Improving Scenario Discovery

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。情景发现是参与式计算机辅助情景开发的宝贵方法。它对许多困难的、高度不确定的政策问题都很有用，比如气候变化和能源规划。该项目旨在通过应用新算法来推进场景发现方法，这些算法可以提供重要的新功能，然后评估决策者如何对结果做出反应。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：情景代表了一组可能的未来世界状态，这些状态代表了拟议政策的脆弱性，即，策略未能实现其性能目标的情况。情景发现的特点是这样的集，帮助用户应用统计或数据挖掘算法的模拟模型生成的结果的数据库，以确定易于解释的组合不确定的模型输入参数高度预测这些政策相关的情况下。该方法已经在几个高影响力的政策研究中证明是成功的，但其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以及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，并通过采用主成分分析方法将模拟生成的数据库转换为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。该项目还将通过与决策者进行一系列研讨会和访谈来提高对决策者如何最好地使用场景发现的理解，以：i）评估算法使用的可解释性代理措施与决策者实际可以解释的结果相对应的程度; ii）评估决策者？对不同复杂程度的设想方案的有用性的看法。研讨会的结果将指导该项目中考虑的算法的开发。

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.