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Decision Field Theory: Equivalence with probit models and guidance for identifiability

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  • Szép, Teodóra
  • van Cranenburgh, Sander
  • Chorus, Caspar G.

Abstract

We examine identifiability and distinguishability in Decision Field Theory (DFT) models and highlight pitfalls and how to avoid them. In the past literature, the models’ parameters have been put forward as being able to capture the psychological processes in a decision maker’s mind during deliberation. DFT models have been widely used to analyse human decision making behaviour, and many empirical applications in the choice modelling domain rely solely on data concerning the observed final choice. This raises the question if such data are rich enough to allow for the identification of the model’s parameters. Insight into identifiability and distinguishability is crucial as it allows the researcher to determine which behavioural and psychological conclusions can or cannot be drawn from the estimated DFT model and how a DFT model can be specified in such a way that resulting parameters have meaningful interpretations. In this paper, we address this issue. To do this, we first show which specifications of DFT are equivalent to conventional probit models. Then, building on this equivalence result, we apply established analytical methods to highlight and explain the identification and distinguishability issues that arise when estimating DFT models on conventional choice data. We find evidence that some of the DFT models’ special cases suffer from identifiability issues. Our results warrant caution when DFT models are used to infer psychological processes and human behaviour from conventional choice data, and they help researchers choose the correct specification of DFT models.

Suggested Citation

  • Szép, Teodóra & van Cranenburgh, Sander & Chorus, Caspar G., 2022. "Decision Field Theory: Equivalence with probit models and guidance for identifiability," Journal of choice modelling, Elsevier, vol. 45(C).
  • Handle: RePEc:eee:eejocm:v:45:y:2022:i:c:s1755534522000161
    DOI: 10.1016/j.jocm.2022.100358
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    References listed on IDEAS

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    1. Walter, Eric & Pronzato, Luc, 1996. "On the identifiability and distinguishability of nonlinear parametric models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 42(2), pages 125-134.
    2. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    3. John D. Hey & Gianna Lotito & Anna Maffioletti, 2018. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 8, pages 189-219, World Scientific Publishing Co. Pte. Ltd..
    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. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    6. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    7. Busemeyer, Jerome R. & Diederich, Adele, 2002. "Survey of decision field theory," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 345-370, July.
    8. Axhausen, Kay W. & Hess, Stephane & König, Arnd & Abay, Georg & Bates, John J. & Bierlaire, Michel, 2008. "Income and distance elasticities of values of travel time savings: New Swiss results," Transport Policy, Elsevier, vol. 15(3), pages 173-185, May.
    9. Busemeyer, Jerome R. & Townsend, James T., 1992. "Fundamental derivations from decision field theory," Mathematical Social Sciences, Elsevier, vol. 23(3), pages 255-282, June.
    10. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    11. 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.
    12. Jerome R. Busemeyer & Jörg Rieskamp, 2014. "Psychological research and theories on preferential choice," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 3, pages 49-72, Edward Elgar Publishing.
    13. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
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