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Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments

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  • Hernandez, Jose Ignacio
  • van Cranenburgh, Sander
  • Chorus, Caspar
  • Mouter, Niek

Abstract

We propose three procedures based on association rules (AR) learning and random forests (RF) to support the specification of a portfolio choice model applied in data from complex choice experiment data, specifically a Participatory Value Evaluation (PVE) choice experiment. In a PVE choice experiment, respondents choose a combination of alternatives, subject to a resource constraint. We combine a methodological-iterative (MI) procedure with AR learning and RF models to support the specification of parameters of a portfolio choice model. Additionally, we use RF model predictions to contrast the validity of the behavioural assumptions of different specifications of the portfolio choice model. We use data of a PVE choice experiment conducted to elicit the preferences of Dutch citizens for lifting COVID-19 measures. Our results show model fit and interpretation improvements in the portfolio choice model, compared with conventional model specifications. Additionally, we provide guidelines on the use of outcomes from AR learning and RF models from a choice modelling perspective.

Suggested Citation

  • Hernandez, Jose Ignacio & van Cranenburgh, Sander & Chorus, Caspar & Mouter, Niek, 2023. "Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments," Journal of choice modelling, Elsevier, vol. 46(C).
    • Handle: RePEc:eee:eejocm:v:46:y:2023:i:c:s1755534522000549
    DOI: 10.1016/j.jocm.2022.100397
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    References listed on IDEAS

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    1. Carson, Richard T. & Eagle, Thomas C. & Islam, Towhidul & Louviere, Jordan J., 2022. "Volumetric choice experiments (VCEs)," Journal of choice modelling, Elsevier, vol. 42(C).
    2. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
    3. Thijs Dekker & Paul (P.R.) Koster & Niek Mouter, 2019. "The economics of participatory value evaluation," Tinbergen Institute Discussion Papers 19-008/VIII, Tinbergen Institute.
    4. Mulderij, Lisanne S. & Hernández, José Ignacio & Mouter, Niek & Verkooijen, Kirsten T. & Wagemakers, Annemarie, 2021. "Citizen preferences regarding the public funding of projects promoting a healthy body weight among people with a low income," Social Science & Medicine, Elsevier, vol. 280(C).
    5. Ortelli, Nicola & Hillel, Tim & Pereira, Francisco C. & de Lapparent, Matthieu & Bierlaire, Michel, 2021. "Assisted specification of discrete choice models," Journal of choice modelling, Elsevier, vol. 39(C).
    6. Mouter, Niek & Koster, Paul & Dekker, Thijs, 2021. "Contrasting the recommendations of participatory value evaluation and cost-benefit analysis in the context of urban mobility investments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 54-73.
    7. Niek Mouter & Jose Ignacio Hernandez & Anatol Valerian Itten, 2021. "Public participation in crisis policymaking. How 30,000 Dutch citizens advised their government on relaxing COVID-19 lockdown measures," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-42, May.
    8. Rotteveel, A. H. & Lambooij, M. S. & Over, E. A. B. & Hernández, J. I. & Suijkerbuijk, A. W. M. & de Blaeij, A. T. & de Wit, G. A. & Mouter, N., 2022. "If you were a policymaker, which treatment would you disinvest? A participatory value evaluation on public preferences for active disinvestment of health care interventions in the Netherlands," Health Economics, Policy and Law, Cambridge University Press, vol. 17(4), pages 428-443, October.
    9. Caputo, Vincenzina & Lusk, Jayson L., 2022. "The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis," Food Policy, Elsevier, vol. 109(C).
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