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To assess the effectiveness of a simulation-based approach to linguistic category evolution, a language evolution model is applied to the natural color categorizations of 108 linguistic communities from the World Color Survey (2009). This dynamic model is derived from Komarova et al. [J. Math. Psychol.51, 359 (2007)JMTPAJ0022-249610.1016/j.jmp.2007.06.001] and evolves color-naming systems to a stable equilibrium through agent interactions. This simulation-based approach remedies the sparseness of empirical, diachronic data by broadly approximating evolutionary trends in an idealized form. Additionally, we present novel explorations of evaluating equilibrium stability and determining category boundaries. Results show that all 108 systems evolve to a stable equilibrium while maintaining key features of the original categorizations, suggesting that our simulations are a suitable representation of natural evolutionary processes. This approach can have valuable insights and implications for research where diachronic data are sparse. For example, on the linguistic debate between the Partition Hypothesis and an Alternative Hypothesis that asserts categories evolve by expanding their boundaries from fixed best exemplars, our analysis finds that the Alternative Hypothesis best explains the observed changes in the simulations of the World Color Survey language populations.

Further evolution of natural categorization systems: an approach to evolving color concepts