jump when including the effect of (local) accessibility $d_4$ in the simulations, i.e. when transitioning from $\alpha_4=0$ to $\alpha_4>0$. In particular, the more activities were influent, the denser the city became—a nonintuitive emergent effect, compared to top-down planning alternatives that would try to optimize accessibility while keeping density low. Speed, or rather “sluggishness”, exploded when density was the only influence on urban sprawl: this confirmed that pure density-driven dynamics creates anarchic growth, without concern for network performance.

As for global accessibility, or rather the difficulty thereof, it was minimal for $\alpha_4=0$ : an interesting paradoxical effect suggesting that when individual agents took into account local accessibility ($\alpha_4>0$), a few of them might have occupied the “best spots” too quickly, significantly diminishing the others’ prospects. Therefore, at the collective level, it would be better for everyone to ignore that dimension—an example where competition at the individual level does not produce the most efficient system for all. Finally, no meaningful conclusion could be formulated about the chaotic variations of Moran’s index, except for its extreme sensitivity to spatial structure.

![Sample surface plots of the evaluation functions. For each 4D field of evaluation values in the hypercube, we select two out of four 