Remodeling the Economic Future
Decades ago I dated a chess player, a very good chess player, one who trained with chess masters and knew first hand many of the names in competition at that time. One day in the smokey basement pub where chess players meet to play, she came back from a game downright pissed off.
She had lost. Now, she was very good, but people win and lose all the time. I asked her why she was so upset. Her explanation stumped me: "He played like a fish," she said.
Huh?
I had her describe what it meant to play "like a fish." She explained that fish make wild, unpredictable moves, that their play doesn't fit any recognizable pattern.
"But he won," I said. I suppose comments like this are one of the big reasons we haven't seen each other in almost 20 years; but I was honestly then trying to understand the difference between a truly great player who wins and a "fish" who wins. To me, they both win, so what's the difference? After all, if a master sat me down and schooled me in the ways of the board, I wouldn't know if I was undone by a lost Fibunacci Bishop or a Pawn's Gambit or the Flirty Queen. I would only know that I lost. Checkmate.
Out on a walk last night, I finally reasoned why the term "fish" might be used. Hook a fish and drag it out of the water, and it flops about madly on the deck or the dock without getting anywhere. A chess "fish," therefore, might be someone whose play seems erratic and pointless. They don't seem to be getting anywhere, or going anywhere. Ah, but the schooled opponent of the fish is judging the fish's moves on a learned pattern, the movement of one who walks on dry land.
Let's take this fish analogy a bit further and suppose that the fish player is actually playing by rules applicable in the water. Those spastic arches and flops across the board make no sense to us dry-landers; but put us in the drink and we shall see the fish's twitches move it across great distances with an admirable economy of effort. We walkers, on the other hand, slap and kick and flap about and barely get anywhere in the water. (I have a video of myself scuba diving in Hawaii, if anyone needs images of an amateur diver for comic relief.)
All this led me to reconsider a word upon which I've been stumbling quite a bit lately: Heuristics. According to the Wiki, "(H)euristics stand for strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines." That is (if I understand this concept) we humans model complex phenomena by reducing key elements we see into more easily understood patterns and, by extrapolation, attempt to use this model to make predictions about what should happen if certain modifications to the phenomena are changed or, if left alone, what should develop in the future.
We've been talking chess up until now, so let's continue. I went with her on some of those lessons. One was revealing simply because I tried paying attention and trying to learn something. She brought a play-by-play of a recent match, and she and her instructor reviewed her choices and options that she may have missed. After she revealed each move, he would comment, sometimes at length, at similar games between masters. I have to admit that his knowledge of chess lore was fascinating. He could dredge from his brain piece positions from matches in the 1950s, and relate those ancient games to the one she had brought for review, following options and asking whether she had considered this or that strategy, this or that defense.
I've thought about this lesson for decades now. I'm not one of those people who believe we humans use only a small percentage of our gray matter. That's utter bullshit. I do believe people benefit from using that squishy lump just as we benefit from flexing out motion making muscles, but to assign a mere 10% as the benchmark of the average brain's use is simply laughable. More likely, we brain folks are just not accustomed to using our brains. We're using our memories to do silly things instead of wowing the crowds with feats of memory or invention.
What this master and my ex were doing, though, were analyzing their realities, those chess games, based upon a memory of past games and the evaluated strategies used by those past players. They were taking the history of the game and distilling it into meta-strategies, patterns of pieces moving in sequence that strike a mental chord and that can be used to predict, based upon those histories, what outcomes might develop.
And here we find the weakness to which our minds are enslaved: What if these models of games past, these heuristic memory aids, don't apply to the game at hand? If the player she faced that day hadn't studied Karkarov or Fischer or Deep Blue, that player might very well be moving about the board in "unrecognized" patterns. Yes, the moves might very well be indicative of a novice, the spastic horsie jumps and silly castlings of someone who isn't bothering to analyze things, to project the possible outcomes, someone who is instead just playing for a lark. These moves could, though, be coldly calculated using difference heuristic experiences, different modeling, perhaps done on the player's own. This guy might have been an autistic Rainman character who played in his own head without considering the depth and breadth of the game's official history. This fish might have been swimming in his best element and watching her, my ex, kicking wildly just to keep her head above water.
With this possibility in mind, I've been mulling over the recent financial crisis, looking for clues that might point to what went wrong and how to avoid personal disaster. I won't claim I know enough to solve the world's problems, but I might just take actions that make me and The Wife more comfortable in our collective dotage.
Over the last year, many in the press have been blaming the financial institutions that brought everyone into this morass for placing too much emphasis on a mathematical formula. Okay. Let's follow that bit just for a moment. While we do so, though, let's also dismiss the outright malfeasance and crimes that happened, focusing on a simple formula that computers can use. I don't care what formula it is, formulae are used to model realities too complex for most understand without that aid. Ah, but in that case we have ourselves a bit of a problem, because we evaluate the strength or weakness of a formula based upon the previous laws of mathematics. Mathematical formulae of all stripes are therefore a bit of heuristic history used by computers, either those in beige cases or those with sharp pencils and some time on their hands.
In Emergence, Steven Johnson recounts an experiment by Danny Hillis. He tried to break a record by coming up with a new number sorting program:
For years, number sorting has served as one of the benchmark tests for ingenious programmers, like chess-playing applications. Throw a hundred random numbers at a program and see how many steps it takes to sort the digits in the correct order. Using traditional programming techniques, the record for number sorting stood at sixty steps when Hillis decided to try his hand. But Hillis didn't just sit down to write a number-sorting application. What Hillis created was a recipe for learning, a program for creating another program. . . . He taught the computer to figure out how to sort numbers on its own.
. . . Hillis instructed the computer to generate thousands of miniprograms, each composed of random combinations of instructions, creating a kind of digital gene pool. Each program was confronted with a disorderly sequence of numbers, and each tried its hand at putting them in the correct order. The first batch of programs were, as you might imagine, utterly inept at number sorting. . . . But some programs were better than others. . . . Those programs became the basis for the next iteration, only Hillis would mutate their code slightly and crossbreed them with the other promising programs. And the whole process would repeat itself; the most successful programs of the new generation would be chosen, then subjected to the same transformation. Mix, mutate, evaluate, repeat. (Johnson, Emergence, 2001, Scribner, p. 170-171, emphasis by the author.)
I'm a big fan of using computers to solve complex problems, be those problems of understanding Darwin's process or Hillis's quest to order numbers quickly. After a few thousand iterations, Hillis found he had a mutated and bred some pretty decent programs, just not record breakers. So he introduced "predators" to the mix, evaluators that would judge each competitor . . . and delete the ones that took the most steps.
After only thirty minutes of this new system, the computer had evolved a batch of programs that could sort numbers in sixty-two steps, just two shy of the all-time record. Hillis's system functioned, in biological terms, more like an environment than an organism: it created a space where intelligent programs could grow and adapt. . . . (Johnson, ibid, pp. 172-173.)
Ah, but it's Hillis's observation that should give us pause: "One of the interesting things about the sorting programs that evolved in my experiment is that I do not understand how they work. . . . I have carefully examined their instruction sequences themselves. It may be that the programs are not understandable." (Johnson, ibid, p. 173, emphasis, this time, mine.)
They work, but they cannot be understood. Therefore, they cannot be evaluated, at least not by most human minds. How they work might just exceed our heuristic abilities. They just don't make sense to us.
Many other things don't make sense, even to those tasked with making sense of the chaos. Take economists. First of all, as John Michael Greer points out in his Archdruid Report, economists are in a particularly tough bind:
First of all, for professional economists, being wrong is much more lucrative than being right. During the runup to a speculative binge, and even more so during the binge itself, a great many people are willing to pay handsomely to be told that throwing their money into the speculation du jour is the right thing to do. Very few people are willing to pay to be told that they might as well flush it down the toilet, even - indeed, especially - when this is the case. During and after the crash, by contrast, most people have enough calls on their remaining money that paying economists to say anything at all is low on the priority list. (Emphasis by the author)
This might have something to do with positive-outcome bias. People tend to want to hear good, positive outcomes, despite the fact that neutral or negative results can be of enormous value to future investigators. A recent experiment furthermore implies this bias is far more prevalent than even the Skeptic's Dictionary suggests. "Media bias may be due to scientific journal bias, but the latter seems to be due mainly to researchers not submitting negative outcome studies for publication (the file-drawer effect), rather than to bias on the part of publication or peer review editors" might have just been further refined to include bias on the part of the publishers of peer-reviewed material discounted at the end of the Skeptic's definition. There appears to be equal bias in both the presenters and the reviewers.
Ah, but the economists face positive-outcome bias on steroids. Say good things and get paid, verses warn of doom and starve. Hmmmm. That doesn't sound like a hard professional choice to make.
Furthermore, Greer notes that economics has hardly passed scientific muster yet:
The fact that you can get some fraction of nature to behave in a certain way under arbitrary conditions in the artificial setting of a laboratory does not mean that nature behaves that way left to herself. If all you want to know is what you can force a given fraction of nature to do, this is well and good, but if you want to understand how the world works, the fact that you can often force nature to conform to your theory is not exactly helpful.
Economics is particularly vulnerable to this sort of malign feedback because its raw material - human beings making economic decisions - is so complex that the only way to control all the variables is to impose conditions so arbitrary and rigid that the results have only the most distant relation to the real world. The logical way out of this trap is to concentrate on the equivalent of natural history, which is economic history: the record of what has actually happened in human communities under different economic conditions. This is exactly what those who predicted the housing crash did: they noted that a set of conditions in the past (a bubble) consistently led to a common result (a crash) and used that knowledge to make accurate predictions about the future.
Yet this is not, on the whole, what successful economists do nowadays. Instead, a great many of them spend their careers generating elaborate theories and quantitative models that are rarely tested against the evidence of economic history. The result is that when those theories are tested against the evidence of today’s economic realities, they often fail.
Examined, economic histories can predict future collapses just as studying past chess games can help players familiarize themselves with and play toward future probable outcomes. But only, as Greer notes, when economists actually look at history. And, I'll note, when the past is similar enough to the future to allow such heuristic predictions.
But what if the future has a completely new set of circumstances to offer? What if the future flops around in new and unpredictably interesting ways . . . like, say, a fish? This is the topic covered by Greer's latest entry where he likens the economist's conundrum to Descarte's Fallacy:
Rene Descartes is famous nowadays for saying “I think, therefore I am.” Few people these days take the time to find out what he meant by that statement, and fewer still catch onto the radical project that underlay it. . . .
Descartes was arguing, in effect, that “to be” means the same thing as “to be known,” and “to be known” in turn equals “to be precisely defined.” It’s clear that he recognized, and intended, the sweeping implications of this metaphysical stance. It’s equally clear that a great many of the people who unknowingly follow his lead nowadays either accept those implications uncritically or have never noticed their existence. In the hands of much of modern science, in particular, Descartes’ equation has been blended with a passion for quantitative measurement to produce an even more extreme form of the same logic. To a great many scientists today, what exists is limited to what can be known; what can be known is limited to what can be measured; and what can be measured is treated as though it was identical to its measurements. (Emphasis mine.)
Greer continues to point out that wealth, the sum total of labor and resources at hand, is not the same as money. Money is merely the yardstick by which we -- at any given time -- measure wealth. That italicized portion proves most important, especially when one considers that times change. Greer continues:
Money is so convenient as a way of measuring wealth that very often it ends up eclipsing wealth, and this is why most economists nowadays, even when they think they’re talking about wealth, are actually talking about money. This becomes especially problematic when, as so often happens, they start attributing to wealth characteristics that are only true of money.
This habit of thought pervades contemporary economics. For a relevant example, watch the way most economists these days brush aside the immense challenges of peak oil with the assurance that if oil ever does get scarce, the market will come up with alternatives. Implicit in this claim is the assumption that any energy source is as good as any other, and that the total amount in the system is effectively unlimited. This is true of money - one dollar bill is worth exactly the same amount as any other, and the total number of dollars in circulation is as close to limitless, these days, as the printing presses of the US Treasury can make it - but it is emphatically not true of energy resources, or of any other form of wealth.
He continues by noting with precision how oil and other fossil fuels cannot be easily replaced, and that most of our economy depends upon these fuels to function. Even if they were to be replaced, retooling our economy would prove a monumental, unprecedented task, the costs of which most economists have yet to consider:
Presumably an economist would notice something odd if he sat down at a lunch counter, ordered the daily special, and was handed instead a box of socket wrenches, even if the price of the wrenches was exactly the same as the daily special. If the economist was starving on a desert island and a crate that washed ashore proved to contain socket wrenches rather than food, the difference would be a matter of life or death. This latter is uncomfortably close to our position just now, as the world’s energy companies race each other and the clock to extract fossil fuels in nearly unimaginable volumes from the Earth’s dwindling supplies. If we allow ourselves to wait until those supplies start to run short, it will be much too late to start retooling our civilization for some other energy resource, even if one happens to turn up.
Hence, Descarte's Fallacy as applied to economics: "Cogito ergo sums of money" is simply a mangled pun when there's nothing of sufficient value to buy.
I have no plan to escape from any heuristic trap I might have built for myself. Why should I? I didn't build it; it's how our brains function. When one rides the roller coaster, one expects some ups and downs. It's enough that I can recognize that these traps exist, that our human brains have limited abilities to escape the mental shorthand that passes for our understanding. It's been recognized for thousands of years. Zen masters have long noted the koan, a logical impossibility or dissonant narrative designed to help acolytes snap out of familiar heuristic thought patterns and achieve bits of sharp enlightenment.
Is it too late to do the same for economics? I hope not. I have, though, been noting an almost disgusting lack of awareness in economist interviews, a prevalence to repeat the free-market über alles party line without considering all the variables that made this petroleum-fueled expansion possible and that has no experience calculating how much it might cost in human labor to grow, for example, a day's worth of bread: "The 7,500 calories in today's ($.69) bag of flour would equal the diet of a four-person peasant family for a whole day; the difference is that it would take three days of medieval work to afford."
Such stark changes are already, I think, happening. One just needs to know what one should look for, and how to cut through the Green Shoots recovery-is-on-its-way blather that distracts most who talk about the economy, even when that's their job. . . perhaps especially when that's their job.
They're acting just like they're losing to a fish. An enormous whale of a fish.
She had lost. Now, she was very good, but people win and lose all the time. I asked her why she was so upset. Her explanation stumped me: "He played like a fish," she said.
Huh?
I had her describe what it meant to play "like a fish." She explained that fish make wild, unpredictable moves, that their play doesn't fit any recognizable pattern.
"But he won," I said. I suppose comments like this are one of the big reasons we haven't seen each other in almost 20 years; but I was honestly then trying to understand the difference between a truly great player who wins and a "fish" who wins. To me, they both win, so what's the difference? After all, if a master sat me down and schooled me in the ways of the board, I wouldn't know if I was undone by a lost Fibunacci Bishop or a Pawn's Gambit or the Flirty Queen. I would only know that I lost. Checkmate.
Out on a walk last night, I finally reasoned why the term "fish" might be used. Hook a fish and drag it out of the water, and it flops about madly on the deck or the dock without getting anywhere. A chess "fish," therefore, might be someone whose play seems erratic and pointless. They don't seem to be getting anywhere, or going anywhere. Ah, but the schooled opponent of the fish is judging the fish's moves on a learned pattern, the movement of one who walks on dry land.
Let's take this fish analogy a bit further and suppose that the fish player is actually playing by rules applicable in the water. Those spastic arches and flops across the board make no sense to us dry-landers; but put us in the drink and we shall see the fish's twitches move it across great distances with an admirable economy of effort. We walkers, on the other hand, slap and kick and flap about and barely get anywhere in the water. (I have a video of myself scuba diving in Hawaii, if anyone needs images of an amateur diver for comic relief.)
All this led me to reconsider a word upon which I've been stumbling quite a bit lately: Heuristics. According to the Wiki, "(H)euristics stand for strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines." That is (if I understand this concept) we humans model complex phenomena by reducing key elements we see into more easily understood patterns and, by extrapolation, attempt to use this model to make predictions about what should happen if certain modifications to the phenomena are changed or, if left alone, what should develop in the future.
We've been talking chess up until now, so let's continue. I went with her on some of those lessons. One was revealing simply because I tried paying attention and trying to learn something. She brought a play-by-play of a recent match, and she and her instructor reviewed her choices and options that she may have missed. After she revealed each move, he would comment, sometimes at length, at similar games between masters. I have to admit that his knowledge of chess lore was fascinating. He could dredge from his brain piece positions from matches in the 1950s, and relate those ancient games to the one she had brought for review, following options and asking whether she had considered this or that strategy, this or that defense.
I've thought about this lesson for decades now. I'm not one of those people who believe we humans use only a small percentage of our gray matter. That's utter bullshit. I do believe people benefit from using that squishy lump just as we benefit from flexing out motion making muscles, but to assign a mere 10% as the benchmark of the average brain's use is simply laughable. More likely, we brain folks are just not accustomed to using our brains. We're using our memories to do silly things instead of wowing the crowds with feats of memory or invention.
What this master and my ex were doing, though, were analyzing their realities, those chess games, based upon a memory of past games and the evaluated strategies used by those past players. They were taking the history of the game and distilling it into meta-strategies, patterns of pieces moving in sequence that strike a mental chord and that can be used to predict, based upon those histories, what outcomes might develop.
And here we find the weakness to which our minds are enslaved: What if these models of games past, these heuristic memory aids, don't apply to the game at hand? If the player she faced that day hadn't studied Karkarov or Fischer or Deep Blue, that player might very well be moving about the board in "unrecognized" patterns. Yes, the moves might very well be indicative of a novice, the spastic horsie jumps and silly castlings of someone who isn't bothering to analyze things, to project the possible outcomes, someone who is instead just playing for a lark. These moves could, though, be coldly calculated using difference heuristic experiences, different modeling, perhaps done on the player's own. This guy might have been an autistic Rainman character who played in his own head without considering the depth and breadth of the game's official history. This fish might have been swimming in his best element and watching her, my ex, kicking wildly just to keep her head above water.
With this possibility in mind, I've been mulling over the recent financial crisis, looking for clues that might point to what went wrong and how to avoid personal disaster. I won't claim I know enough to solve the world's problems, but I might just take actions that make me and The Wife more comfortable in our collective dotage.
Over the last year, many in the press have been blaming the financial institutions that brought everyone into this morass for placing too much emphasis on a mathematical formula. Okay. Let's follow that bit just for a moment. While we do so, though, let's also dismiss the outright malfeasance and crimes that happened, focusing on a simple formula that computers can use. I don't care what formula it is, formulae are used to model realities too complex for most understand without that aid. Ah, but in that case we have ourselves a bit of a problem, because we evaluate the strength or weakness of a formula based upon the previous laws of mathematics. Mathematical formulae of all stripes are therefore a bit of heuristic history used by computers, either those in beige cases or those with sharp pencils and some time on their hands.
In Emergence, Steven Johnson recounts an experiment by Danny Hillis. He tried to break a record by coming up with a new number sorting program:
For years, number sorting has served as one of the benchmark tests for ingenious programmers, like chess-playing applications. Throw a hundred random numbers at a program and see how many steps it takes to sort the digits in the correct order. Using traditional programming techniques, the record for number sorting stood at sixty steps when Hillis decided to try his hand. But Hillis didn't just sit down to write a number-sorting application. What Hillis created was a recipe for learning, a program for creating another program. . . . He taught the computer to figure out how to sort numbers on its own.
. . . Hillis instructed the computer to generate thousands of miniprograms, each composed of random combinations of instructions, creating a kind of digital gene pool. Each program was confronted with a disorderly sequence of numbers, and each tried its hand at putting them in the correct order. The first batch of programs were, as you might imagine, utterly inept at number sorting. . . . But some programs were better than others. . . . Those programs became the basis for the next iteration, only Hillis would mutate their code slightly and crossbreed them with the other promising programs. And the whole process would repeat itself; the most successful programs of the new generation would be chosen, then subjected to the same transformation. Mix, mutate, evaluate, repeat. (Johnson, Emergence, 2001, Scribner, p. 170-171, emphasis by the author.)
Click to viewI'm a big fan of using computers to solve complex problems, be those problems of understanding Darwin's process or Hillis's quest to order numbers quickly. After a few thousand iterations, Hillis found he had a mutated and bred some pretty decent programs, just not record breakers. So he introduced "predators" to the mix, evaluators that would judge each competitor . . . and delete the ones that took the most steps.
After only thirty minutes of this new system, the computer had evolved a batch of programs that could sort numbers in sixty-two steps, just two shy of the all-time record. Hillis's system functioned, in biological terms, more like an environment than an organism: it created a space where intelligent programs could grow and adapt. . . . (Johnson, ibid, pp. 172-173.)
Ah, but it's Hillis's observation that should give us pause: "One of the interesting things about the sorting programs that evolved in my experiment is that I do not understand how they work. . . . I have carefully examined their instruction sequences themselves. It may be that the programs are not understandable." (Johnson, ibid, p. 173, emphasis, this time, mine.)
They work, but they cannot be understood. Therefore, they cannot be evaluated, at least not by most human minds. How they work might just exceed our heuristic abilities. They just don't make sense to us.
Many other things don't make sense, even to those tasked with making sense of the chaos. Take economists. First of all, as John Michael Greer points out in his Archdruid Report, economists are in a particularly tough bind:
First of all, for professional economists, being wrong is much more lucrative than being right. During the runup to a speculative binge, and even more so during the binge itself, a great many people are willing to pay handsomely to be told that throwing their money into the speculation du jour is the right thing to do. Very few people are willing to pay to be told that they might as well flush it down the toilet, even - indeed, especially - when this is the case. During and after the crash, by contrast, most people have enough calls on their remaining money that paying economists to say anything at all is low on the priority list. (Emphasis by the author)
This might have something to do with positive-outcome bias. People tend to want to hear good, positive outcomes, despite the fact that neutral or negative results can be of enormous value to future investigators. A recent experiment furthermore implies this bias is far more prevalent than even the Skeptic's Dictionary suggests. "Media bias may be due to scientific journal bias, but the latter seems to be due mainly to researchers not submitting negative outcome studies for publication (the file-drawer effect), rather than to bias on the part of publication or peer review editors" might have just been further refined to include bias on the part of the publishers of peer-reviewed material discounted at the end of the Skeptic's definition. There appears to be equal bias in both the presenters and the reviewers.
Ah, but the economists face positive-outcome bias on steroids. Say good things and get paid, verses warn of doom and starve. Hmmmm. That doesn't sound like a hard professional choice to make.
Furthermore, Greer notes that economics has hardly passed scientific muster yet:
The fact that you can get some fraction of nature to behave in a certain way under arbitrary conditions in the artificial setting of a laboratory does not mean that nature behaves that way left to herself. If all you want to know is what you can force a given fraction of nature to do, this is well and good, but if you want to understand how the world works, the fact that you can often force nature to conform to your theory is not exactly helpful.
Economics is particularly vulnerable to this sort of malign feedback because its raw material - human beings making economic decisions - is so complex that the only way to control all the variables is to impose conditions so arbitrary and rigid that the results have only the most distant relation to the real world. The logical way out of this trap is to concentrate on the equivalent of natural history, which is economic history: the record of what has actually happened in human communities under different economic conditions. This is exactly what those who predicted the housing crash did: they noted that a set of conditions in the past (a bubble) consistently led to a common result (a crash) and used that knowledge to make accurate predictions about the future.
Yet this is not, on the whole, what successful economists do nowadays. Instead, a great many of them spend their careers generating elaborate theories and quantitative models that are rarely tested against the evidence of economic history. The result is that when those theories are tested against the evidence of today’s economic realities, they often fail.
Examined, economic histories can predict future collapses just as studying past chess games can help players familiarize themselves with and play toward future probable outcomes. But only, as Greer notes, when economists actually look at history. And, I'll note, when the past is similar enough to the future to allow such heuristic predictions.
But what if the future has a completely new set of circumstances to offer? What if the future flops around in new and unpredictably interesting ways . . . like, say, a fish? This is the topic covered by Greer's latest entry where he likens the economist's conundrum to Descarte's Fallacy:
Rene Descartes is famous nowadays for saying “I think, therefore I am.” Few people these days take the time to find out what he meant by that statement, and fewer still catch onto the radical project that underlay it. . . .
Descartes was arguing, in effect, that “to be” means the same thing as “to be known,” and “to be known” in turn equals “to be precisely defined.” It’s clear that he recognized, and intended, the sweeping implications of this metaphysical stance. It’s equally clear that a great many of the people who unknowingly follow his lead nowadays either accept those implications uncritically or have never noticed their existence. In the hands of much of modern science, in particular, Descartes’ equation has been blended with a passion for quantitative measurement to produce an even more extreme form of the same logic. To a great many scientists today, what exists is limited to what can be known; what can be known is limited to what can be measured; and what can be measured is treated as though it was identical to its measurements. (Emphasis mine.)
Greer continues to point out that wealth, the sum total of labor and resources at hand, is not the same as money. Money is merely the yardstick by which we -- at any given time -- measure wealth. That italicized portion proves most important, especially when one considers that times change. Greer continues:
Money is so convenient as a way of measuring wealth that very often it ends up eclipsing wealth, and this is why most economists nowadays, even when they think they’re talking about wealth, are actually talking about money. This becomes especially problematic when, as so often happens, they start attributing to wealth characteristics that are only true of money.
This habit of thought pervades contemporary economics. For a relevant example, watch the way most economists these days brush aside the immense challenges of peak oil with the assurance that if oil ever does get scarce, the market will come up with alternatives. Implicit in this claim is the assumption that any energy source is as good as any other, and that the total amount in the system is effectively unlimited. This is true of money - one dollar bill is worth exactly the same amount as any other, and the total number of dollars in circulation is as close to limitless, these days, as the printing presses of the US Treasury can make it - but it is emphatically not true of energy resources, or of any other form of wealth.
He continues by noting with precision how oil and other fossil fuels cannot be easily replaced, and that most of our economy depends upon these fuels to function. Even if they were to be replaced, retooling our economy would prove a monumental, unprecedented task, the costs of which most economists have yet to consider:
Presumably an economist would notice something odd if he sat down at a lunch counter, ordered the daily special, and was handed instead a box of socket wrenches, even if the price of the wrenches was exactly the same as the daily special. If the economist was starving on a desert island and a crate that washed ashore proved to contain socket wrenches rather than food, the difference would be a matter of life or death. This latter is uncomfortably close to our position just now, as the world’s energy companies race each other and the clock to extract fossil fuels in nearly unimaginable volumes from the Earth’s dwindling supplies. If we allow ourselves to wait until those supplies start to run short, it will be much too late to start retooling our civilization for some other energy resource, even if one happens to turn up.
Hence, Descarte's Fallacy as applied to economics: "Cogito ergo sums of money" is simply a mangled pun when there's nothing of sufficient value to buy.
I have no plan to escape from any heuristic trap I might have built for myself. Why should I? I didn't build it; it's how our brains function. When one rides the roller coaster, one expects some ups and downs. It's enough that I can recognize that these traps exist, that our human brains have limited abilities to escape the mental shorthand that passes for our understanding. It's been recognized for thousands of years. Zen masters have long noted the koan, a logical impossibility or dissonant narrative designed to help acolytes snap out of familiar heuristic thought patterns and achieve bits of sharp enlightenment.
Is it too late to do the same for economics? I hope not. I have, though, been noting an almost disgusting lack of awareness in economist interviews, a prevalence to repeat the free-market über alles party line without considering all the variables that made this petroleum-fueled expansion possible and that has no experience calculating how much it might cost in human labor to grow, for example, a day's worth of bread: "The 7,500 calories in today's ($.69) bag of flour would equal the diet of a four-person peasant family for a whole day; the difference is that it would take three days of medieval work to afford."
Such stark changes are already, I think, happening. One just needs to know what one should look for, and how to cut through the Green Shoots recovery-is-on-its-way blather that distracts most who talk about the economy, even when that's their job. . . perhaps especially when that's their job.
They're acting just like they're losing to a fish. An enormous whale of a fish.