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Computational modeling is widely used to study how humans and organizationssearch and solve problems in fields such as economics, management, culturalevolution, and computer science. We argue that current computational modelingresearch on human problem-solving needs to address several fundamental issuesin order to generate more meaningful and falsifiable contributions. Based oncomparative simulations and a new type of visualization of how to assess thenature of the fitness landscape, we address two key assumptions that approachessuch as the NK framework rely on: that the NK captures the continuum of thecomplexity of empirical fitness landscapes, and that search behavior is adistinct component, independent from the topology of the fitness landscape. Weshow the limitations of the most common approach to conceptualize how complex,or rugged, a landscape is, as well as how the nature of the fitness landscapeis fundamentally intertwined with search behavior. Finally, we outline broaderimplications for how to simulate problem-solving.

Human search in a fitness landscape: How to assess the difficulty of a search problem