Introduction
My name is Momo Hirose, and I am Class of 2024 double-majoring in Computer Science and Global Management.
Abstract
The implementation of policy technology tools that leverage artificial intelligence to improve decision making is gaining attention in public policy. However, tools that not only evaluate historical data and policies, but also propose and simulate policies based on them, have not been fully developed. This study focuses on the research and development of an algorithm for allocating Japan’s national budget based on past data, using four different machine learning models and genetic algorithm optimization methods. It takes as inputs classified categories and Japan’s past budgets and economic indicators, such as the country’s GDP growth rate and inflation rate, and returns a proposal for specific public budget allocations f for the following year.
Data Architecture
Poster
Video
Gitlab
https://code.cs.earlham.edu/mhirose20/momo_senior-capstone/-/tree/main