Content-dependent biases in social learning strategies: a multiscale approach

• Autores: Simon Carrignon
• Directores de la Tesis: Sergi Valverde Castillo (dir. tes.), Xavier Rubio Campillo (codir. tes.)
• Lectura: En la Universitat Pompeu Fabra ( España ) en 2019
• Idioma: español
• Tribunal Calificador de la Tesis: Albert Díaz Guilera (presid.), Alberto Acerbi (secret.), Maxime Derex (voc.)
• Programa de doctorado: Programa de Doctorado en Biomedicina por la Universidad Pompeu Fabra
• Materias:
  • Psicología
    • Psicopedagogía
      • Leyes del aprendizaje
  • Sociología
    • Sociología cultural
      • Evolución cultural
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • The content of what we learn shapes the evolution of human culture and society. In this thesis, we have quantified the influence of content-dependent biases in social learning strategies. Our theoretical framework combines agent-based models and Bayesian inference to measure content-dependent biases in large-scale social learning strategies. Our first empirical study measures the impact of social transmission biases in Twitter. The novelty of the second study is two-fold: ours is one of the rare uses of computational modelling in historical Roman Studies and one of the first tests of the impact of success bias across large spatial and temporal scales.