IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v46y2023ics1755534522000549.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534522000549
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jocm.2022.100397?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Thijs Dekker & Paul (P.R.) Koster & Niek Mouter, 2019. "The economics of participatory value evaluation," Tinbergen Institute Discussion Papers 19-008/VIII, Tinbergen Institute.
    3. 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.
    4. Neill, Clinton L. & Lahne, Jacob, 2022. "Matching reality: A basket and expenditure based choice experiment with sensory preferences," Journal of choice modelling, Elsevier, vol. 44(C).
    5. Caputo, Vincenzina & Lusk, Jayson L., 2022. "The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis," Food Policy, Elsevier, vol. 109(C).
    6. 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.
    7. 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.
    8. Carson, Richard T. & Eagle, Thomas C. & Islam, Towhidul & Louviere, Jordan J., 2022. "Volumetric choice experiments (VCEs)," Journal of choice modelling, Elsevier, vol. 42(C).
    9. 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).
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Hössinger, Reinhard & Peer, Stefanie & Juschten, Maria, 2023. "Give citizens a task: An innovative tool to compose policy bundles that reach the climate goal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    3. Moeltner, Klaus & Neill, Clinton L. & Ramsey, Austin F. & Wang, Huaiyu, 2023. "Eliciting Choice Across Borders: Preferences for U.S. Rice Among Ethnic Chinese in China and the United States," 2023 Annual Meeting, July 23-25, Washington D.C. 335680, Agricultural and Applied Economics Association.
    4. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
    5. Beeramoole, Prithvi Bhat & Arteaga, Cristian & Pinz, Alban & Haque, Md Mazharul & Paz, Alexander, 2023. "Extensive hypothesis testing for estimation of mixed-Logit models," Journal of choice modelling, Elsevier, vol. 47(C).
    6. Dubey, Subodh & Cats, Oded & Hoogendoorn, Serge & Bansal, Prateek, 2022. "A multinomial probit model with Choquet integral and attribute cut-offs," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 140-163.
    7. Caputo, Vincenzina & Lusk, Jayson L., 2022. "The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis," Food Policy, Elsevier, vol. 109(C).
    8. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    9. Vittadini, Giorgio & Sturaro, Caterina & Folloni, Giuseppe, 2022. "Non-Cognitive Skills and Cognitive Skills to measure school efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    10. Yao, Rui & Bekhor, Shlomo, 2022. "A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 273-294.
    11. Rotteveel, Adriënne H. & Lambooij, Mattijs S. & van Exel, Job & de Wit, G. Ardine, 2022. "To what extent do citizens support the disinvestment of healthcare interventions? An exploration of the support for four viewpoints on active disinvestment in the Netherlands," Social Science & Medicine, Elsevier, vol. 293(C).
    12. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    13. Ali, Azam & Kalatian, Arash & Choudhury, Charisma F., 2023. "Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    14. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.
    15. Arkoudi, Ioanna & Krueger, Rico & Azevedo, Carlos Lima & Pereira, Francisco C., 2023. "Combining discrete choice models and neural networks through embeddings: Formulation, interpretability and performance," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    16. Weitao Jian & Kunxu Chen & Junshu He & Sifan Wu & Hongli Li & Ming Cai, 2023. "A Federated Personal Mobility Service in Autonomous Transportation Systems," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    17. Zongxiang Wang & Tianhao Chen & Wei Li & Kai Zhang & Jianwu Qi, 2023. "Construction and Demonstration of the Evaluation System of Public Participation Level in Urban Planning Based on the Participatory Video of ‘General Will—Particular Will’," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    18. Norzitah Abdul Karim & Azhan Rashid Senawi & Amirul Afif Muhamat & Nurhuda Nizar & Norazlina Abd Wahab & Musaab Ab Kadir, 2023. "Do Economic Depressions Contribute to CO2 Emissions? An ARDL Bound Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 175-180, March.
    19. Bhat, Chandra R. & Mondal, Aupal & Pinjari, Abdul Rawoof & Saxena, Shobhit & Pendyala, Ram M., 2022. "A multiple discrete continuous extreme value choice (MDCEV) model with a linear utility profile for the outside good recognizing positive consumption constraints," Transportation Research Part B: Methodological, Elsevier, vol. 156(C), pages 28-49.
    20. Mouter, Niek & Jara, Karen Trujillo & Hernandez, Jose Ignacio & Kroesen, Maarten & de Vries, Martijn & Geijsen, Tom & Kroese, Floor & Uiters, Ellen & de Bruin, Marijn, 2022. "Stepping into the shoes of the policy maker: Results of a Participatory Value Evaluation for the Dutch long term COVID-19 strategy," Social Science & Medicine, Elsevier, vol. 314(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eejocm:v:46:y:2023:i:c:s1755534522000549. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-choice-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.