This study presents a new model of search on a rugged landscape, which employs modeling techniques from fractal geometry rather than the now-familiar NK modeling technique. In our simulations, firms search locally in a two-dimensional fitness landscape, choosing moves in a way that responds both to local payoff considerations and to a more global sense of opportunity represented by a firm-specific preferred direction. The latter concept provides a very simple device for introducing cognitive or motivational considerations into the formal account of search behavior, alongside payoff considerations. After describing the objectives and the structure of the model, we report a first experiment which explores how the ruggedness of the landscape affects the interplay of local payoff and cognitive considerations (preferred direction) in search. We show that an intermediate search strategy, combining the guidance of local search with a moderate level of non-local obsession, is distinctly advantageous in searching a rugged landscape. We also explore the effects of other considerations, including the objective validity of the preferred direction and the degree of dispersion of firm strategies. We conclude by noting available features of the model that are not exercised in this experiment. Given the inherent flexibility of the model, the range of questions that might potentially be explored is extremely large.
Rugged Landscapes Local Search Cognition Obsession Fractal Geometry