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Hypothetical bias and decision-rule effect in modelling discrete directional choices

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  • Haghani, Milad
  • Sarvi, Majid

Abstract

This empirical study investigates, in parallel, two key questions in relation with modelling discrete choices. (i) To what extent econometric modelling estimates and predictions derived from responses to hypothetical choice scenarios differ from those of an equivalent realistic choice context (i.e. hypothetical bias)? (ii) To what extent econometric modelling estimates and predictions differ as a result of postulating “regret minimisation” as the decision rule as opposed to the “utility maximisation” assumption (i.e. decision-rule effect)? The magnitude of these two effects are also compared relatively.

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  • Haghani, Milad & Sarvi, Majid, 2018. "Hypothetical bias and decision-rule effect in modelling discrete directional choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 361-388.
    • Handle: RePEc:eee:transa:v:116:y:2018:i:c:p:361-388
    DOI: 10.1016/j.tra.2018.06.012
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    Cited by:

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    2. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part I. Integrative synthesis of empirical evidence and conceptualisation of external validity," Papers 2102.02940, arXiv.org.
    3. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods," Journal of choice modelling, Elsevier, vol. 41(C).
    4. Haghani, Milad & Bliemer, Michiel C.J. & Rose, John M. & Oppewal, Harmen & Lancsar, Emily, 2021. "Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimag," Journal of choice modelling, Elsevier, vol. 41(C).
    5. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    6. Shahhoseini, Zahra & Sarvi, Majid, 2019. "Pedestrian crowd flows in shared spaces: Investigating the impact of geometry based on micro and macro scale measures," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 57-87.
    7. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers 2102.02945, arXiv.org.
    8. Kingsley Adjenughwure & Basil Papadopoulos, 2019. "Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings," Sustainability, MDPI, vol. 11(4), pages 1-19, February.
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    10. Zhou, Zi-Xuan & Nakanishi, Wataru & Asakura, Yasuo, 2021. "Route choice in the pedestrian evacuation: Microscopic formulation based on visual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    11. Haghani, Milad & Sarvi, Majid, 2019. "Laboratory experimentation and simulation of discrete direction choices: Investigating hypothetical bias, decision-rule effect and external validity based on aggregate prediction measures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 134-157.
    12. Zhao, Jianfeng & Liu, Henry J. & Love, Peter E.D. & Greenwood, David J. & Sing, Michael C.P., 2022. "Public-private partnerships: A dynamic discrete choice model for road projects," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    13. Zhou, Zi-Xuan & Nakanishi, Wataru & Asakura, Yasuo, 2021. "Data-driven framework for the adaptive exit selection problem in pedestrian flow: Visual information based heuristics approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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