Bayesian Optimization on the Equilibrium Manifold
均衡多様体上のベイズ最適化 (AI 翻訳)
Felix Kubler
🤖 gxceed AI 要約
日本語
本論文は、異質な主体からなる動学的経済モデルにおいて、気候変動を考慮した最適炭素税の計算にベイズ最適化を適用する。均衡多様体が低次元のNegishi加重パラメータ化を持つ場合、ベイズ最適化が高確率で近似解を発見し、候補解を検証できることを示す。現実的な被害関数のキャリブレーションでは、複数均衡の可能性は低いと結論づける。
English
This paper applies Bayesian optimization to compute optimal carbon taxes in a dynamic heterogeneous-agent economy with climate change. It shows that when the equilibrium manifold has a low-dimensional Negishi-weight parameterization, Bayesian optimization reliably finds approximate solutions and can certify them with high probability. Using a realistic calibration, it finds that competitive equilibria are most likely unique despite the carbon externality.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のカーボンプライシング政策（特に炭素税の導入・最適化）に示唆を与える。ベイズ最適化という機械学習手法を経済モデルに応用した点は、日本でも政策立案におけるAI活用の可能性を示す。
In the global GX context
This paper contributes to global climate policy by introducing Bayesian optimization as a tool for computing optimal carbon taxes in complex macroeconomic models. It addresses the challenge of multiple equilibria in climate-economy models, a concern for policymakers worldwide.
👥 読者別の含意
🔬研究者:Provides a novel method for computing optimal policy in heterogeneous-agent models with climate externalities, relevant for macroeconomists and climate economists.
🏢実務担当者:Offers insights for designing carbon tax policies using advanced computational techniques, though direct application may require further adaptation.
🏛政策担当者:Highlights the feasibility of unique optimal carbon taxes even under climate uncertainty, supporting carbon pricing decisions.
📄 Abstract（原文）
Computing optimal policy in heterogeneous-agent economies is complicated by the possibility of multiple equilibria. We overcome this difficulty by showing that when the equilibrium manifold has a low-dimensional Negishi-weight parameterization, Bayesian optimization reliably finds approximate solutions and can be used to certify candidate solutions with high probability. This insight brings recent machine learning advances to bear on a core problem in macroeconomics. We apply Bayesian optimization to a dynamic economy with heterogeneous agents and climate change and compute optimal carbon taxes in this setting. Although in principle the presence of the carbon externality creates scope for multiple equilibria, we show that in an example with realistic calibration of damages competitive equilibra are most likely unique.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2606.29299first seen 2026-06-30 04:11:59
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