Optimal cue aggregation in the absence of criterion knowledge

Abstract

The study of multi‐cue judgment investigates how decision makers aggregate cues to predict the value of a criterion variable. We consider a multi‐cue judgment task in which decision makers have prior knowledge of inter‐cue relationships but are ignorant of how the cues correlate with the criterion. In this setting, a naive judgment strategy prescribes weighting the cues equally. Although many participants are well described via an equal weighting scheme, we find that a substantial minority of participants make predictions consistent with a weighting scheme based on a low‐dimensional projection of the cue space that optimally takes into account inter‐cue correlations. The use of such a weighting scheme is consistent with minimizing maximal error in prediction when the cue‐criterion relationships are unknown.