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Spatial evolution of human cultures inferred through Bayesian phylogenetic analysis.

Takuya Takahashi1, Yasuo Ihara2

  • 1Meiji Institute for Advanced Study of Mathematical Sciences (MIMS), Meiji University, Nakano 4-21-1, Nakanoku, Tokyo 164-8525, Japan.

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|January 3, 2023
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This study introduces a Bayesian statistical framework to analyze cultural transmission, differentiating between ancestral descent and horizontal gene transfer. The model reconstructs cultural trait evolution and transmission dynamics across populations.

Keywords:
Bayesian phylogenetic analysiscoalescent theorycultural evolutionnetworkpopulation geneticsspatial dynamics

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Area of Science:

  • Anthropology
  • Computational Biology
  • Bayesian Statistics

Background:

  • Human cultural diversity arises from both ancestral inheritance and transmission between contemporary groups.
  • Understanding the interplay of vertical (descent) and horizontal (neighboring populations) cultural transmission is crucial for evolutionary studies.
  • Existing models often struggle to disentangle these transmission pathways and their geographical influences.

Purpose of the Study:

  • To develop a Bayesian statistical framework for analyzing spatial variations in cultural repertoires.
  • To model both horizontal transmission and mutation of cultural traits within a spatially explicit network model.
  • To infer transmission rates, mutation rates, and trait identities in unobserved populations.

Main Methods:

  • A network model representing populations as nodes, incorporating horizontal transmission and trait mutation.
  • Application of Markov chain Monte Carlo (MCMC) algorithms, adapted from Bayesian phylogenetic analysis.
  • Development of a heuristic algorithm to reduce computational complexity by simulating coalescent processes.

Main Results:

  • The framework successfully computes posterior distributions for key parameters like horizontal transmission and mutation rates.
  • The method allows for the reconstruction of the genealogical tree of cultural traits under low mutation rates.
  • Numerical simulations demonstrate computational feasibility, though broad posteriors may arise with uninformative priors.

Conclusions:

  • The proposed Bayesian framework provides a robust method for analyzing complex cultural transmission patterns.
  • This approach enhances our ability to understand the spatial dynamics of cultural evolution and trait diversification.
  • The model offers a powerful tool for inferring historical and contemporary cultural exchange across populations.