The literature on exploration and learning in principle seeks to understand the behavior of actors in light of both their cognitive faculties and the environments they search. However, surprisingly little is known about the structure of the environment in the workhorse model, the NK fitness landscape. This model has been used to study the behavioral implications of ruggedness---the existences of many local fitness optima---, but we show that the parameter that tunes ruggedness, the eponymous K, also alters several features of the landscape that have relevance for behavior. This calls into question whether ruggedness is indeed the cause of the comparatively poor performance outcomes documented in the literature. We propose the Dirichlet Dot Product landscape to isolate the role of ruggedness. We document a complex relationship between ruggedness and the other characteristics and, in a pre-registered human-subject experiment, show that search on highly rugged Dirichlet Dot Product landscapes is as effective as search on single-peaked NK landscapes. Successful search behavior is compatible with rugged landscapes, suggesting human actors are not hopelessly myopic and in fact do well at apprehending the broader structure of the problem space. This finding suggests a need for more research on the macroscopic structure of decision-making environments and for revisiting the putative cognitive implications of the NK model.
