Abstract:

Mathematical and computational approaches have made important contributions to cultural evolutionary theory by highlighting the effect of factors such as population size, social network structure, and transmission fidelity on cultural evolution. However, some cultural phenomena have so far been more difficult to capture using agent-based and formal models. In particular, determining how personality, background, and cultural context shape how individuals select, transform and transmit social information has remained out of the reach of traditional modelling approaches, and has thus mainly been studied empirically. We here propose that leveraging the capacity of Large Language Models (LLMs) to “role-play” and impersonate specific profiles may be fruitful to address this gap. Indeed, these generative models have been found to exhibit quite satisfying accuracy and relatively good consistency when used to simulate different characters. We present a framework for simulating and analyzing cultural evolution in populations of role-playing LLMs, along with results from use-cases that illustrate the kind of hypotheses that can be generated using this approach. The software we developed for conducting these simulations is open-source and features an intuitive, code-free user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence.

Speaker's social media:

@Jeremy__Perez