TY - GEN ID - cogprints531 UR - http://cogprints.org/531/ A1 - Gabora, L. Y1 - 1995/// N2 - 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. PB - Addison Wesley KW - adaptation KW - artificial society KW - computational anthropology KW - creativity KW - culture KW - cultural evolution KW - cultural learning KW - diversity KW - drift KW - drives KW - epistasis KW - embodiment KW - evolution KW - fitness KW - imitation KW - innovation KW - Lamark KW - meme KW - memetic algorithm KW - memetic evolution KW - memory KW - mental simulation KW - mutation KW - neural network KW - optimization KW - overdominance KW - replication KW - selection KW - social learning KW - transmission KW - underdominance. TI - Meme and Variations: A Computational Model of Cultural Evolution SP - 471 AV - public EP - 485 ER -