Research Article HumanSearchinaFitnessLandscape:HowtoAssessthe DifficultyofaSearchProblem OanaVuculescu, 1 MadsKockPedersen , 2 JacobF.Sherson , 2 andCarstenBergenholtz 1 1 Department of Management, Aarhus University, Aarhus, Denmark 2 ScienceAtHome, Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark Correspondence should be addressed to Mads Kock Pedersen; madskock@phys.au.dk Received 29 January 2020; Revised 1 June 2020; Accepted 20 June 2020; Published 15 July 2020 Academic Editor: Marcio Eisencraft Copyright © 2020 Oana Vuculescu et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational modeling is widely used to study how humans and organizations search and solve problems in fields such as economics, management, cultural evolution, and computer science. We argue that current computational modeling research on human problem-solving needs to address several fundamental issues in order to generate more meaningful and falsifiable contributions. Based on comparative simulations and a new type of visualization of how to assess the nature of the fitness landscape, we address two key assumptions that approaches such as the NK framework rely on: that the NK captures the continuum of the complexity of empirical fitness landscapes and that search behavior is a distinct component, independent from the topology of the fitness landscape. We show 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 landscape is fundamentally intertwined with search behavior. Finally, we outline broader implications for how to simulate problem-solving. 1.Introduction “Solving a problem simply means representing it so as to make the solution transparent” [1]. ere is a long tradition of studying how to search for solutions to “hard” problems, i.e., problems where it is computationally impossible or merely too expensive to list and test all possible solutions [2, 3]. e prevalent way of addressing individual or organizational search behavior and how to conceptualize the space of solutions stems from early work on population genetics, namely, the fitness landscape model [4]. By focusing on fitness interactions between genes, Wright’s framework allows for a link between low-level properties of genes and the high-level patterns of the dy- namics of evolution [5]. e model’s most famous extension, the NK model [6], explicitly models adaptive evolution as a “search in protein space” [6] which tries to find a maximum point for a chosen fitness function. is approach has grown outside the boundaries of population genetics literature and inspired a series of scholars from computer science [7], organizational theory [8–10], economics [11], cultural evolution [12], and physics [13, 14] to computationally model complex, adaptive systems. How can problem-solving be modeled in this frame- work? Imagine trying to solve an innovation problem, for instance, designing a new educational app. e app will likely be based on predefined libraries, which constitute interconnected modules; changing something in one module might influence the functionality of another module. e extent of this interdependence will differ from envi- ronment to environment and influence how one should search for the good design. Sometimes changing one small element at a time (“local-search”) might be efficient, while in other environments the level of interdependency might make this approach inefficient. Levinthal [15] introduced the NK model to the social science literature in order to facilitate formal modeling and simulation of how the level of interdependence of organi- zational activities affects its long-term chances of finding the Hindawi Complexity Volume 2020, Article ID 7802169, 11 pages https://doi.org/10.1155/2020/7802169