 The Hong-Page result is therefore very sensitive to the “smoothness” of the epistemic landscape modeled. As hinted in  section 3.2.2,  this is an indication from within the modeling tradition itself of the danger of restricted and over-simple abstractions regarding epistemic landscapes. Moreover, the model’s sensitivity is not limited to landscape smoothness: social epistemic success depends on the pool of numbers from which heuristics are drawn as well, with “diversity” showing strength on smoother landscapes if the pool of heuristics is expanded. Results also depend on whether social interaction is modeled using of Hong-Page’s “relay” or an alternative dynamics in which individuals collectively (rather than sequentially) announce their results, with all moving to the highest point announced by any. Different landscape smoothnesses, different heuristic pool sizes, and different interactive dynamics will favor the epistemic advantages of different compositions of groups, with different proportions of random and best-performing individuals (Grim et al. 2019).