  Research in cultural evolution aims at providing causal explanations for the
change of culture over time. Over the past decades, this field has generated an
important body of knowledge, using experimental, historical, and computational
methods. While computational models have been very successful at generating
testable hypotheses about the effects of several factors, such as population
structure or transmission biases, some phenomena have so far been more complex
to capture using agent-based and formal models. This is in particular the case
for the effect of the transformations of social information induced by evolved
cognitive mechanisms. We here propose that leveraging the capacity of Large
Language Models (LLMs) to mimic human behavior may be fruitful to address this
gap. On top of being an useful approximation of human cultural dynamics,
multi-agents models featuring generative agents are also important to study for
their own sake. Indeed, as artificial agents are bound to participate more and
more to the evolution of culture, it is crucial to better understand the
dynamics of machine-generated cultural evolution. We here present a framework
for simulating cultural evolution in populations of LLMs, allowing the
manipulation of variables known to be important in cultural evolution, such as
network structure, personality, and the way social information is aggregated
and transformed. The software we developed for conducting these simulations is
open-source and features an intuitive user-interface, which we hope will help
to build bridges between the fields of cultural evolution and generative
artificial intelligence.
