The one Kling put up.
I have two channels, defined by the wage flow from multiple wage groups. After the crash, a powerful group had higher entropy (greater precision) then lower power group. In fact, for a moment, the high power wage flow swamped the available wage to the entropy limit. We can look time dependent or time dependent. I want to set a noise level, then using some reasonable window, set the scale values in x and y for both groups to meet the SNR. Those scale values are my bounded functions. Watch as they change prior to and farther along after the crash.
Higher entropy means the wages are ore informative about skills and capability. So this implies more economic interest in these people at the moment.
Under entanglement, the rapid changes in wage entropy measures have associated entropy changes in real inventory quants. So look at consumer purchases too.
Constant SNR implies stability. Test for two stable wage points, maybe three. The spiral effect suggests that the low power group would be losing TV, new car, at more frequent intervals via inventory lengthening. They would consumption rank in the minimum number of steps, spiral down.
Do the entropy measure blind by taking BEA data by occupation and measured wage. In fact, make all the data into a format that allows us to match polynomials easily over any dimensions.
I do add updates to the entropy page, and there is a good one on maximum entropy methods.
And my own entanglement bias. You will find spirals in the data, so we still need an aggregate relative measure.
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