The market is a controlled dynamic system. - page 153
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Let's pose a question:
Is it possible to determine from the available output data what was the control signal that led to these actual data?
In terms of physics :
For 2 systems ("many potatoes and water - intensive heating mode" and "few potatoes and water - low heating mode") a ratio of "weight - mode" was selected, so that the temperature change during heating will be near the points of the above chart.
The question turns out even wider - how to find out how much potatoes we have and at what cooking mode all happens if there is no cooling curve?
In terms of the market:
how many potatoes do we have? What does this value mean and is it constant?
Is it possible to determine from the available output data what the control signal that led to this actual data was?
I think not, you can't. There is no limit - it turns out the control signal was heating the potato.
Is it possible to determine from the available output data what was the control signal that led to this actual data?
I think not, you can't. There is no limit - it turns out the control signal was heating the potato.
You can, but not from a run-up.
I would like to hear from transport manager Oleg - how he would do it.
I don't think so, you can't. There is no limit - it turns out the control signal was heating the potato.
You can, but in general not always accurately. It depends on what the transfer function of the system is. In the example described, you can.
You just have to keep in mind that a stable system becomes unstable when the problem is reversed, roughly speaking the poles of the transfer function become zeros and the zeros become poles. Therefore regularisation is usually needed to solve the inverse problem.
Sooner or later, you'll just have to bring the conversation precisely to finding an optimum in the definition of the control signal...;)
In our problem (the market problem, not the potato problem), the very definition of what an optimum is is a non-trivial task. Once we have defined this definition, finding the optimum becomes a technical problem.
In our problem (the market problem, not the potato problem) the very definition of what an optimum is is a non-trivial task. Once we are defined with such a definition, the search for an optimum becomes a technical problem.
alsu has already given hints on what criteria this optimum is searched for, when he posted his flowchart (about the market).
Another hint was given by Alexey in the post above.
Yes, the problem is not trivial and not everyone can do it.
SZY: Actually, you know yourself without any hints.
In terms of physics :
For 2 systems ("many potatoes and water - intensive heating mode" and "few potatoes and water - low heating mode") a certain "weight - mode" ratio is selected, so that the temperature change during heating will be near the points of the above chart.
The question turns out even wider - how to find out how much potatoes we have and at what cooking mode all happens if there is no cooling curve?
In terms of the market:
how many potatoes do we have? What does this value mean and is it constant?
But you try within the framework of the example given.
(and don't ask too many questions - you can add a hundred more to the ones you found - time of year, time of day, phase of the moon.... -- all of which affect the outcome, mind you. )
You can, but in general not always accurately. It depends on what the transfer function of the system is. In the example described, you can.
One should only keep in mind that a stable system at reversal of the problem turns into an unstable one, roughly speaking, poles of the transfer function become zeros and zeros become poles. Therefore to solve the inverse problem as a rule a regularisation is needed.
Everything is fine if the system is linear, i.e. if the superposition principle is satisfied. But even in the simplest problem with potatoes there is a non-linearity in the form of restriction (water is heated to a temperature not exceeding 100 degrees). And so by simple inversion of PF we can only approach the solution in the linearity regions. In the non-linear areas there is indeterminacy. Note: uncertainty not as randomness, but uncertainty as multivariance.
For our market problem, however, such a straightforward solution is unacceptable. Or, less categorically, it may be acceptable as a first approximation.
Everything is fine if the system is linear, i.e. if the superposition principle is observed. But even in the simplest problem with potatoes there is a non-linearity in the form of a constraint (water is heated to a temperature not exceeding 100 degrees). And so by simple inversion of PF we can only approach the solution in the linearity regions. In the non-linear areas there is indeterminacy. Note: uncertainty not as randomness, but uncertainty as multivariance.
For our market problem, however, such a straightforward solution is unacceptable. Or, less categorically, it may be acceptable as a first approximation.
Everything is fine if the system is linear, i.e. if the superposition principle is observed. But even in the simplest problem with potatoes there is a non-linearity in the form of a constraint (water is heated to a temperature not exceeding 100 degrees). And so by simple inversion of PF we can only approach the solution in the linearity regions. In the non-linear areas there is indeterminacy. Note: uncertainty not as randomness, but uncertainty as multivariance.
For our market problem, however, such a straightforward solution is unacceptable. Or, less categorically, it may be acceptable as a first approximation.
This is the point