Explaining Color Term Typology as the Product of Cultural Evolution using a Bayesian Multi-agent Model

An expression-induction model was used to simulate the evolution of basic color terms in order to test Berlin and Kay’s (1969) hypothesis that the typological patterns observed in basic color term systems are produced by a process of cultural evolution under the influence of universal aspects of human neurophysiology. Ten agents were simulated, each of which could learn color term denotations by generalizing from examples using Bayesian inference. Conversations between these agents, in which agents would learn from one-another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of color terms of each type correlated closely with the equivalent frequencies found in the world color survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over