Convergence of knowledge in a cultural evolution model with population structure, random social learning and credibility biases
Abstract
Understanding how knowledge is created and propagates within groups is crucial to explain how human populations have evolved through time. Anthropologists have relied on different theoretical models to address this question. In this work, we introduce a mathematically oriented model that shares properties with individual based approaches, inhomogeneous Markov chains and learning algorithms, such as those introduced in [F. Cucker, S. Smale, Bull. Amer. Math. Soc., 39 (1), 2002] and [F. Cucker, S. Smale and D.~X Zhou, Found. Comput. Math., 2004]. After deriving the model, we study some of its mathematical properties, and establish theoretical and quantitative results in a simplified case. Finally, we run numerical simulations to illustrate some properties of the model.
Keywords
Cite
@article{arxiv.1911.05482,
title = {Convergence of knowledge in a cultural evolution model with population structure, random social learning and credibility biases},
author = {Sylvain Billiard and Maxime Derex and Ludovic Maisonneuve and Thomas Rey},
journal= {arXiv preprint arXiv:1911.05482},
year = {2019}
}
Comments
25 pages, 11 figures