EXPERIMENT 14-b:

While Experiment 14-a was running, I wondered how the same mutant would do
against GnuBG 4-ply and started a session of 1,000 games on another computer.

The intervals for five stages of a game, double points, take points, beaver points
and the too good point are all the same as in Experiment 14-a.

After 1,000 cubeful games, the bot won 1,817 (52.20%) and the mutant won 1,664
(47.80%) points, resulting in 153 less points, i.e. ony -0.1530 ppg, for the mutant.

Compare these to the 38,717 (52.54%) for the bot 34,979 (47.46%) for the mutant
in Experiment 14-a against GnuBG 3-ply. Again, quite astoundingly the mutant did
even better against GnuBG 4-ply.

Results in numbers of games won were almost even again and almost exactly the
same with 509 (50.60%) for the bot and 494 (49.40%) for the mutant, compared to
10,124 (50.62%) and 9,876 (49.38%) respectively in Experiment 14-a.

Pretty amazing, isn't it? I certainly never expected such results and again I believe
that I delivered more than my promise to eventually topple the gamblegammon
world upside down by demonstrating  that the so-called "cube skill theory" is just
a myth-ematical fantasy.

I only ran 1,000 games because even that took more than four days on my spare
computer. Yet, I think it's enough to give an idea considering the minimal amount
luck fluctuations. But, please don't take my word for anything. Run as many trials
as it would take to convince yourselves. After all, that's why I'm sharing my Python
scripts openly.

This experiment, does however, raise a question about almost no difference in the
mutant's performances against 2-ply, 3-ply and 4-ply. I will have to do a separate
test to look into this. (See Experiment 15).