I say the noise variation, of that chart, is a couple of quarters and the decay time is four years. This is a five or six pole estimation. The number of updates required for the economy to predict this system is exactly your economy, in units of inventory exchanges times inventory value, giving you the money equation. The accumulation of updates goes power yada yada yada.
So, you have this second energy constraint on solutions of the diagonalizing set of production functions.
The military acts like this, because they are regular. They compute results in quanta, look how they rank, look at their contracts. But we can measure this, in the real economy, like it is done above. Then reconstruct estimates of labor, for example, that have limited solutions in structure, find optimum "cheats" for flat line government, and spot money speculators.
The growth model is intuitive, either a linear expansion in precision or a rank jump in system parameter count. We stretch to our most uncomfortable precision, and as we do this there are increasing amounts of rank jump, and rank reversion. Go back to Brads equations and get precision from total factor productivity. Compare the outlook from an increase in precision to outlook from a rank jump in system parameters. Your get the probability of events like Bretton Woods.
I know nothing about stocks, but that won't stop me.
Track you stocks with a four pole adaptive filter, track net revenue. Monitor the residual noise power spectrum.
Monitor the same stock revenue with a five pole filter, and monitor the residual. When the five pole shows significant reduction in noise residual, the stock is a pick.
You are picking stocks that are candidates for rank jump.
Let's look at the Roman system. If one had a regular (productivity growth near zero) but stable system in Roman times, then the accounting system begins to resemble the rank structure of the production functions. In mathematics coding, look at Huffman coding, then allocate significant bits of your counting system to the major modes of your economy. A unit of inventory might become one town - three estates + 2 regimental troops. That is, almost an exact description of a goods convoy across the Roman area of influence.
This should be historically verified.
We would also spot, historically, in Roman times the odd lot convoys which made speculators rich. They would spend the cost for real decimal multiplication, and assemble odd lot convoys, making marginal profit off of the quantization error.
One can go back to the paper Brad referenced about yields in very early agriculture, showing we were not knowledge limited but transportation limited in agricultural output.
The quantum historian can do an almost exact physical count of transportation capability. That limits the largest NlogN update rate, and from that reconstruct the probable wealth relationship between wealthy farmers and the transportation sector, because we know what the N-1 rank update rate must be, so we know right away a limit on the equilibriated farming production function. We know where wealthy farmers sat in society.
The quantum economic historian can make bounded guesses at the lineage of structure, by rank and update rate, over different epochs in history. Brad did this with connected exponentials, each connection point is a rank adjustment.
We need to do this on smaller scales. We need to recognize basic quantum limits (one man one vote) and do work around. The Malthusian moment for a libertarian is when he realizes global rank is finite bound, around 7, 8?
Lets apply the Econobrowser chart above to the typical medieval agricultural town. We want to decompose the town into the lord mayor and staff, the food industry, and the rest is labor.
The Lord mayor makes a five year bet on net profits for the town in the next trading cycle with towns across the region. The lord mayor takes his bet to the farmers and to the rest of the labor community. Over the next five years he will adjust his bet on a quarterly basis, splitting the deviation from his five year bet with deviations from the other 2 and one year bets. All parties seek to maintain their respective productions functions.
On the chart, then, revisions come back to the lord mayor. annually from the farmer, who gets them quarterly from labor. If the mayor is getting eight updates over the period, and labor is delivering 20, then farming is handling updates in rate fluctuations between 20 and 8, that defines the spectrum of farming.
If modes are reporting periods,
We can define modes, the intercity trading mode, the multi-year farming mode, and quarterly labor mode; then each production function has a three mode spectral definition, plus variance. The variance between the long mode for farmers will be split between the long mode variance for labor and the lord mayor. Mode estimates (eigen values) should be independently normally distributed.
Dividing up the system parameters, thusly, defines the wealth spectrum of the town, and defines an estimate of the block diagonal forms these three production functions might take. The minimizing objective is to allocate production spectrum to minimize quantization error, essentially. In other words, the model chosen delivers the better price estimate/time than a lower or high rank with increased precision..
Econobrowser is leading edge.
I say the noise variation, of that chart, is a couple of quarters and the decay time is four years. This is a five or six pole estimation. The number of updates required for the economy to predict this system is exactly your economy, in units of inventory exchanges times inventory value, giving you the money equation. The accumulation of updates goes power yada yada yada.
So, you have this second energy constraint on solutions of the diagonalizing set of production functions.
The military acts like this, because they are regular. They compute results in quanta, look how they rank, look at their contracts. But we can measure this, in the real economy, like it is done above. Then reconstruct estimates of labor, for example, that have limited solutions in structure, find optimum "cheats" for flat line government, and spot money speculators.
The growth model is intuitive, either a linear expansion in precision or a rank jump in system parameter count. We stretch to our most uncomfortable precision, and as we do this there are increasing amounts of rank jump, and rank reversion. Go back to Brads equations and get precision from total factor productivity. Compare the outlook from an increase in precision to outlook from a rank jump in system parameters. Your get the probability of events like Bretton Woods.
Posted by: Matt | May 02, 2008 at 01:47 PM
How would you do quantum stock picking?
I know nothing about stocks, but that won't stop me.
Track you stocks with a four pole adaptive filter, track net revenue. Monitor the residual noise power spectrum.
Monitor the same stock revenue with a five pole filter, and monitor the residual. When the five pole shows significant reduction in noise residual, the stock is a pick.
You are picking stocks that are candidates for rank jump.
Posted by: Matt | May 02, 2008 at 02:54 PM
You people insist on leaving me this thread?
Let's look at the Roman system. If one had a regular (productivity growth near zero) but stable system in Roman times, then the accounting system begins to resemble the rank structure of the production functions. In mathematics coding, look at Huffman coding, then allocate significant bits of your counting system to the major modes of your economy. A unit of inventory might become one town - three estates + 2 regimental troops. That is, almost an exact description of a goods convoy across the Roman area of influence.
This should be historically verified.
We would also spot, historically, in Roman times the odd lot convoys which made speculators rich. They would spend the cost for real decimal multiplication, and assemble odd lot convoys, making marginal profit off of the quantization error.
One can go back to the paper Brad referenced about yields in very early agriculture, showing we were not knowledge limited but transportation limited in agricultural output.
The quantum historian can do an almost exact physical count of transportation capability. That limits the largest NlogN update rate, and from that reconstruct the probable wealth relationship between wealthy farmers and the transportation sector, because we know what the N-1 rank update rate must be, so we know right away a limit on the equilibriated farming production function. We know where wealthy farmers sat in society.
The quantum economic historian can make bounded guesses at the lineage of structure, by rank and update rate, over different epochs in history. Brad did this with connected exponentials, each connection point is a rank adjustment.
We need to do this on smaller scales. We need to recognize basic quantum limits (one man one vote) and do work around. The Malthusian moment for a libertarian is when he realizes global rank is finite bound, around 7, 8?
Posted by: Matt | May 03, 2008 at 09:43 AM
When I can't stop.
Lets apply the Econobrowser chart above to the typical medieval agricultural town. We want to decompose the town into the lord mayor and staff, the food industry, and the rest is labor.
The Lord mayor makes a five year bet on net profits for the town in the next trading cycle with towns across the region. The lord mayor takes his bet to the farmers and to the rest of the labor community. Over the next five years he will adjust his bet on a quarterly basis, splitting the deviation from his five year bet with deviations from the other 2 and one year bets. All parties seek to maintain their respective productions functions.
On the chart, then, revisions come back to the lord mayor. annually from the farmer, who gets them quarterly from labor. If the mayor is getting eight updates over the period, and labor is delivering 20, then farming is handling updates in rate fluctuations between 20 and 8, that defines the spectrum of farming.
If modes are reporting periods,
We can define modes, the intercity trading mode, the multi-year farming mode, and quarterly labor mode; then each production function has a three mode spectral definition, plus variance. The variance between the long mode for farmers will be split between the long mode variance for labor and the lord mayor. Mode estimates (eigen values) should be independently normally distributed.
Dividing up the system parameters, thusly, defines the wealth spectrum of the town, and defines an estimate of the block diagonal forms these three production functions might take. The minimizing objective is to allocate production spectrum to minimize quantization error, essentially. In other words, the model chosen delivers the better price estimate/time than a lower or high rank with increased precision..
Posted by: Matt | May 03, 2008 at 12:01 PM