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Weighting or aggregating? Investigating information processing in multi‐attribute choices
Author(s) -
Genie Mesfin G.,
Krucien Nicolas,
Ryan Mandy
Publication year - 2021
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.4245
Subject(s) - weighting , computer science , information processing , data mining , information retrieval , econometrics , mathematics , medicine , psychology , cognitive psychology , radiology
Abstract Multi‐attribute choices are commonly analyzed in economics to value goods and services. Analysis assumes individuals consider all attributes, making trade‐offs between them. Such decision‐making is cognitively demanding, often triggering alternative decision rules. We develop a new model where individuals aggregate multi‐attribute information into meta‐attributes. Applying our model to a choice experiment (CE) dataset, accounting for attribute aggregation (AA) improves model fit. The probability of adopting AA is greater for: homogenous attribute information; participants who had shorter response time and failed the dominance test; and for later located choices. Accounting for AA has implications for welfare estimates. Our results underline the importance of accounting for information processing rules when modelling multi‐attribute choices.