 Extensions of these results appear in Grim, Singer et al. (2013). There a small sample of landscapes is replaced with a quantified “fiendishness index”, roughly representing the extent to which a landscape embodies a “needle in a haystack”. Higher fiendishness quantifies a lower probability that hill-climbing from a randomly chosen point “finds” the landscape’s global maximum. Landscapes, though still two-dimensional, are “looped” so as to avoid edge-effects also noted in Hegselmann and Krause (2006). Here again results emphasize the epistemic advantages of ring-like or distributed network over fully connected networks in the exploration of intuitively difficult epistemic landscapes. Distributed single rings achieve the highest percentage of cases in which the highest point on the landscape is found, followed by all other network configurations. Total or completely connected networks show the worst results over all. Times to convergence are shown to be roughly though not precisely the inverse of these relationships. See  the interactive simulation of a Grim and Singer et al.’s model  in the Other Internet Resources section below.