urban configurations in this plane allowed a better understanding and comparison of their features and growth process. Again, for certain corner parameter values (all of them 0 except one or two at 1), the results ended up in distinct locations on the map, which could be relatively well explained. Intermediate combinations of parameters, however, seemed to project the structures quite literally “all over the map”, which might be interpreted as the emergence of chaos in the system.

![Statistical distribution of the output evaluations. For each of the 15 corner points of the 4D hypercube of $\alpha_k$’s (excluding the origin), we ran 500 simulations from random initializations of 4 city centers $C_0$. Three resulting distributions out of these 15 are displayed, each in the form of a histogram of evaluation function values, $D$, $S$, $I$, and $A$, fitted with a Gaussian curve. *Green*: $(\alpha_k)=(1,0,0,0)$, i.e. a simulation taking into account only the density $d_1$. *Yellow*: $(0,1,0,0)$, i.e. Euclidean distance $d_2$ only. *Red*: $(0,0,0,1)$, i.e. accessibility $d_4$ only. These three histograms were chosen for their minimum overlap and clarity of display; the other 17 are similar. The narrow peaks (except one), spread about the mean by $\pm10\%$, attest to the low sensitivity 