Merging pieces and tags mentioned in a previous post have gone much smoother than I expected, and it's finished now. I also wrote in that post my estimates how long a game in the largest variant could take. In the meantime I have revised my estimates, and so I present you with bigger, better numbers. Again, these are very rough guesstimates, it's difficult to even assess how much numbers presented here could deviate from real-world matches; in short, those shouldn't be taken too seriously.
Estimates here are based on the fact that most large, complex systems settle its metrics somewhat in the middle, since most extremes tend to cancel each other out. For this analysis we'll be comparing easy to calculate metrics such as:
- size of a chessboards,
- count of all pieces in a variant,
- number of different types of pieces,
- number of possible interactions,
All complexity factors have the same form, so let's define generic complexity factor as a simple ratio between new () and old () metrics, like so:
This definition is all well and good, but applies only if corresponding metrics contribute to system's complexity directly, in a linear fashion. Most of the time, this is not the case. For instance, adding two ranks and files to a classical chessboard is a much larger change (as a percentage) then adding the same to the second largest variant, Discovery. So, each increase in metrics yields diminishing increase in complexity; for such a non-linear increase there is a function which sets limits to growth, and that's natural logarithm ():
There is still a small issue to solve here, before calculations can take place. Observe what happens if we compare e.g. Classical Chess with its own self:
Our complexity factor gets to 0. This is actually fine, all that calculation is showing us is that there is no additional complexity when comparing a variant to itself. Still, we'd like to multiply our complexity factors, as independent variables should be. So, we'll add 1 to formulae, like so:
Now that we have generic formulae for complexity factors sorted out, we can actually calculate something; lets start by comparing sizes of chessboards:
Next, we can compare total number of pieces on the chessboards:
Another comparison is between number of different types of pieces:
Finally, we can compare number of different interactions:
Our complexity is then defined as a product of all factors calculated above:
This is actual length scaling factor of regular games from Classical Chess into One variant, i.e. all games should be 55.767 times longer. For instance, the longest recorded tournament game was 538 moves (269 FIDE moves, aka cycles), which turns into 30002 moves (15001 cycles) for One variant. Average on-line match lasts about 80 moves (40 cycles), in One variant that would become 4461 moves (2230.5 cycles). Average tournament match lasts about 88 moves (44 cycles), which becomes 4907 moves (2453.5 cycles).
The same, however, does not apply when calculating maximal possible game length, because players will try to maximize each and every metrics available to prolong the game. This can be seen in Classical Chess games alone; the longest possible game with 50-cycle rule is 11797 moves (5898.5 cycles), while with 75-cycle rule it's 17697 moves (8848.5 cycles). If we calculate ratio between the two rules:
and game lengths, we can see that contribution to game length by rules extension was almost perfectly linear (50% increase in movement rule resulted in 50% longer game):
So, for maximum game length we have to calculate linear scaling factors; for chessboard sizes factor becomes:
Next, for total number of pieces on the chessboards we have:
For number of different types of pieces we get:
Finally, we can calculate factor for number of different interactions:
Taken together, our complexity becomes:
This is scaling factor for the longest possible games, i.e. the longest games should be 1191.58 times longer in One variant compared to Classical Chess. For instance, previously mentioned 11797 moves (5898.5 cycles) game as the longest possible with 50-cycle rule in One variant becomes 14,057,093 moves (7,028,546.5 cycles) game. Even longer 17697 moves (8848.5 cycles) game with 75-cycle rule in One variant turns into 21,087,427 moves (10,543,713.5 cycles) game.
These are all very large numbers, even "just" 55.767 times increase in complexity results in some ludicrous (as in, almost completely impractical) estimates. For instance, increase in complexity should also translate into longer time allowance per turn, simply because there is so much more stuff a player has to handle. So, 15 seconds per player's turn in bullet game (10 minutes, spread over average of 40 turns per match, see https://en.wikipedia.org/wiki/Time_control#Classification) now becomes approx. 13.941776 minutes per turn; given that average One game length would be 4461 turns, it would last for approx. 1036.5710456 hours of gameplay time; or, 129.5713807 days if we assume 8-hour gameplay in a day. If we don't increase time allowance per turn, 15 second per turn bullet game would take on average 18.5875 hours of gameplay time, or 2.3234375 days with the same 8 hours of gameplay per day.
And that's just bullet, lets not talk about classical time controls here, those numbers would be depressingly huge. One thing that is sorely missing from complexity estimate is mobility, and associated with it, piece powers. These are not easily estimated; also, increase in mobility alone does not add to complexity, rather it's ratio between mobility and available space (i.e. chessboard size), and I'm not sure that ratio has been increased by much. Anyway, this post is already too long, so I'll leave it for some future post.