| 配分額 *注記 |
16,120千円 (直接経費: 12,400千円、間接経費: 3,720千円)
2028年度: 3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
2027年度: 4,940千円 (直接経費: 3,800千円、間接経費: 1,140千円)
2026年度: 3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
2025年度: 4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
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| 研究開始時の研究の概要 |
We will build a Machine-Learning (ML) framework to infer, predict, and control the behavior of interacting rational agents. For this, we will develop a Game-Theory Informed ML method that can be trained on behavioral data, and use it to infer the hidden utility functions that govern the decision-making of the agents. In particular, we focus on the societal response to a pandemic (e.g., COVID-19). We will first validate our method on synthetic data, before applying it to real-world data.
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