[voidmain]

I think I can solve your puzzle.

First assign the number of potential aunts and nephews in each continent, city, etc top down in your tree (ensuring that the numbers are equal at the top and that you can reversibly assign a compact integer to each aunt and to each nephew). Then you need an invertible pseudorandom permutation of the right size P, i.e P(P^-1(x))=x for 0<=x<N. For nephew n the aunt is a=P(n) and you can walk down the tree to generate the details of the entity which is aunt a. If you start with the aunt you just use P^-1.

Cryptography has already developed families of functions P. For N=2^128, AES is such a family. For non power of two N, you want to look at "format-preserving encryption". For example you can use a Feistel network to permute the next larger power of two and simply iterate that permutation until you get a result in range.

I think you could extend this primitive to nonuniform graphs (aunt more likely in the same city) and more kinds of relationships. You can also use rejection sampling to condition the relationships further and probably to deal with the top down population assignment being approximate.

All that said, I think procedural generation currently runs out of creativity long before you would run out of memory to store explicit relationship indices. I think it's a lot more hopeful to get more creativity out of generative AI while keeping things grounded by explicitly representing the world, even if it "doesn't scale" to creating trillions of galaxies.

[vintermann]

Thanks for the suggestions! I did indeed try to employ a couple of "odd" cryptographic primitives when I played around with this.

Yes, it's probably a good idea to keep track of in- and outflows, in higher nodes. The higher node could have a parameter saying something about the number of aunt-nephew relationships (or any kind of relationship) that cross continent borders. Those parameters, too, could get generated on demand.