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Optimal cue aggregation in the absence of criterion knowledge
Author(s) -
Zhao Wenjia Joyce,
DavisStober Clintin P.,
Bhatia Sudeep
Publication year - 2019
Publication title -
journal of behavioral decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 76
eISSN - 1099-0771
pISSN - 0894-3257
DOI - 10.1002/bdm.2123
Subject(s) - weighting , aggregate (composite) , space (punctuation) , variable (mathematics) , projection (relational algebra) , scheme (mathematics) , task (project management) , value (mathematics) , computer science , psychology , artificial intelligence , mathematics , social psychology , cognitive psychology , machine learning , algorithm , medicine , mathematical analysis , materials science , management , economics , composite material , radiology , operating system
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.