Legislative Voting Dynamics in Ukraine
Abstract
Current work in roll call modeling focuses on the legislative decision process and does not take advantage of the dynamic nature of legislation. Some political systems, such as Ukraine’s Verkovna Rada, are inherently dynamic, and should be modeled as such. In the model proposed, the entire legislative body is modeled together and bills are viewed as a dynamic process. This model requires no contextual information about individual legislators and predicts the amount of favorable votes a bill will receive within 6.2%, on average. Additionally, we find differences in behavior of bills proposed by the President and those proposed by parliament members or the Cabinet. This work only uses a simple differential model, opening the door to the use of more complex models capable of leveraging contextual information in the future.
Tom Magelinski, PhD
Senior Data Scientist - Information Extraction and Generative AI
I build AI systems that help domain experts understand vast amounts of data through state-of-the-art techniques from natural language processing, generative modeling, graph ML, and network science. I’m particularly interested researching and developing techniques to combine NLP and graph-based approaches to capture complex relationships in unstructured data.