How Culture Can Guide Evolution: An Inquiry into Gene/Meme Enhancement and Opposition

Author:

Best Michael L.1

Affiliation:

1. Media Laboratory, Massachusetts Institute of Technology

Abstract

We study the relationship between genetic evolution, learning, and culture. We start with the sim ulation environment of Hinton and Nowlan in which individual learning was shown to guide genetic evolution towards a difficult adaptive goal. We then consider, in lieu of individual learn ing, culture in the form of social learning by imitation. Our results demonstrate that when genes and culture cooperate, or enhance one another, culture too is able to guide genetic evolution towards an adaptive goal. Further, we show that social learning is superior to individual learning insofar as it with genetic evolution converges more quickly to the goal. However, the social learn ing algorithm results in slower genetic assimilation of adaptive alleles than with individual learn ing. It is as if, we argue, the adaptive values are stored in the culture rather than in the genes. Finally, we consider what happens when culture and genes pursue diametrically opposed goals. Here we show that culture, in the form of social learning, is no real match when opposed to genet ic evolution with individual learning. In fact, only the most herculean of social learning algorithms is able to keep a neutralizing toe-hold against the slow plodding force of genetic evolution. Finally, our results suggest that in both cases, opposition and enhancement, transmission forces such as the ratio of teacher to learner are central to the success of social learning.

Publisher

SAGE Publications

Subject

Behavioral Neuroscience,Experimental and Cognitive Psychology

Reference40 articles.

1. Ackley, D.H. & Littman, M.L. (1994). Altruism in the evolution of communication. In R.A. Books & P Maes (Eds.), Artificial Life IV (pp. 40-48). Cambridge, MA: MIT Press.

2. Baldwin, J.M. (1996). A new factor in evolution. In R.K. Belew & M. Mitchell (Eds.), Adaptive individuals in evolving populations: Models and algorithms. (pp. 59-80). Reading, MA: Addison-Wesley Publishing Company, Inc. (Original work published 1896).

3. An Ecology of Text: Using Text Retrieval to Study Alife on the Net

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