Gabriel Nova, Sander van Cranenburgh, Stephane Hess
arXiv 3 Nov 2024 · Econometrics · publishedJournal of Choice Modelling (2025) · 1 citations (OpenAlex)
arXiv:2411.01704 · PDF · DOI · OpenAlex · Extracted main text
Discrete Choice Modelling serves as a robust framework for modelling human choice behaviour across various disciplines. Building a choice model is a semi structured research process that involves a combination of a priori assumptions, behavioural theories, and statistical methods. This complex set of decisions, coupled with diverse workflows, can lead to substantial variability in model outcomes. To better understand these dynamics, we developed the Serious Choice Modelling Game, which simulates the real world modelling process and tracks modellers' decisions in real time using a stated preference dataset. Participants were asked to develop choice models to estimate Willingness to Pay values to inform policymakers about strategies for reducing noise pollution. The game recorded actions across multiple phases, including descriptive analysis, model specification, and outcome interpretation, allowing us to analyse both individual decisions and differences in modelling approaches. While our findings reveal a strong preference for using data visualisation tools in descriptive analysis, it also identifies gaps in missing values handling before model specification. We also found significant variation in the modelling approach, even when modellers were working with the same choice dataset. Despite the availability of more complex models, simpler models such as Multinomial Logit were often preferred, suggesting that modellers tend to avoid complexity when time and resources are limited. Participants who engaged in more comprehensive data exploration and iterative model comparison tended to achieve better model fit and parsimony, which demonstrate that the methodological choices made throughout the workflow have significant implications, particularly when modelling outcomes are used for policy formulation.
appendix boundary found by appendix_titled_section at “Appendix” · 81% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Train, K.E (2009) Discrete choice methods with simulation | 0.928 | 4 | 3 | 100% |
| 2 | Ben-Akiva, M.E., Lerman, S.R (1985) Discrete choice analysis: theory and application to travel demand. volume 9 | 0.874 | 5 | 2 | 100% |
| 3 | Mariel, P., Hoyos, D., Meyerhoff, J., Czajkowski, M., Dekker, T., Gl… (2021) Environmental valuation with discrete choice experiments: Guidance on design, implementation and data analysis | 0.874 | 5 | 2 | 100% |
| 4 | Beeramoole, P.B., Arteaga, C., Pinz, A., Haque, M.M., Paz, A (2023) Extensive hypothesis testing for estimation of mixed-logit models | 0.737 | 3 | 2 | 100% |
| 5 | Parady, G., Ory, D., Walker, J (2021) The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of va… | 0.737 | 3 | 2 | 100% |
| 6 | Daly, A., Hess, S., de Jong, G (2012) Calculating errors for measures derived from choice modelling estimates self | 0.644 | 2 | 2 | 100% |
| 7 | Hensher, D.A., Rose, J.M., Greene, W.H (2015) Applied choice analysis | 0.644 | 2 | 2 | 100% |
| 8 | Hess, S., Daly, A (2024) Handbook of choice modelling self | 0.644 | 2 | 2 | 100% |
| 9 | Louviere, J (2000) Stated Choice Methods: Analysis and Applications | 0.644 | 2 | 2 | 100% |
| 10 | McFadden, D (1974) The measurement of urban travel demand | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 65 scored citations.