Data from: Social network architecture and the tempo of cumulative cultural evolution
Cantor, Mauricio et al. (2021), Data from: Social network architecture and the tempo of cumulative cultural evolution, Dryad, Dataset, https://doi.org/10.5061/dryad.3r2280gff
The ability to build upon previous knowledge—cumulative cultural evolution—is a hallmark of human societies. While cumulative cultural evolution depends on the interaction between social systems, cognition and the environment, there is increasing evidence that cumulative cultural evolution is facilitated by larger and more structured societies. However, such effects may be interlinked with patterns of social wiring, thus the relative importance of social network architecture as an additional factor shaping cumulative cultural evolution remains unclear. By simulating innovation and diffusion of cultural traits in populations with stereotyped social structures, we disentangle the relative contributions of network architecture from those of population size and connectivity. We demonstrate that while more structured networks, such as those found in multilevel societies, can promote the recombination of cultural traits into high-value products, they also hinder spread and make products more likely to go extinct. We find that transmission mechanisms are therefore critical in determining the outcomes of cumulative cultural evolution. Our results highlight the complex interaction between population size, structure and transmission mechanisms, with important implications for future research.
These data were generated by computer simulations: we constructed two agent-based models to simulate cultural evolution on different types of social network architectures.
We first generated social networks with six different architectures—random, small-world, lattice, modular, modular lattice, and multilevel—capturing different levels and combinations of clustering and modularity. We expressed these network architectures in populations with different sizes and densities of connections (average degree), where all individuals in the network had the same degree. We then build two agent-based models to explore how network architecture affects cumulative culture evolution. Briefly, our models allow innovations of cultural products to take place along two cultural lineages, with the knowledge of new products being spread through social connections via two transmission mechanics: either one-to-many or one-to-one diffusion. Once a high level of product diversity has been reached in both lineages, agents can recombine each lineage’s products into one with a final higher payoff product (hereafter ‘recombination’). Finally, we compared the performance of agents arranged in the different network architecture in terms of time to cultural recombination (i.e. tempo), time to diffusion, and the diversity of cultural traits.
The two agent-based models differed in the mechanics of information transmission: one-to-many versus one-to-one diffusion pathways. In our first agent-based model (model 1), all agents were initialized with an inventory of three items from each of two lineages. In each simulation round (epoch), each focal agent was selected once, at random, and a partner randomly chosen from its social network connections. These agents combined one or two items from their inventory in proportion to their value into a triad of items. If this triad was a valid product, knowledge of that product was learned, spread immediately to all their network connections (one-to-many diffusion), and subsequently became available as an ingredient for making new products. Simulations finished once a recombination product (a triad that recombines specific products from both lineages) was first innovated. We ran 5,000 simulations for each of the network architecture types, sizes and densities of connections, recording time to achieve the recombination product (in epochs) and tracking the diversity of cultural innovations over time. An epoch was one simulation round in which each agent was selected once as a focal agent in random order.
Since these data were generated by computer simulations, we also provide all the code to run the models and generate replicates of these data. Please visit the online repository associated to this article at https://github.com/simeonqs/Social_network_architecture_and_the_tempo_of_cumulative_cultural_evolution to have access to:
- The R code to create the social networks
- The Python code to run the two agent based models
- The R code to run the two agent based models
- The R code to create the figures in the article that uses these simulated data.
Deutsche Forschungsgemeinschaft, Award: EXC 2117–422037984
H2020 European Research Council, Award: 850859
China Scholarship Council, Award: 201706100183
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Award: 88881.170254/2018-01
Advanced Centre for Collective Behaviour
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Award: Eccellenza Professorship Grant: PCEFP3_187058
Advanced Centre for Collective Behaviour