Causal Model Based Cue Weighting

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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.