Abstract
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.
Highlights
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 dynamics 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 [8,9,10], economics [11], cultural evolution [12], and physics [13, 14] to computationally model complex, adaptive systems
We provide a novel type of visualization that maps how different search strategies “generate” different landscapes, rather than merely constituting search in an a priori given space. e conceptualization of the fitness landscape is not independent of assumptions about search behavior. is interdependence was not an issue in the original NK model, since it assumed a particular search strategy of genes engaging in local-search with occasional random jumps [6]
Summary
“Solving a problem means representing it so as to make the solution transparent” [1]. 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 organizational activities affects its long-term chances of finding the Complexity optimal solution to a problem and surviving in a competitive environment. Us, rather than quantitatively characterizing problem difficulty (via ruggedness measures such as the K/N ratio), the aim is to categorize fitness landscapes into different types in order to determine the appropriate algorithm to use in a search problem [22]. A K/N ratio cannot capture if a problem should be classified as hierarchical Such hierarchical problems are likely to generate deceptive landscapes (cf Figure 1(c) where the majority of the landscape would steer a local-search hill-climber towards a Complexity. E implementations vary in both details and conclusions regarding the influence of neutrality on the features of the landscape [61], but they do conclusively show that neutrality is an important feature that influences search performance and is not captured by the traditional measures of ruggedness [7] commonly used in NK studies
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