论文标题
环境经济学和不确定性:审查和机器学习前景
Environmental Economics and Uncertainty: Review and a Machine Learning Outlook
论文作者
论文摘要
环境科学的经济评估涉及对环境影响,适应和脆弱性的测量或估值。综合评估建模是环境经济学的统一框架,它试图结合物理,生态和社会经济系统的关键要素。综合评估中的不确定性表征因组件模型而有所不同:与机械物理模型相关的不确定性通常会通过模拟或蒙特卡洛抽样的集合来评估,而与影响模型相关的不确定性则通过猜想或计量经济学分析评估。歧管采样是一种机器学习技术,它构建了所有相关变量的联合概率模型,该模型可能集中在低维几何结构上。与传统的密度估计方法相比,歧管采样更有效,尤其是当数据由一些潜在变量生成时。歧管约束的联合概率模型有助于回答预测,回应和预防的决策问题。歧管抽样用于评估墨西哥湾海上钻孔的风险。
Economic assessment in environmental science concerns the measurement or valuation of environmental impacts, adaptation, and vulnerability. Integrated assessment modeling is a unifying framework of environmental economics, which attempts to combine key elements of physical, ecological, and socioeconomic systems. Uncertainty characterization in integrated assessment varies by component models: uncertainties associated with mechanistic physical models are often assessed with an ensemble of simulations or Monte Carlo sampling, while uncertainties associated with impact models are evaluated by conjecture or econometric analysis. Manifold sampling is a machine learning technique that constructs a joint probability model of all relevant variables which may be concentrated on a low-dimensional geometric structure. Compared with traditional density estimation methods, manifold sampling is more efficient especially when the data is generated by a few latent variables. The manifold-constrained joint probability model helps answer policy-making questions from prediction, to response, and prevention. Manifold sampling is applied to assess risk of offshore drilling in the Gulf of Mexico.