Using AI for Analysing Political Leaders

Shuai ZHAO

Research output: Other Conference ContributionsPosterpeer-review

Abstract

This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.
Original languageEnglish
Publication statusPublished - 1 Sept 2023
Event2023 American Political Science Association Annual Meeting & Exhibition - Los Angeles, United States
Duration: 31 Aug 20233 Sept 2023
https://connect.apsanet.org/apsa2023/

Conference

Conference2023 American Political Science Association Annual Meeting & Exhibition
Abbreviated titleAPSA 2023
Country/TerritoryUnited States
CityLos Angeles
Period31/08/233/09/23
Internet address

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