Bridging Scales in Cultural Evolution

C2S 2026 — Computational Cultural Science Workshop
École nationale des Chartes – PSL, Paris, 18–19 May 2026

Abstract

Computational approaches have provided powerful tools to better understand culture. By allowing us to mine large datasets and extract meaningful patterns even in the absence of established theory, methods such as machine learning, Bayesian inference, network analysis, and dimensionality reduction have proven transformative. Yet these approaches remain biased toward certain types of data and scales of analysis. We propose combining agent-based modelling with these computational methods to test hypotheses at the mesoscale: processes unfolding at the regional level over millennia.

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Author

Simon Carrignon — UCL GEE / CDAL / ENCOUNTER / UGI
s.carrignon@ucl.ac.uk