arXiv:2606.07572v1 Announce Type: cross Abstract: Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains limited. This study introduces nonlinear machine-learning forecasting models, based on decision tree and ensemble learning methods, for predicting the outcomes of Japanese lower-house elections. To assess the methodological benefits of our approach, we replicated the theoretical framework and dataset of Lewis-Beck and Tien's (LBT) foundational statistical forecasting model for Japanese elections.
The increasing maturity and accessibility of nonlinear machine learning methods are enabling their application to more complex social science problems like political forecasting.
This study demonstrates the growing capability of AI to analyze and predict human societal behavior, impacting political strategy, public opinion analysis, and potentially government stability.
The accuracy and sophistication of election forecasting models for specific nations like Japan are improving, potentially offering new tools for political actors and observers.
- · Political strategists
- · Data scientists specializing in social prediction
- · Academic researchers in computational social science
- · AI/Machine Learning sector
- · Traditional polling methods
- · Human political analysts relying solely on qualitative methods
- · Political campaigns lacking sophisticated data analytics
More accurate election predictions become available to the public and political entities in Japan.
Political campaigns begin to heavily integrate and rely on AI-driven forecasting for strategy and resource allocation.
The perceived determinism of AI-driven political forecasting could influence voter behavior or erode faith in democratic processes.
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Read at arXiv cs.LG