For each node in the generation, assign two parental nodes from the preceding generation by a power law for shortest distance. Iterate back to step 2.
Shortest distance is computed between nodes each time each time parental nodes are assigned.
The power law for distance is simulated for each node by choosing one parental node randomly, and then choosing the other parent node according to the probability distribution of the power law, which selects the distance. Hence, once the next node is chosen and its first parental node is assigned we do not need to recompute all the distances to other potential parent nodes but we simply draw the parental distance from the power law distribution and proceed as follows. Procede randomly from the node to any of its ancestors n generations above, then proceed randomly downward to any node n generations below this ancestor. Then we can show simulation results at various populations, generational depth, and parameters of the power law. As a later refinement of this model, we introduce a class-differentiation model.