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.

技术描述通常依靠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定，例如寻求多个医生对认真诊断的意见。先前的研究与冲突和不精确的预测有所不同。当多个顾问提供不同但精确的预测时，就会观察到冲突（例如，一个专家项目的积雪6英寸，另一个项目仅1英寸）。当顾问同意其不精确的同意时，就会观察到不精确（例如，两位专家预测1至6英寸的降雪）。以前的模型分别处理冲突和不精确，但很少有分化的且经常相关（例如，一个专家预测1至5英寸的雪，另一个专家预测2至6英寸）。该提案研究了这两个因素（冲突和不精确）的各种组合如何基于理论上的假设来改变人们的看法和选择，即冲突和不精确是预测集的基本属性的函数。最终目标是确定汇总和提出多个预测的最佳模式，以调用对信息的准确看法。该研究计划包括一系列在线实验，涉及全国代表性样本，比较了冲突和不精确的各种组合，其类型和程度因两个关键设定因素的类型和程度而异，相似性和对称性。相似性是指预测之间的关系，并且具有三个类别：不重叠的不相交集，与部分重叠的集合相交的集合以及一个集合完全嵌入另一组中的嵌套集。对称性是指中心周围集合的平衡（或数学上，所有预测与中位数的相对偏差）。不对称的方向可以变化，因此（负）偏斜集的高值（低）值较少。参与者将在定义明确的域中查看几组间隔预测，并将估计最可能的值，可能的值范围，并评分关键属性（例如，模棱两可，可信度，信息性等）上的集合。我们还将操纵主题领域（使用金融，健康和政治环境）来测试结果的普遍性。更广泛的意义和重要的分析将量化操纵决策者的各种因素的影响？决定。 我们还将使用各种维度降低和分类技术（多维缩放与聚类分析配对）来根据其“心理距离”来绘制各种投影集，以帮助了解推动人们反应的认知过程。这项研究是基于经验观察到的心理原则来改善风险和不确定性交流的重要一步。这样的步骤对于弥合专家与外行之间的差距至关重要，因为非专家通常会受到信息的呈现方式不成比例的影响。这些结果与许多领域有关，例如军事情报，气候预测等，这些领域必须做出仔细的决定，以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。