Huffman encode state GDP numbers, for example

A Hufman ncoder removes redundancy in  a sequence of numbers.  In state GDP  numbers, California removes 1/6 of the redundancy, but appears at the one out of fifty times.  The small states,m whichremove 1/6, but appear 1/3 of the time.  The Huffman compression  algorithm allocates codes, long codes for innovative numbers, small codes for the less significant.  The total number of bits needed to encode the sequence is less than  the original, when redundancies removed.

OK, here is a theoretical key.  There is a decoding network, and path length is significance.  California gets more of that decoding graph pace than the some small  number of states., small states  compressed. That decoding network is also the logistical minimum network to deliver state GDPs, it minimizes transaction costs.  As a model of government goods of  constrained flow, one can see the problem; the optimum logistical map is a far distance from  the Senate map.

The Huffman encoder can be made as more precise as long as  a lengthening data window introduces redundancy, to be compressed.   Huffman is useful, for example;  locate the significant stocks in an index; compute the  significance of chicken feed to omlet prices. Any place where accounting can be done on an isolated channel that maps into a constrained flow. Its like Ito's calculus for spreadsheets.

Another clue.
The decodin g network is a balanced queuing network, each node being equally active. So finite Poisson applies, and I have no idea, I retired.  But Huffman does that, and a data geek should jump all over that tool.

One can imagine the betting bot runs a moving window Huffman tree.  The bot settles bets in any branch where the queue grows beyond the bound.

The state GDP better

It has a window large and a multiple of 50.  So, it collects the complete window and generates the most probable GDP, a compressed value. In the margin  of the compression, bets have small change on the line, that' where the game is played.  The exact compression map is not known until the precision bound  is broken. The tree rebalanced, and small change becomes integer change, a pay off, or pay out.  The closer you bet to the new decoded value, the more you earn.  A gaussian payout across each bin  seems what happens, but go talk to the pros.

The better bot could declare a specific precision, the number of balanced, decoded leaves on the decoder tree.  So betters spread across the GDP range waiting for the encoded GDP to define the boundaries. The GDP  numbers co e out, the bot Huffman encodes them from  50 numbers to 27 bins. Once the tree computed, the 27 ocunts travels down the tree and splits with the betters in the bin. So the bot may very well lose money when  insiders innovate, that is great, that is the idea.  The bot shares in  the risk of tree rebalancing.