Simulation models of cultural evolution in R

This tutorial shows how to create very simple simulation or agent-based models of cultural evolution in R. Currently these are:

• Model 1: Unbiased transmission
• Model 2: Unbiased and biased mutation
• Model 3: Biased transmission (direct/content bias)
• Model 4: Biased transmission (indirect bias)
• Model 5: Biased transmission (conformist bias)
• Model 6: Vertical and horizontal transmission
• Model 7: Migration
• Model 8: Blending inheritance
• Model 9: Demography and cultural gain/loss
• Model 10: Polarization
• Model 11: Cultural group selection
• Model 12: Historical dynamics
• Model 13: Social contagion
• Model 14: Social networks
• Model 15: Opinion formation
• Model 16: Bayesian iterated learning
• Model 17: Reinforcement learning
• Model 18: Evolution of social learning
• Model 19: Evolution of social learning strategies

Each model is contained in a separate RMarkdown (Rmd) file. You can either (i) download each of these Rmd files from https://github.com/amesoudi/cultural_evolution_ABM_tutorial then open them in RStudio or another IDE, executing the code as you read the text, or (ii) read the online version of the tutorial at https://bookdown.org/amesoudi/ABMtutorial_bookdown/ which contains the compiled models with outputs. There is also a pdf version of the entire book on the github page. For maximum learning (and fun), I recommend (i), where you can execute and play around with the code yourself.

You can cite the tutorial as:

• Mesoudi, Alex (2023) Simulation models of cultural evolution in R. doi:10.5281/zenodo.5155821. Available at https://github.com/amesoudi/cultural_evolution_ABM_tutorial and https://bookdown.org/amesoudi/ABMtutorial_bookdown/.

or use the Cite this repository button on github.

A longer and more detailed book-length resource, which builds on some of these models, can be found here:

• Alberto Acerbi, Alex Mesoudi, and Marco Smolla (2020) Individual-based models of cultural evolution. A step-by-step guide using R. doi:110.31219/osf.io/32v6a. Available at: https://acerbialberto.com/IBM-cultevo/