Code and data to reproduce the analysis and figures from the manuscript by Michael Chimento, Brendan J. Barrett, Anne Kandler and Lucy M. Aplin.
Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed interchangeably or modeled separately, yet to date, no study has accounted for both processes and explored their interaction. We present a generative model that integrates the two to explore how variation in production rules might shape cultural diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires also change over time, and the social transmission of behaviours depends on their frequency. We diffuse a novel behaviour through social networks across a large parameter space to demonstrate how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking transmission and production might affect the performance of two commonly used inferential models for social learning; Network-based Diffusion Analysis, and Experienced Weighted Attraction models. Clarifying the distinction between acquisition and production yields predictions for how production influences diffusion that are generalisable across species, and has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.
| Directory | Description |
|---|---|
| analysis | R code used to clean raw data, run statistical analyses and create figures |
| model_outputs | Raw data generated by models, as well as .Rda files generated by data_cleaning.R |
| models | agent based models |
| output | tables and figures generated by files in analysis folder |
Data used in the manuscript can be found at the following resource: Chimento, Michael (2022), Data to reproduce "Cultural diffusion dynamics depend on behavioural production rules", Dryad, Dataset, https://doi.org/10.5061/dryad.vx0k6djvk
To reproduce the analysis presented in the manuscript, clone this repository, and place the Rda files in the model_outputs/Rda_files directory. The R code will then run as is.
All agent based simulations were performed using Python v. 3.8.10 on Linux Pop!_OS 20.10 using the following Python3 libraries
- networkx v. 2.6.2
- numpy v. 1.19.0
- pandas v. 1.1.5
- scipy v. 1.5.4
All analyses were performed using R v. 4.0.2 and Stan v. 2.27 using the following R packages
- igraph v. 1.2.6
- ggpubr v. 0.4.0.999
- kableExtra v. 1.3.4
- knitr v. 1.30
- NBDA v. 0.9.6
- reader v. 1.0.6
- rethinking v. 2.13
- Rstan v. 2.21.2
- sna v. 2.6
- tidyverse v. 1.3.0