Causal Model Based Cue Weighting

在判断和决策领域，两种截然不同的方法定义了关于人们如何组合可用的信息以形成判断的辩论：线性模型和启发式模型。由于线性模型在某些环境中擅长解释判断，而启发式解释在其他环境中更有效，许多研究人员认为，人们会根据判断任务的具体情况在不同的策略之间进行切换。在这项研究中，PI开发和测试了一个因果推理模型，该模型将线性和启发式判断模型包含在一个单一的、统一的框架中。PI还将测试该模型的其他影响，这些影响是以前的模型无法解释的。就更广泛的影响而言，这项研究涉及各种领域。医学诊断依赖于医生成功地为线索(症状)分配适当的权重。法官和陪审团被要求结合线索(证据)来做出有罪和处罚的判决。投资者根据各种市况等线索来决定投资策略。理解线索权重有可能提高重要日常判断的质量。认识到判断对不同领域的决策具有重大影响，这项研究将研究如何利用调查结果来改善现实世界政策关注领域的判断。

Within the field of judgment and decision making, two distinct approaches have defined the debate over how people combine available information to form judgments: linear models and heuristic models. Since linear models excel at explaining judgment in some environments, and heuristic accounts are more effective in other environments, many researchers have argued that people switch between the different strategies depending on the particulars of the judgment task. In this research, the PI develops and tests a model of causal reasoning that subsumes both linear and heuristic models of judgments in a single, unified framework. The PI will also test other implications of the model that previous models have been unable to explain.In terms of broader impacts, this research has implications across a variety of fields. Medical diagnosis relies on doctors successfully assigning appropriate weights to cues (symptoms). Judges and juries are asked to combine cues (evidence) to make judgments of guilt and punishment. Investors look at cues such as various market conditions to decide on investment strategies. Understanding cue weighting has the potential improve the quality of important daily judgments. Recognizing that judgments have significant consequences for decision-making in a variety of domains, this research will examine how to leverage findings to improve judgment in real-world areas of policy concern.