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  • AI-driven taxonomy of democratic systems [Elektronski vir]
    Kolar, Žiga, računalničar ; Gams, Matjaž, 1954-
    This study employs machine learning techniques to present an in-depth analysis and comparison of various forms of democracies, designs a robust taxonomy. The study first characterizes democracies ... globally with a focus on key features such as political institutions, electoral systems, citizen participation, and rights and freedoms. It then utilizes machine learning methods on 11 text descriptions of different democracy types to model these diverse democratic structures. The resulting taxonomy offers a more intricate understanding of democratic variations and exemplifies machine learning’s capability in political science. It explores which groups of democracies represent sensible similarities and distances to other groups. The outcomes of this research not only contribute to better understanding of democratic forms but also showcase the potential of machine learning, offering a promising methodology for exploring complex political phenomena.
    Source: Proceedings of the International Conference on Digital Transformation and Innovation Management 2024 and ICT, Society and Human Beings 2024 [Elektronski vir] : Budapest, Hungary July 13 - 15, 2024 (Str. 183-190)
    Type of material - conference contribution ; adult, serious
    Publish date - 2024
    Language - english
    COBISS.SI-ID - 202063875

    Link(s):

    https://digitaltransformation-conf.org/call-for-papers/

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