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Decision field theory: An extension for real-world settings

Author

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  • Hancock, Thomas O.
  • Hess, Stephane
  • Choudhury, Charisma F.
  • Tsoleridis, Panagiotis

Abstract

Decision field theory (DFT) is a model originally developed in cognitive psychology to explain behavioural phenomena such as context effects and decision-making under time pressure. Given this focus, the model has primarily been used to explain choices observed under controlled laboratory settings, with little attention paid to generalisability. Recent work has improved the mathematical foundations of DFT, making it a tractable model that is easier to apply to a wider variety of choice contexts. In particular, the inclusion of attribute importance parameters has led to successful applications to multi-alternative multi-attribute choice settings, notably with stated preference data in transport. However, thus far, implementations to real-life behaviour (i.e., revealed preference, RP, data) have been limited. The aim of this paper is to extend DFT for larger and more real-world applications, where data may be more ‘noisy’ and prone to larger variances of the error term. A theoretical extension for the model is presented, relaxing the assumption of independent normal error terms to capture heteroskedasticity. We apply the new model specification to two large-scale revealed preference datasets, also incorporating a range of sociodemographic variables. The new ‘heteroskedastic’ DFT model substantially outperforms the original version of DFT, as well as choice models based on econometric theory, in both estimation and validation subsets.

Suggested Citation

  • Hancock, Thomas O. & Hess, Stephane & Choudhury, Charisma F. & Tsoleridis, Panagiotis, 2024. "Decision field theory: An extension for real-world settings," Journal of choice modelling, Elsevier, vol. 52(C).
  • Handle: RePEc:eee:eejocm:v:52:y:2024:i:c:s1755534524000277
    DOI: 10.1016/j.jocm.2024.100495
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    1. Simonson, Itamar, 1989. "Choice Based on Reasons: The Case of Attraction and Compromise Effects," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(2), pages 158-174, September.
    2. Joshua I. Gold & Michael N. Shadlen, 2000. "Representation of a perceptual decision in developing oculomotor commands," Nature, Nature, vol. 404(6776), pages 390-394, March.
    3. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    4. Hancock, Thomas O. & Hess, Stephane & Choudhury, Charisma F., 2018. "Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 18-40.
    5. Michel Bierlaire, 2006. "A theoretical analysis of the cross-nested logit model," Annals of Operations Research, Springer, vol. 144(1), pages 287-300, April.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    7. Busemeyer, Jerome R. & Townsend, James T., 1992. "Fundamental derivations from decision field theory," Mathematical Social Sciences, Elsevier, vol. 23(3), pages 255-282, June.
    8. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    9. Brathwaite, Timothy & Walker, Joan L., 2018. "Asymmetric, closed-form, finite-parameter models of multinomial choice," Journal of choice modelling, Elsevier, vol. 29(C), pages 78-112.
    10. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    11. Moshe Ben-Akiva & Joffre Swait, 1986. "The Akaike Likelihood Ratio Index," Transportation Science, INFORMS, vol. 20(2), pages 133-136, May.
    12. Pettibone, Jonathan C., 2012. "Testing the effect of time pressure on asymmetric dominance and compromise decoys in choice," Judgment and Decision Making, Cambridge University Press, vol. 7(4), pages 513-521, July.
    13. Thomas Otter & Joe Johnson & Jörg Rieskamp & Greg Allenby & Jeff Brazell & Adele Diederich & J. Hutchinson & Steven MacEachern & Shiling Ruan & Jim Townsend, 2008. "Sequential sampling models of choice: Some recent advances," Marketing Letters, Springer, vol. 19(3), pages 255-267, December.
    14. Busemeyer, Jerome R. & Diederich, Adele, 2002. "Survey of decision field theory," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 345-370, July.
    15. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    16. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    17. Chiara Calastri & Romain Crastes dit Sourd & Stephane Hess, 2020. "We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning," Transportation, Springer, vol. 47(1), pages 175-201, February.
    18. Huber, Joel & Payne, John W & Puto, Christopher, 1982. "Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(1), pages 90-98, June.
    19. Hancock, Thomas O. & Hess, Stephane & Marley, A.A.J. & Choudhury, Charisma F., 2021. "An accumulation of preference: Two alternative dynamic models for understanding transport choices," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 250-282.
    20. Horowitz, Joel, 1981. "Identification and diagnosis of specification errors in the multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 15(5), pages 345-360, October.
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