Representative Methods of Computational Socioeconomics

TL;DR

This paper reviews three key computational methods—natural data analysis, large-scale online experiments, and big data-survey integration—in computational socioeconomics, highlighting their potential and current challenges.

Contribution

It introduces and discusses three representative methods in computational socioeconomics, emphasizing recent advances and limitations in the field.

Findings

Natural data analyses provide new insights into socioeconomic phenomena.

Large-scale online experiments enable scalable social science research.

Integration of big data and surveys enhances data richness and accuracy.

Abstract

The increasing data availability and imported analyzing tools from computer science and physical science have sharply changed traditional methodologies of social sciences, leading to a new branch named computational socioeconomics that studies various phenomena in socioeconomic development by using quantitative methods based on large-scale real-world data. Sited on recent publications, this Perspective will introduce three representative methods: (i) natural data analyses, (ii) large-scale online experiments, and (iii) integration of big data and surveys. This Perspective ends up with in-depth discussion on the limitations and challenges of the above-mentioned emerging methods.