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

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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.