用轻量机器学习找可持续发展政策最优解
Modelling the Doughnut of social and planetary boundaries with frugal machine learning
- 用随机森林和Q-learning等轻量算法寻找可持续政策参数
- 在简单宏观经济模型中成功找到兼顾社会与环境的可行路径
- 适合关注可持续政策设计的气候经济研究者
社会与行星边界‘ Doughnut’框架已成为评估环境与社会可持续性的常用工具。本文提供了一个概念验证分析，展示机器学习方法如何应用于简单的Doughnut宏观经济模型。首先，我们表明机器学习可用于寻找符合‘在Doughnut内生活’的政策参数；其次，展示了强化学习代理可在参数空间中识别通往理想政策的最优轨迹。所测试的方法包括随机森林分类器和$Q$-learning，均为轻量级机器学习方法，能够发现同时实现环境与社会可持续性的政策组合。下一步是将这些方法应用于更复杂的生态宏观经济模型。
原文摘要 · Abstract (English)
The 'Doughnut' of social and planetary boundaries has emerged as a popular framework for assessing environmental and social sustainability. Here, we provide a proof-of-concept analysis that shows how machine learning (ML) methods can be applied to a simple macroeconomic model of the Doughnut. First, we show how ML methods can be used to find policy parameters that are consistent with 'living within the Doughnut'. Second, we show how a reinforcement learning agent can identify the optimal trajectory towards desired policies in the parameter space. The approaches we test, which include a Random Forest Classifier and $Q$-learning, are frugal ML methods that are able to find policy parameter combinations that achieve both environmental and social sustainability. The next step is the application of these methods to a more complex ecological macroeconomic model.
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