Not assigning prices out of the gate makes it more obvious that this could be used for things like energy/resource consumption directly. Lately this has been of interest to me.
Do non-equilibrium markets as gradient descent imply that this method could be used to determine in what way the market is out of equilibrium? If the math sticks well, a gradient can contain a lot more information than the lower/higher price expectation I usually see.
It seems like applying this same logic inside the firm should be possible, and so if we use price and apply recursively we could get a fairly detailed picture; I don’t see any reason the gradient trick wouldn’t apply to the labor market as well, for example.
The original piece continues where this post leaves off to discuss how this logic applies inside the firm. The main takeaway there is that most firms do not have competitive internal resource markets, so each part of the company usually optimizes for some imperfect metric. The better those metrics approximate profit in competitive markets, the closer the company comes to maximizing overall profit. This model is harder to quantify, but we can predict that e.g. deep production pipelines will be less efficient than broad pipelines.
I’m still writing the piece on non-equilibrium markets. The information we get on how the market is out of equilibrium is rather odd, and doesn’t neatly map to any other algorithm I know. The closest analogue would be message-passing algorithms for updating a Bayes net when new data comes in, but that analogy is more aesthetic than formal.
The information we get on how the market is out of equilibrium is rather odd, and doesn’t neatly map to any other algorithm I know
I don’t want to put the cart before the horse or anything, but this increases my expectation that the information is valuable, rather than decreases it.
There are a few things I like about this.
Not assigning prices out of the gate makes it more obvious that this could be used for things like energy/resource consumption directly. Lately this has been of interest to me.
Do non-equilibrium markets as gradient descent imply that this method could be used to determine in what way the market is out of equilibrium? If the math sticks well, a gradient can contain a lot more information than the lower/higher price expectation I usually see.
It seems like applying this same logic inside the firm should be possible, and so if we use price and apply recursively we could get a fairly detailed picture; I don’t see any reason the gradient trick wouldn’t apply to the labor market as well, for example.
The original piece continues where this post leaves off to discuss how this logic applies inside the firm. The main takeaway there is that most firms do not have competitive internal resource markets, so each part of the company usually optimizes for some imperfect metric. The better those metrics approximate profit in competitive markets, the closer the company comes to maximizing overall profit. This model is harder to quantify, but we can predict that e.g. deep production pipelines will be less efficient than broad pipelines.
I’m still writing the piece on non-equilibrium markets. The information we get on how the market is out of equilibrium is rather odd, and doesn’t neatly map to any other algorithm I know. The closest analogue would be message-passing algorithms for updating a Bayes net when new data comes in, but that analogy is more aesthetic than formal.
I don’t want to put the cart before the horse or anything, but this increases my expectation that the information is valuable, rather than decreases it.