DOI: 10.5061/dryad.3r2280gff
title: Data from: Social network architecture and the tempo of cumulative cultural evolution

description: 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.
description: 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. Our second agent-based
 model (model 2) extended the first by changing the transmission mechanic
 and altering the set of valid combinations such that the model can run
 past the first innovation of either recombination product. Transmission of
 valid products now occurred between dyads of agents (one-to-one diffusion)
 prior to choosing items from their inventory, in contrast to the broadcast
 style of diffusion in model 1. Secondly, if a triad contained either
 recombination products, the final product was that recombination product.
 In the case where both recombination products were present in the triad,
 one was chosen as the final product at random. This allowed us to track
 the diffusion of recombination products beyond their innovation. We also
 ran 5,000 simulations for the same parameter space of model 1, recording
 time (in epochs) to cultural lineage recombination, as well as time to
 diffusion to the majority of the network.
description: 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.