mexico. Or the world-wide effect of building one water desalinization plant.

Loft marveled that such a dizzying array of experiments will be done using time-tested and sometimes rudimentary math — 19th century laws of thermal dynamics, rules of mechanics devised by Isaac Newton after an apple supposedly bonked him on the head and got him thinking about gravity. Yellowstone will use the same, just a whole lot of it at once.

We have the math. All we need is the machine.

The scientists behind Yellowstone shrug at a bitter reality: cutting edge doesn’t last long in their world. The Wyoming facility was built with enough space to accommodate the next generation of computer, which is already being contemplated, before this one is put together. “We won’t be cool for long,” Loft said. “This business is ephemeral. There’s not much room for nostalgia.”

Here is the The National Center for Atmospheric Research, having received a $70 million computer from the government, already now is planning for its replacement. Are we to believe that meteorological research is important, but economic research is not? Are we to believe there would be no value in being able to predict the next recession or depression, so we could forestall it?

I can make the case that, considering its affect on human lives, economics is the most important science of all. So where are the super computers?

We want that next machine. We need that next machine. The American people need us to have that next machine. My question is: Who in the world of economics, is asking for that next machine?

Rodger Malcolm Mitchell

http://www.rodgermitchell.com

==========================================================================================================================================
No nation can tax itself into prosperity, nor grow without money growth. Monetary Sovereignty: Cutting federal deficits to grow the economy is like applying leeches to cure anemia. Two key equations in economics:
Federal Deficits – Net Imports = Net Private Savings
Gross Domestic Product = Federal Spending + Private Investment and Consumption + Net exports

#MONETARY SOVEREIGNTY

7 Responses »

Mitchell’s laws: The more budgets are cut and taxes inceased, the weaker an economy becomes. To survive long term, a monetarily non-sovereign government must have a positive balance of payments. Austerity = poverty and leads to civil disorder. Those, who do not understand the differences between Monetary Sovereignty and monetary non-sovereignty, do not understand economics.
==========================================================================================================================================

I predict that within 20 years, the science of economics, and the economists who ply that trade, will be gone, and the world will be much better for it.

Wikipedia and Websters New Collegiate Dictionary both define “economics” as: “The social science that analyzes the production, distribution, and consumption of goods and services.” The key word is “analyzes.”

How do economists “analyze” production, distribution and consumption? They begin by assembling information. They ask, “What and how many?” “Who?” “Where?” “When and how often?”

From these data, they come to conclusions (“Why?”), and from these conclusions, they make predictions and recommendations for actions. It is the conclusions and recommendations that give economics value. Without them, economics would be useless.

In a previous post, How IBM can change the world, I described how the IBM super computer named “Watson” played Jeopardy and beat the best, two, human Jeopardy players in history.

Those who know Jeopardy understand what an amazing accomplishment this was. The game is question and answer, with the twist that the answer is given, and the contestant must come up with the question. That twist essentially is meaningless; the contestant merely precedes his answer with the words “Who is” or “What is” and voila, an answer becomes the required question.

The difficulty lies in the nature of the questions. Not only do they ask for a broad knowledge of obscure trivia, but the wording of the questions can be ambiguous and non-specific, involving puns, analogies, metaphors and axioms.

Even a large group of programmers could not possibly input Watson with the infinite question and answer variations, so they used machine learning — a method by which a computer repeatedly answers questions, then is given the correct answers. Over time, the computer detects patterns that allow it to answer future questions more accurately.

The computers review data and the proven-wrong or proven-right answers, and doing this often enough, allows them to calculate degrees of “correctness” — the odds of various answers. Computers, of course, can handle massive data tirelessly. Humans cannot.

Every hypothesis and theory in economics evolves exactly the same way. An economist looks at data and determines its meaning. He does that by seeing repetitions and calculating correlations. At its essence, economics is mathematics.

As an economist who fancies himself somewhat creative, I believe there is no creativity or genius in economics. What passes for creativity and genius is just discovery. The economist compares two sets of data and discovers they seem to correlate — or not. So he adds more data, and soon he comes to the conclusion, or rather, discovers, that one factor seems to precede the other — most of the time.

Then he adds other data to see how they affect the results. Economics is a trial and error game.

Upon seeing what Watson can do, and understanding the nature of economics, I have come to this conclusion: There is not one thing human economists do that a computer cannot do faster, more accurately and without the hubris that affects human economists’ judgement.

Actually, what Watson did with Jeopardy is much more difficult than what a Watson clone could do with economics. Jeopardy involved interpretation of the nuances in the English language. Economics is much more straightforward — made for a computer.

Watson was primed with information, statistical and nonstatistical. An economics Watson, let’s call it “Econoputer,” would receive data from: Encyclopedias, the complete text of every book ever written, census tables, phone books, statistical abstracts, voting data, Macroeconomic and Regional Data, public finance, all federal laws, transportation, health, education, economic history, weather history, agricultural, astronomy, tax tables, physics, chemistry, crime and punishment — trillions of words and pieces of data that continuously are collected from existing sources.

Then our “Econoputer” would begin to correlate its information, trillions upon trillions of calculations, to establish probability tables, showing the likelihoods of cause and effect. No problem for Watson’s future “children.”

The President of the United states might ask a clerk in the Treasury Department, “What will happen to the economy, if I cut FICA to 5%?” Or he might go further: “What level of FICA will yield the greatest GDP and employment growth?

Econoputer would present its results: “Given all historical data, and the current situation, cutting FICA to 1.3% has a 92% probability of raising GDP 4.6% and a 94% probability of increasing employment 5.1%”

Or the President might go even broader: He might ask, “What can I do with all taxes for maximum economic growth?” Econoputer already has correlated all past tax rates and tax laws with all past tax collections, and correlated that with every other economic factor affecting GDP growth. It might tell the President to cut certain tax rates and increase others, while increasing the IRS staff by 7%.

This is what human economists attempt to do now, but no economist can do it. We are forced by human limitations, to use much less data, which is corrupted by personal biases. Can you imagine a human economist trying to answer the question: What will happen to farm income in Idaho, and total U.S. GDP, if we bring 25,000 soldiers back from Afghanistan?”

Wild-ass guess is what you’d get from a human economist. Econoputer would provide a far more precise calculation. It would include civilian clothing sales, new births and deaths, baby food sales, imports and exports, hospital visits, vacation travel — millions of changes no human could take into account.

Pierre Simon Laplace said:

We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.

The quantum mechanics’ Heisenberg Uncertainty Principle proved Laplace wrong at the atomic level, but on a macroeconomic level, the more you know about the past and present, the more you can know about the future.

Because of Watson and its future progeny, economics will be reduced to information collection, and economists will be clerks. I’m an economist, but I hardly can wait for this to happen. It will mark the end of uninformed argument and decision-making, and humans will benefit from a better world.

Rodger Malcolm Mitchell

http://www.rodgermitchell.com

==========================================================================================================================================
No nation can tax itself into prosperity, nor grow without money growth. Monetary Sovereignty: Cutting federal deficits to grow the economy is like applying leeches to cure anemia. Two key equations in economics:
Federal Deficits – Net Imports = Net Private Savings
Gross Domestic Product = Federal Spending + Private Investment and Consumption + Net exports

#MONETARY SOVEREIGNTY

7 Responses »

Mitchell’s laws: The more budgets are cut and taxes inceased, the weaker an economy becomes. To survive long term, a monetarily non-sovereign government must have a positive balance of payments. Austerity = poverty and leads to civil disorder. Those, who do not understand the differences between Monetary Sovereignty and monetary non-sovereignty, do not understand economics.
==========================================================================================================================================

Economics has more data than the human mind can comprehend. So economists (including this one) tend to focus on small clumps of data we can visualize, and from them, we draw conclusions: For instance, consider these data:

1817-1821: U. S. Federal Debt reduced 29%. Depression began 1819.
1823-1836: U. S. Federal Debt reduced 99%. Depression began 1837.
1852-1857: U. S. Federal Debt reduced 59%. Depression began 1857.
1867-1873: U. S. Federal Debt reduced 27%. Depression began 1873.
1880-1893: U. S. Federal Debt reduced 57%. Depression began 1893.
1920-1930: U. S. Federal Debt reduced 36%. Depression began 1929.

I conclude, from these data, federal surpluses cause depressions. I even offer a rationalle: Surpluses remove dollars from the economy, and a growing economy requires a growing supply of money, while a shrinking supply of money causes economic shrinkage (a depression). I see ample proof of this in many places, and currently the euro nations are on track to provide even more proof.

But wait a minute. The 1819 depression began after just two years of surplus, while the 1929 depression waited for nine years of surplus. Why?

And then there was the Clinton surplus of 1998 -2000, that only caused a recession in 2001. So was there something else that triggered, or outright caused, these depressions?

I discuss this at: What triggers recessions and depressions?? But that discussion barely brushed the surface of the question.

Why didn’t I go deeper? Too many variables of indeterminate weights.

Read any paper, book or blog post on economics, and you will see conclusions, possibly supported by data, but you’ll not see all the related data along with historically proven weights. You might even see formulas on the order of X = 1a + 2b + 3c . . . N, but how predictive are those formulas? Commodity and stock chartists provide seemingly infinite graphs, and how predictive are they?

While I feel confident that federal surpluses, and even reductions in deficit growth, hurt the economy, and I offer data to support this conclusion, I do not offer proof. No economist ever has proved much of anything, though we all argue mightily for our positions. If this reminds you of religion, where nothing is proved and everyone is absolutely certain, you’re right. Economics is closer to religion than to science, and the reason is complexity.

It doesn’t have to be this way. The human brain is limited in the number of related factors it consciously can organize. Show me a formula based on a dozen variables, and I will not be able to visualize it.

But ask me to catch a fly ball, in which my brain subconsciously must analyze such variables as the speed and trajectory of the ball, wind speed, wind direction, ground (running) conditions, ball weight and size, plus all the past experiences I’ve had in running and catching a ball, and my brain can predict exactly where my glove has to be, and when — usually.

I’ll run at exactly the right pace, neither too slowly nor too fast, so that the trajectory of my glove intersects the trajectory of the ball, right on time — an amazing feat made even more amazing by the thousands of decisions and predictions my brain must make when signalling each my muscles to contract the right amounts at the right moments, just so I can take one step, let alone intercept a fly ball.

Why can I make all those predictions, involving thousands of weighted variables , but am unable to visualize a handful of variables simultaneously? I believe the answer is: Feedback.

Last year, the IBM computer named “Watson,” defeated the two greatest human players in Jeopardy history. Those who know the game, understand that this achievement was orders of magnitude beyond winning at chess. Jeopardy questions are filled with linguistic misdirections puns, rhymes, puzzles and verbal tricks.

English by itself is a complex language. Consider the real headline, “English Left Waffles on Falklands.” What does it mean? Did the English cook up a stack of waffles and leave them on some islands? Or did it mean the English left (i.e. liberals) were undecided about what to do with the Falklands?

Add that misdirection to the need to understand facts, slogans and ideas we all take for granted, and you can visualize of the kind of complexity Watson conquered. How did it do it?

Well, I can tell you what didn’t happen. There weren’t an infinite number of programmers inputting an infinite number of possible questions, in the hopes that one would match the latest Jeopardy question.

No, instead they used machine learning. Here’s an example: One of the questions named two people and asked what they had in common. The answer was supposed to be what state (Iowa, Ohio, etc.) they came from. Watson missed the first question, because it found something else the two had even more in common. The human contestants answered correctly.

Then Watson was told the correct answer, but not the reasoning behind it. The same thing happened with the second question. Watson gave the wrong answer. Humans gave the right answer. Watson was told the right answer.

But, on the third question, Watson answered correctly. It had “learned,” from the first two answers, that a state name was wanted. Thereafter, Watson answered all similar questions correctly. Given all possible answers, Watson offered the answer having the highest probability.

There would have been no way for programmers to anticipate that question, then program Watson with the answer. Machine learning accomplished in seconds, what ordinary programming never could.

Similarly, though I have caught thousands of balls in every weather, on every kind of field — balls of different sizes and shapes (beachballs, footballs, marbles) –balls going at different speeds, different distances — the next time I catch a ball, the situation will be unique. But I will receive feedback — continuous feedback. And the odds are, I either will catch the ball or quickly will realize I can’t.

With every step I take, my brain will recognize thousand of things familiar enough to analyze, and based on that familiarity, will make appropriate adjustments, perhaps millions of adjustments per second. And this feedback will allow me to predict exactly where my glove needs to be and how my muscles need to move.

Bottom line: Economics never will be a complete science so long as economists rely solely on conclusions drawn from limited data. The solution is to use a super computer, of Watson capacity or greater, that is given every conceivable piece of data prior to every important result — a super computer that is told to correlate all that data with each result (i.e. “correct answer”), and to learn from each result (feedback), the most likely next result.

IBM spent millions on Watson. They achieved some measure of publicity, but they now can achieve so much more. If IBM would create an “economics Watson,” pumped full of data and engaged in machine learning, IBM could predict, and thereby change, the world.

Rodger Malcolm Mitchell

http://www.rodgermitchell.com

==========================================================================================================================================
No nation can tax itself into prosperity, nor grow without money growth. Monetary Sovereignty: Cutting federal deficits to grow the economy is like applying leeches to cure anemia. Two key equations in economics:
Federal Deficits – Net Imports = Net Private Savings
Gross Domestic Product = Federal Spending + Private Investment and Consumption + Net exports

#MONETARY SOVEREIGNTY

8 Responses »