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On the robustness of efficient experimental designs towards the underlying decision rule

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  • van Cranenburgh, Sander
  • Rose, John M.
  • Chorus, Caspar G.

Abstract

We present a methodology to derive efficient designs for Stated Choice (SC) experiments based on Random Regret Minimisation (RRM) behavioural assumptions. This complements earlier work on the design of efficient SC experiments based on Random Utility Maximisation (RUM) models. Capitalizing on this methodology, and using both analytical derivations and empirical data, we investigate the importance of the analyst’s assumption regarding the underlying decision rule used to generate the efficient experimental design. We find that conventional RUM-efficient designs can be statistically highly inefficient in cases where RRM is the better representation of the actual choice behaviour, and vice versa. Furthermore, we present a methodology to construct efficient designs that are robust towards the uncertainty on the side of the analyst regarding the underlying decision rule.

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  • van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.
    • Handle: RePEc:eee:transa:v:109:y:2018:i:c:p:50-64
    DOI: 10.1016/j.tra.2018.01.001
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    Cited by:

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    2. van Cranenburgh, Sander & Bliemer, Michiel C.J., 2019. "Information theoretic-based sampling of observations," Journal of choice modelling, Elsevier, vol. 31(C), pages 181-197.
    3. Danaf, Mazen & Atasoy, Bilge & de Azevedo, Carlos Lima & Ding-Mastera, Jing & Abou-Zeid, Maya & Cox, Nathaniel & Zhao, Fang & Ben-Akiva, Moshe, 2019. "Context-aware stated preferences with smartphone-based travel surveys," Journal of choice modelling, Elsevier, vol. 31(C), pages 35-50.
    4. van Cranenburgh, Sander & Meyerhoff, Jürgen & Rehdanz, Katrin & Wunsch, Andrea, 2024. "On the impact of decision rule assumptions in experimental designs on preference recovery: An application to climate change adaptation measures," Journal of choice modelling, Elsevier, vol. 50(C).
    5. van Cranenburgh, Sander & Collins, Andrew T., 2019. "New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules," Journal of choice modelling, Elsevier, vol. 31(C), pages 104-123.
    6. Tian, Qi & Zhao, Jinhua, 2018. "Regret Minimization in Decision Making: Implications for Choice Modeling and Policy Design," 2018 Annual Meeting, August 5-7, Washington, D.C. 274016, Agricultural and Applied Economics Association.

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