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Integrating attribute non-attendance and value learning with risk attitudes and perceptual conditioning

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  • Balbontin, Camila
  • Hensher, David A.
  • Collins, Andrew T.

Abstract

This study integrates multiple decision process strategies (Attribute Non-Attendance and Value Learning) with risk attitudes, perceptual conditioning and overt experience, which, to the best of our knowledge, has not been done before. We propose a way to include multiple heuristics as an alternative to current dominant paradigms, which supports a behavioural view that more than one heuristic simultaneously is at play in attribute and alternative processing. Our results show that including multiple decision process strategies, and integrating of all these components, significantly improves the model’s performance and enhances our understanding of how preferences are made.

Suggested Citation

  • Balbontin, Camila & Hensher, David A. & Collins, Andrew T., 2017. "Integrating attribute non-attendance and value learning with risk attitudes and perceptual conditioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 172-191.
    • Handle: RePEc:eee:transe:v:97:y:2017:i:c:p:172-191
    DOI: 10.1016/j.tre.2016.11.002
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    Cited by:

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    2. Hensher, David A. & Balbontin, Camila & Collins, Andrew T., 2018. "Heterogeneity in decision processes: Embedding extremeness aversion, risk attitude and perceptual conditioning in multiple process rules choice making," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 316-325.
    3. Balbontin, Camila & Hensher, David A. & Collins, Andrew T., 2017. "Is there a systematic relationship between random parameters and process heuristics?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 160-177.
    4. Kazagli, Evanthia & de Lapparent, Matthieu, 2023. "A discrete choice modeling framework of heterogenous decision rules accounting for non-trading behavior," Journal of choice modelling, Elsevier, vol. 48(C).
    5. Li, Zheng, 2018. "Unobserved and observed heterogeneity in risk attitudes: Implications for valuing travel time savings and travel time variability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 112(C), pages 12-18.
    6. Balbontin, Camila & Hensher, David A. & Collins, Andrew T., 2019. "How to better represent preferences in choice models: The contributions to preference heterogeneity attributable to the presence of process heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 218-248.
    7. Jang, Sunghoon & Rasouli, Soora & Timmermans, Harry, 2022. "The effect of task complexity on stated choice processes: The moderating role of cognitive ability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    8. Follett, Lendie & Naald, Brian Vander, 2023. "Heterogeneity in choice experiment data: A Bayesian investigation," Journal of choice modelling, Elsevier, vol. 46(C).
    9. Gonçalves, Tânia & Lourenço-Gomes, Lina & Pinto, Lígia M. Costa, 2022. "The role of attribute non-attendance on consumer decision-making: Theoretical insights and empirical evidence," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 788-805.
    10. Gonzalez-Valdes, Felipe & Heydecker, Benjamin G. & Ortúzar, Juan de Dios, 2022. "Quantifying behavioural difference in latent class models to assess empirical identifiability: Analytical development and application to multiple heuristics," Journal of choice modelling, Elsevier, vol. 43(C).

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