Feature Engineering for Quantitative Analysis of Cultural Evolution

Fabio Celli and Bruno Lepri

No aj8xk_v1, SocArXiv from Center for Open Science

Abstract: In this paper we introduce the use of time-resolved variables to represent the evolution of categorical variables through time. Traditionally, in data science the presence or absence of categorical variables are treated as 1 or 0 with One-Hot Encoding, and then aggregated with compression techniques, such as Principal Components. We annotated time-resolved variables on the Seshat dataset as sequences of categorical features and we compare them to the same categorical features treated with one-hot encoding and Principal Component compression. We find that time-resolved variables are better predictors of the evolution of social scale and social hierarchy, but not of government specialization. We discuss advantages and limitation of the usage of time-resolved variables for the computational analysis of cultural evolution.

Date: 2023-09-14
New Economics Papers: this item is included in nep-evo
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:aj8xk_v1

DOI: 10.31219/osf.io/aj8xk_v1

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