Feature Engineering for Quantitative Analysis of Cultural Evolution

Fabio Celli

No aj8xk, 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, the presence or absence of categorical variables are treated as 1 or 0 for computational purposes, and then aggregated with compression techniques, potentially generating biases when the information compressed into different variables is uneven. Our tests reveal that the use of time-resolved variables can help to prevent these biases, to assure higher reliability of the results and to allow an easier explanation of the models.

Date: 2022-12-11
New Economics Papers: this item is included in nep-evo
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/64cb79e49cbf0330861e47ef/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:aj8xk

DOI: 10.31219/osf.io/aj8xk

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().