Doctoral Dissertation Research in DRMS: The coupled impact of conflict and imprecision of multiple forecasts

DRMS 博士论文研究：冲突和多重预测不精确的耦合影响

• 批准号: 1459150
• 负责人: David Budescu
• 金额: $ 1.54万
• 依托单位: Fordham University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1459150&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; DRMS; coupled

Technical DescriptionPeople often rely on projections from multiple experts to make decisions. This includes daily decisions like utilizing multiple weather forecasts as well as life-changing decisions like seeking multiple doctors' opinions about a serious diagnosis. Previous research has differentiated between conflicting and imprecise forecasts. Conflict is observed when multiple advisers offer different, but precise, forecasts (e.g. one expert projects 6 inches of snow and another projects only 1 inch). Imprecision is observed when the advisers agree in their imprecision (e.g. both experts forecast 1 to 6 inches of snow). Previous models treat conflict and imprecision separately, but they are rarely well-differentiated and often correlated (e.g. one expert predicts 1 to 5 inches of snow and another predicts 2 to 6 inches). This proposal examines how various combinations of these two factors (conflict and imprecision) alter people's perceptions and choices based on the theoretical hypothesis that conflict and imprecision are functions of the underlying attributes of the forecast sets. The ultimate goal is to determine optimal modes of aggregating and presenting multiple forecasts to invoke accurate perceptions of the information. The research plan includes a series of online experiments involving nationally-representative samples comparing various combinations of conflict and imprecision varied by the type and degree of two key set factors, similarity and symmetry. Similarity refers to the relationship between the forecasts and has three categories: disjoint sets that do not overlap, intersecting sets that partially overlap, and nested sets where one set is fully embedded in the other. Symmetry refers to the balance of the sets around the center (or mathematically, the relative deviation of the mean of all forecasts from their median). The direction of asymmetry can vary, so positively (negatively) skewed sets have fewer high (low) values. Participants will view several sets of interval forecasts in well-defined domains and will estimate the most likely value, range of possible values, and rate the sets on key attributes (e.g., ambiguity, credibility, informativeness, etc.). We will also manipulate the topic domains (using finance, health, and politics contexts) to test the generalizability of the results. Broader Significance and ImportanceThe analysis will quantify the effects of the various factors manipulated on the decision makers? decisions. We will also use various dimensionality reduction and classification techniques (multidimensional scaling paired with cluster analysis) to map the various projection sets based on their "psychological distances" to help understand the cognitive processes that drive people's responses. This study is an important step toward improving the communication of risk and uncertainty based on empirically observed psychological principles. Such steps are vital to bridging the gap between experts and laypeople because non-experts are often disproportionally influenced by how information is presented. These results are relevant to many domains such as military intelligence, climate forecasting, etc., which must make careful decisions to invest their scarce time and money to reduce uncertainties between and within experts.

技术描述人们经常依赖多个专家的预测来做出决策。这包括日常决策，例如利用多个天气预报，以及改变生活的决策，例如寻求多位医生对严重诊断的意见。先前的研究区分了相互矛盾的预测和不精确的预测。当多个顾问提供不同但精确的预测时，就会出现冲突（例如，一位专家预测降雪量为 6 英寸，而另一位专家预测降雪量仅为 1 英寸）。当顾问们一致认为他们的不精确性（例如，两位专家预测降雪量为 1 到 6 英寸）时，就会观察到不精确性。以前的模型分别处理冲突和不精确性，但它们很少有很好的区分，并且经常相关（例如，一位专家预测降雪量为 1 到 5 英寸，另一位专家预测降雪量为 2 到 6 英寸）。该提案基于冲突和不精确性是预测集的基本属性的函数这一理论假设，研究了这两个因素（冲突和不精确性）的各种组合如何改变人们的看法和选择。最终目标是确定聚合和呈现多个预测的最佳模式，以调用对信息的准确感知。该研究计划包括一系列涉及全国代表性样本的在线实验，比较根据两个关键因素（相似性和对称性）的类型和程度而变化的冲突和不精确性的各种组合。相似性是指预测之间的关系，分为三类：不重叠的不相交集、部分重叠的相交集以及一组完全嵌入另一组的嵌套集。对称性是指围绕中心的集合的平衡（或者在数学上，所有预测的平均值与其中位数的相对偏差）。不对称的方向可能会有所不同，因此正（负）偏斜的集合具有较少的高（低）值。参与者将查看明确定义领域中的几组区间预测，并估计最可能的值、可能值的范围，并对关键属性（例如模糊性、可信度、信息性等）对这些组进行评级。我们还将操纵主题领域（使用金融、健康和政治背景）来测试结果的普遍性。更广泛的意义和重要性该分析将量化各种因素对决策者的影响？决定。 我们还将使用各种降维和分类技术（多维尺度与聚类分析相结合）根据“心理距离”映射各种投影集，以帮助理解驱动人们反应的认知过程。这项研究是基于经验观察的心理学原理，朝着改善风险和不确定性沟通迈出的重要一步。这些步骤对于弥合专家和外行之间的差距至关重要，因为非专家往往会受到信息呈现方式的不成比例的影响。这些结果与军事情报、气候预测等许多领域相关，这些领域必须做出谨慎的决定，投入稀缺的时间和金钱，以减少专家之间和内部的不确定性。