Improving Scenario Discovery

改进场景发现

• 批准号: 0922754
• 负责人: Robert Lempert
• 金额: --
• 依托单位: Rand Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0922754&HistoricalAwards=false
• 关键词: Improving; Scenario; Discovery

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Scenario Discovery is a valuable approach to participatory, computer-assisted scenario development. It is useful for many hard, deeply uncertain policy problems such as climate change and energy planning. This project aims to advance scenario discovery methodology by applying novel algorithms that can provide significant new capabilities and then evaluating how decision makers respond to the results. It, thus, will simultaneously improve both the technical capacity of the modeling and the ability for decision makers to work effectively with the models. To be more specific: Scenarios represent sets of plausible future states of the world that represent vulnerabilities of proposed policies, i.e., cases where a policy fails to meet its performance goals. Scenario discovery characterizes such sets by helping users to apply statistical or data-mining algorithms to databases of simulation-model-generated results in order to identify easy-to-interpret combinations of uncertain model input parameters highly predictive of these policy-relevant cases. The approach has already proved successful in several high impact policy studies, but its broad application to many policy areas is currently limited by the capabilities of current scenario discovery algorithms and the existing understanding of how decision makers interpret their results. This project will improve the algorithmic capabilities of scenario discovery by identifying and modifying existing adaptive sampling algorithms to address sparse sampling issues and by employing Principal Component Analysis methods to transform the simulation generated-databases into new coordinate systems that existing scenario discovery algorithms can interpret more successfully. The project will also improve the understanding of how decision makers can best use scenario discovery by conducting a series of workshops and interviews with decision makers to: i) assess the degree to which the proxy measures of interpretability used by the algorithms correspond to results that decision makers can actually interpret; and ii) evaluate decision makers? views on the usefulness of scenarios of different levels of complexity. The workshop results will guide the development of the algorithms considered in the project.

该奖项由2009年《美国复苏和再投资法案》(公法111-5)资助。场景探索是参与式计算机辅助场景开发的一种有价值的方法。对于气候变化和能源规划等许多艰难的、高度不确定的政策问题，它是有用的。该项目旨在通过应用能够提供重要新功能的新算法，然后评估决策者对结果的反应，来推进场景发现方法。因此，它将同时提高建模的技术能力和决策者有效使用模型的能力。更具体地说：场景代表了一组看似合理的未来世界状态，这些状态代表了拟议策略的脆弱性，即策略未能达到其性能目标的情况。场景发现通过帮助用户将统计或数据挖掘算法应用于模拟模型生成的结果的数据库，以识别高度预测这些与策略相关的情况的不确定模型输入参数的易于解释的组合来表征这类集合。这种方法已经在几个高影响力的政策研究中被证明是成功的，但它在许多政策领域的广泛应用目前受到当前情景发现算法的能力和对决策者如何解释其结果的现有理解的限制。该项目将通过识别和修改现有的自适应采样算法来解决稀疏采样问题，并通过使用主成分分析方法将模拟生成的数据库转换到现有场景发现算法可以更成功地解释的新坐标系，从而提高场景发现的算法能力。该项目还将通过举办一系列研讨会和与决策者的访谈来提高对决策者如何最好地利用情景发现的理解：i)评估算法使用的可解释性代理测量与决策者实际能够解释的结果相一致的程度；以及ii)评估决策者？对不同复杂程度的情景的有用性的看法。研讨会的结果将指导项目中所考虑的算法的开发。