To cope with the uncertainty of green infrastructure planning, many cities take an adaptive approach and use learning-by-doing to improve estimates of the cost and efficacy of stormwater management practices (SMPs) and use that information to improve stormwater plans. However, deciding whether that learning is worth its expense has been a challenge for practitioners. We propose a modeling framework to assess the economic value of learning. Methodologically, we present a generalized adaptive planning method that includes learning from direct and indirect investments and multiple degrees of learning. The formulation enables users to specify possible knowledge gains from near-term actions and quantify its value by assessing its impacts on subsequent decisions and their performance. Further, we quantify the values of both learning and adaptability by calculating differences in expected system performance between three types of decision making: non-adaptive (no learning between decisions), passive adaptive (adaptive planning that passively accepts incidental learning), and active adaptive (adaptive planning that considers potential learning opportunities when choosing investments). For illustration, we apply the framework to an example inspired by a real stormwater management setting in Philadelphia, PA. In the example, the ability of SMPs to reduce runoff is evaluated by hydrological simulation. The literature and expert opinions inform estimates of costs, SMP performance deterioration over time, and predictions of possible knowledge gains. The results show that active adaptive planning supported by stochastic optimization can achieve substantial cost savings.

中文翻译：

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评估动态适应性规划中学习价值的建模框架：在绿色基础设施投资评估中的应用

为应对绿色基础设施规划的不确定性，许多城市采用适应性方法，通过边做边学来改进对雨水管理实践 (SMP) 成本和效率的估计，并利用该信息改进雨水规划。然而，决定这种学习是否值得付出代价一直是从业者的挑战。我们提出了一个建模框架来评估学习的经济价值。在方法论上，我们提出了一种广义的适应性规划方法，包括从直接和间接投资中学习以及多层次的学习。该公式使用户能够指定从近期行动中可能获得的知识，并通过评估其对后续决策及其绩效的影响来量化其价值。更远，我们通过计算三种决策类型之间预期系统性能的差异来量化学习和适应性的价值：非自适应（决策之间没有学习）、被动自适应（被动接受偶然学习的自适应规划）和主动自适应（自适应在选择投资时考虑潜在学习机会的计划）。为了说明，我们将该框架应用于受宾夕法尼亚州费城真实雨水管理环境启发的示例。在该示例中，通过水文模拟评估了 SMP 减少径流的能力。文献和专家意见为成本估算、SMP 性能随时间的恶化以及可能的知识收益预测提供了依据。