Starting with out assumption of zero transaction costs. Here is a simple example. One day you want the lawn cleaned, rather urgently. Tap the local garden icon with our smart card, put it a bit on red firts, you are urgent. Some kid will show up with garden tools within the hour.
Zero transaction costs, you would rather play tennis. But the pricing ratio works, because the local pit boss is implicitly selling gardening contracts from many kids to many homeowners, locally/ The pit boss is dealing with two queues, a list on incoming contracts to sell for digits, and visa versa. If the probability tree looks like a normal weekend for kids, he the bit error making adjustments to ensure that one yard cleaning cost one set of digits, quantized, and minimizing the extra two block walk it sometimes takes.
In other words, the pit boss finds the common factor, and keeps the residual error over the pricing sequence. The common factor is the smart contract, one typical yard cleaning. Generally, then, the other prices would be integer sized common factors. That is, one can price a yard and a half, getting one unit of shrubbery clipping. The clean up and the clipping become integer dividable. We do the to set container size, in yhis case, ervice containers. A full yard work can be a collection of clipping, mowing and racking, and the collection adequately priced to account for gains to scale. The trading pits look for and adapt to supply chains, the outcome of economies of scale, a flow stability.
Price is the ratio, in sigma, of two queues that are co-compressed. The two queues are the customer and store clerk. The two queues have to be stable. The pit boss does that, and the ratio of sigmas is the outcome. Drop the clerk and customer; replace that with inventory and liquidity events.
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