title: Meme and Variations: A Computational Model of Cultural Evolution
creator: Gabora, L.
subject: Evolution
subject: Population Biology
subject: Theoretical Biology
subject: Cognitive Psychology
subject: Artificial Intelligence
subject: Complexity Theory
subject: Dynamical Systems
subject: Machine Learning
subject: Neural Nets
subject: Evolutionary Psychology
subject: Epistemology
subject: Social Psychology
description: 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.
publisher: Addison Wesley
contributor: Nadel, Lynn
contributor: Stein, Daniel L.
date: 1995
type: Book Chapter
type: NonPeerReviewed
format: text/html
identifier: http://cogprints.org/531/1/mav.htm
identifier:   Gabora, L.  (1995) Meme and Variations: A Computational Model of Cultural Evolution.  [Book Chapter]     
relation: http://cogprints.org/531/