Individual-Based Models of Cultural Evolution: A Step-by-Step Guide Using R

di Alberto Acerbi (Autore)

Alex Mesoudi (Autore)

Marco Smolla (Autore)

Taylor & Francis Ltd, 2022

Individual-Based Models of Cultural Evolution shows readers how to create individual-based models of cultural evolution using the programming language R. The field of cultural evolution has emerged in the last few decades as a thriving, interdisciplinary effort to understand cultural change and cultural diversity within an evolutionary framework and using evolutionary tools, concepts, and methods. Given its roots in evolutionary biology, much of cultural evolution is grounded in, or inspired by, formal models. Yet many researchers interested in cultural evolution come from backgrounds that lack training in formal modelling, such as psychology, anthropology or archaeology. This book addresses that gap. It provides example code in R for readers to run their own models, moving from very simple models of the basic processes of cultural evolution, such as biased transmission and cultural mutation, to more advanced topics such as the evolution of social learning, demographic effects, and social network analysis. Features of this book: Recreates existing models in the literature to show how these were created and to enable readers to have a better understanding of their significance and how to apply them to their own research questions Provides full R code to realize models and analyse and plot outputs, with line-by-line analysis Requires no previous knowledge of the field of cultural evolution, and only very basic programming knowledge This is an essential resource for researchers and students interested in cultural evolution, including disciplines such as psychology, anthropology, archaeology, and biology as well as sociology and digital humanities.

• Autore: Alberto Acerbi, Alex Mesoudi, Marco Smolla
• Editore: Taylor & Francis Ltd
• Anno: 2022
• Rilegatura: Paperback / softback
• Pagine: 216 p.
• Dimensioni: 246 x 174 mm
• Peso: 3020 gr.
• EAN: 9781032252063

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