Abstract

This paper describes a computational model of how ideas, or memes, evolve through the processes of variation, selection, and replication. Every iteration, each neural-network based agent in an artificial society has the opportunity to acquire a new meme, either through 1) INNOVATION, by mutating a previously-learned meme, or 2) IMITATION, by copying a meme performed by a neighbor. Imitation, mental simulation, and using past experience to bias mutation all increase the rate at which fitter memes evolve. Memes at epistatic loci converged more slowly than memes at over- or underdominant loci. The higher the ratio of innovation to imitation, the greater the meme diversity, and the higher the fitness of the fittest meme. Optimization is fastest for the society as a whole with an innovation to imitation ratio of 2:1, but diversity is comprimized.