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

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. The authors explore the two aforementioned problems in the context of discrete directional choices. Disaggregate experimental observations are collected from both stated-choice (SC) and realistic (or experimentally revealed) choice (RC) settings. The authors perform their analyses on the basis of four distinct criteria: parameter estimates, parameter ratios, predictions and prediction errors (in terms of disaggregate (i.e. individual-level) choice probabilities)). (i) The authors' results displayed a great degree of resemblance between the parameter estimate patterns emerged from both data sources including a perfect match between the sign and significance of the SC and RC estimates. Major observed discrepancies, however, were related to the scale of the estimates as well as certain coefficient ratios. In terms of the predictions (i.e. simulated probabilities) and prediction errors (i.e. based on a hold-out part of the sample), SC-based models performed surprisingly similar to those of their RC-based counterparts. (ii) The assumption of the decision rule made even a much less noticeable modelling difference (compared to the hypothetical bias) in both parameter estimates and predictions. The shift from utility to regret model led to only marginal differences in estimates, simulated probabilities and prediction errors. Compared to the hypothetical bias in particular, decision-rule specification was a much less impactful modelling component. These findings contribute to the accumulation of the empirical evidence that can determine how and when one can make best use of the SC methods which, in essence, demands the creation of links between hypothetical and real data in various contexts of choice. The authors' results suggested that, at least in certain contexts and applications, choice elicitation outcomes are reasonably consistent between the hypothetical and realistic settings. The findings also add to the existing evidence that point at the virtual neutrality of the discrete-choice modelling outcomes to the use of utility versus regret optimisation assumption as the choice rule. The study also leaves the authors with the further question of how the observed similarity/dissimilarity patterns (SC versus RC; and regret versus utility) would materialise if aggregate prediction measures are of main concern (as opposed to the use of disaggregate measures). The question has particular bearings on applications in which discrete-choice models are applied to simulate or predict a whole system (i.e. when discrete choice models perform as part of a broader model) as a common practice in transport studies. This question will be investigated in a subsequent work.

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  • English

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  • Accession Number: 01675658
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 7 2018 3:03PM