Hi lupaz,

thanks for your example. The thing is that in our Scatter we are using (or at least trying to use) uniform random distribution during instance generation. :-) In your example you have 8 cube types and 195 instances of them. If I have computed the math correctly for your case (using Markov chains) there is only about 6% probability that there will be NO triplet of same cube types in a row. In other words there is about 94% probability that 3 or more same cube types in a row will appear. Obviously, more instances you create, the latter probability gets increased (195 is already two-degrees larger than 3 and 8).

So from this point of view it is rather Forest Pack that does not behave 'randomly'. It clearly uses some non-uniform distribution. Or you were extremely lucky and hit those 6%, hehe. :-) Anyway, ATM it does not seem to me that our Scatter behaves differently than designed = uniformly. But I get your point and maybe we could offer some other distribution type(s) that might serve certain user scenarios better. I am taking a note for the future. ;-) Thanks.

This reminds of the story about music streaming apps and shuffle playback. Truly random shuffling would mean that you could hear the same song twice in a row, or multiple songs even, which is not what people *expect* from random behavior - similarly, if for example someone was told to put flowers in a garden randomly, he would probably avoid putting the same type of plant next to each other because it wouldn't be random... while in fact it would.

Same here, whenever some random function comes up with the same thing too close to each other I change the seed. Not random enough :D