Relating Magic Walrus to the queuing problem

Magic Walrus is like an upper bound on performance if all parties met their bandwidth requirements, (they had the liquidity to cover their discrete transactions).  How does this relate t the queueing problem?

Let us use Kling's GDP factory model and convert it to a GDP store. In the queuing model, we have trucks dropping off HDP goods in the back and customers buying them in the front.  My claim is that when supply equals demand, there are zero or one trucks in the back and one or two customers in the front.  At that condition, two transactions are pending for each completed movement of one discrete set of goods. One and a half a the front and one half at the back.  Shannon bandwidth requirements are met and prices stable. We are at Magic Walrus.

But the Shannon lock does not include price discovery, the process of quantization. It is the paradox, there has to be some Hawking radiation to find the quants.   So we slightly underperform, testing the boundaries of price such that we end up with about 1.5 transactions for completed transition across the layers.  We have Hawking's radiation because we have a bit of congestion in the store, it is called tradebook uncertainty.  The systems cannot know what is in the shopping baskets until the baskets get to the proper checkout lane.  The shortages and overflows that appear due to undersampling cause price resets. Equivalence tells us that all the shortages and underflows are observable by watching the queues, when two trucks show up or when the checkout line is empty or overflowing.

Hawking radiation is the menu costs, the cost of maintaining the container algebra.