Weighting or aggregating? Investigating information processing in multi‐attribute choices

Mesfin G Genie* (Corresponding Author), Nicolas Krucien, Mandy Ryan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
5 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1291-1305
Number of pages15
JournalHealth Economics
Issue number6
Early online date19 Mar 2021
Publication statusPublished - 1 Jun 2021

Bibliographical note

The design of the choice experiment on which this paper draws was shaped by a team that included, alongside two of the authors, Professor Chris Burton, Professor Vikki Entwistle, Dr Terry Porteous and Dr Alison Elliott. The original CE study was funded by the Health Foundation. The University of Aberdeen and the Chief Scientist Office of the Scottish Government Health and Social Care Directorates fund the Health Economics Research Unit (HERU). The kidney transplantation choice experiment study was funded by the “Progetto di Ateneo KIDNEY” from the University of Padua (Italy). We would like to thank Daniel Rigby (The University of Manchester), Jürgen Maurer (Université de Lausanne), Giacomo Pasini (Ca' Foscari University of Venice), and Luca Corazzini (Ca' Foscari University of Venice) for their helpful comments.
Funding: Health Foundation. Grant Number: THF 7264


  • Multi-attribute choices
  • Attributes aggregation
  • Information processing
  • Choice modelling
  • Choice experiment
  • information processing
  • choice modelling
  • choice experiment
  • attributes aggregation
  • multi-attribute choices


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