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Landscape Revolutions for Cultural Evolution: Integrating Advanced Fitness Landscapes into the Study of Cultural Change

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Handbook of Evolutionary Research in Archaeology

Abstract

The use of fitness landscapes as models and metaphors is increasingly common in the study of cultural evolution. Fitness landscapes used in this context are often based on imagery developed by Sewall Wright and depict a rugged topography of high-fitness peaks and low-fitness valleys. These landscape metaphors and models are used to explain both microscale changes in individual cultural forms and macroscale cultural transitions and diversifications. Recent research on the structure and dynamics of fitness landscapes, however, suggests that in many cases these rugged fitness landscapes may be misleading as they give rise to explanations that highlight the role of selection in directing cultural change and necessitate engagement with the problem of “peak shifting” in macroevolution. Advanced landscape theory suggests that realistic fitness landscapes may be highly multidimensional in nature, giving rise to a topography that includes neutral plateaus, ridges, and networks between peaks. This indicates that major cultural transitions as well as much microevolution may occur solely through neutral or nearly neutral changes, driven by drift rather than selection. We suggest that these advances in fitness landscape research may provide scholars of cultural change with powerful new imagery for theorizing patterns and processes in cultural evolution.

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Notes

  1. 1.

    Much less frequently they represent real data linked to a formal set of mathematics or equations; although with the rise in gene sequencing data, empirical fitness landscapes are becoming increasingly common (De Visser and Krug 2014; Poelwijk et al. 2007).

  2. 2.

    Each type of landscape is privy to a unique set of theoretical and methodological issues (for an exhaustive review, see Kaplan 2008). While detailing all of these is outside the purview of this paper, we note that the types of landscapes most often relied upon by cultural evolutionists, phenotypic adaptive and phenotypic fitness landscapes, are no exception. In the case of adaptive landscapes, the assumption is made that mean population fitness can be mapped onto frequencies (rather than distributions) of traits within a population (Kaplan 2008, p. 628), ignoring the fact that major evolutionary movement on landscapes, guided by selection, is dependent on the differential reproductive success of the individuals traversing it. For this reason, adaptive landscapes should be viewed as descriptive, rather than explanatory of the evolutionary process, and scholars of cultural evolution should use appropriate terminology to reflect this. In the case of fitness landscapes, there are often problems of accurate representation, for example, there is an inherent inability to meaningfully situate individual, discrete genotypes along a continuous axis, a problem that has now been widely commented on in the literature (Calcott 2008). There is also the issue of constructing a fitness function that provides a meaningful quantification of adaptive success with often limited knowledge of the genotype and population structure necessary to map genotypes to individual fitness values (in the case of genotypic fitness landscapes) or genotypes to phenotypes to fitness values (in the case of phenotypic fitness landscapes) (Pigliucci 2012; Pigliucci and Kaplan 2006, p. 185).

  3. 3.

    Fitnesses on non-flat landscapes are assigned in a number of ways, including completely randomly, manually, or based on more complex functions. While an exhaustive account of all of these is impossible here we provide an example of landscape correlation in order to demonstrate the degree to which the process of fitness assignment impacts the topographical structure of the landscape. Uncorrelated fitness landscapes result from the random assignment of fitnesses to configurations on the space. In other words, on an uncorrelated landscape knowing the fitness of one configuration will reveal nothing about the fitness of its one-step neighbors. For example, on the well-studied NK landscape model evolutionary relationships between configurations depend on the interaction between traits (epistasis). The NK model has two parameters, N and K, where N is the number of traits and K is the number of interactions between traits and the K parameter determines the ruggedness of the fitness landscape. The K = N−1 landscape is an example of an entirely uncorrelated landscape space that is extremely rugged, with numerous peaks that are low and steep. The K = 0 landscape is highly correlated and knowing the fitness of one configuration does provide some information about the fitness of each of its one-step neighbors. On this landscape, the surface is smooth and there is a single, high peak with gradually increasing sides.

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Acknowledgments

The authors would like to thank Stephen Shennan and Ian Kuijt for their helpful comments on the manuscript. We would also like to thank George Lesica, Matthew Walsh, Michelle Grocke, Anna Prentiss, and Randy Skelton for advice, support, ideas, and editorial assistance. Cheyenne Laue would like to thank the University of Montana Department of Anthropology, PEO, and AAUW for providing financial support during the writing of the manuscript. We assume full responsibility for all positions taken.Data Sharing Statement Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. Department of Anthropology, University of Montana, Missoula, MT, USA

    Cheyenne L. Laue

  2. Department of Computing Science, University of Montana, Missoula, MT, USA

    Alden H. Wright

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Laue, C.L., Wright, A.H. (2019). Landscape Revolutions for Cultural Evolution: Integrating Advanced Fitness Landscapes into the Study of Cultural Change. In: Prentiss, A. (eds) Handbook of Evolutionary Research in Archaeology. Springer, Cham. https://doi.org/10.1007/978-3-030-11117-5_7

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