David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In [Book Chapter] (1995)
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.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Robert Aunger (1998). The “Core Meme” Meme. Behavioral and Brain Sciences 21 (4):569-570.
Dan Sperber (1998). Are Folk Taxonomies “Memes”? Behavioral and Brain Sciences 21 (4):589-590.
Matt Gers (2008). The Case for Memes. Biological Theory 3 (4):305-315.
Mark Greenberg (2004). Goals Versus Memes: Explanation in the Theory of Cultural Evolution. In Susan L. Hurley & Nick Chater (eds.), Perspectives on Imitation. MIT Press.
Added to index2009-01-28
Total downloads12 ( #120,937 of 1,096,453 )
Recent downloads (6 months)1 ( #231,754 of 1,096,453 )
How can I increase my downloads?