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Convergence of knowledge in a stochastic cultural evolution model with population structure, 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.
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Submitted on : Monday, July 20, 2020 - 9:33:02 AM
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Sylvain Billiard, Maxime Derex, Ludovic Maisonneuve, Thomas Rey. Convergence of knowledge in a stochastic cultural evolution model with population structure, social learning and credibility biases. Mathematical Models and Methods in Applied Sciences, World Scientific Publishing, 2020, 30 (14), pp.2691-2723. ⟨10.1142/S0218202520500529⟩. ⟨hal-02357188v2⟩

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