Journal of Choice Modelling最新文献

Consumers are often assumed to use a two-stage decision process, screening out products in the first step and choosing among the remaining alternatives in the second step. When analyzing data from discrete choice studies, a compensatory decision strategy is usually presumed. Gilbride and Allenby (2004) introduced a method to model a decision process in a choice-based conjoint analysis combining the compensatory assumption with the two-stage decision process. Respondents first screen out alternatives that do not meet minimum requirements for attributes, followed by a choice between the remaining alternatives using the compensatory rule.

In this paper, we extend their approach by considering not only screening with a minimum threshold but also with a maximum value for every attribute. We compare this extension to the original method by Gilbride and Allenby (2004) and a single-step compensatory model. We do so on the basis of one simulation scenario as well as three empirical conjoint datasets.

The results indicate that two-sided screening is applied especially to prices. Both the original and extended models exhibit nearly identical performance. However, they outperform the one-step choice model that ignores screening in terms of fit and predictive validity.

Use of preference information to infer risk tolerance has increased in recent years as a way to inform benefit-risk evaluations in regulatory and medical decision making. However, a framework for the measurement of tolerance for multiple uncertain outcomes has not been formalized when choices do not comply with expected utility theory (EUT). We developed a formal analytic framework for the measurement of preferences through choices under uncertainty with multiple risks. Based on the analytic framework, we find that violations of EUT can lead to interaction effects between uncertain outcomes, not just nonlinearities in the disutility of risks. Our framework also implies that measures of risk tolerance derived from utility, such as maximum-acceptable risk, must consider all relevant risks jointly if their effect on choices is expected to violate EUT. Somewhat reassuringly, however, we find that cross-outcome effects are expected to be negligible when the probabilities of other outcomes approach certainty. Finally, we identify a simple test that can help evaluate whether preferences for one uncertain outcome are affected by other uncertain outcomes.

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual’s choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.