Convergence of knowledge in a stochastic cultural evolution model with population structure, social learning and credibility biases

Détails

ID Serval
serval:BIB_95F9585C77B8
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Convergence of knowledge in a stochastic cultural evolution model with population structure, social learning and credibility biases
Périodique
Mathematical Models and Methods in Applied Sciences
Auteur⸱e⸱s
Billiard Sylvain, Derex Maxime, Maisonneuve Ludovic, Rey Thomas
ISSN
0218-2025
1793-6314
Statut éditorial
Publié
Date de publication
30/12/2020
Volume
30
Numéro
14
Pages
2691-2723
Langue
anglais
Résumé
Understanding how knowledge emerges and propagates within groups is crucial to explain the evolution of human populations. In this work, we introduce a mathematically oriented model that draws on individual-based approaches, inhomogeneous Markov chains and learning algorithms, such as those introduced in [F. Cucker and S. Smale, On the mathematical foundations of learning, Bull. Amer. Math. Soc. 39 (2002) 1–49; F. Cucker, S. Smale and D. X. Zhou, Modeling language evolution, Found. Comput. Math. 4 (2004) 315–343]. 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. Our main result is that, as time goes to infinity, individuals’ knowledge can converge to a common shared knowledge that was not present in the convex combination of initial individuals’ knowledge.
Mots-clé
Applied Mathematics, Modeling and Simulation
Web of science
Création de la notice
08/11/2022 10:06
Dernière modification de la notice
24/02/2024 7:35
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