Journal of Choice Modelling最新文献

Best-worst scaling (BWS) is a theory-driven choice experiment used for the prioritization of a finite number of options. Within the context of prioritization, BWS is also known as MaxDiff, BWS object case, and BWS Case 1. Now used in numerous fields, we conducted a transdisciplinary literature review of all published applications of BWS focused on prioritization to compare norms on the development, design, administration, analysis, and quality of BWS applications across fields. We identified 526 publications published before 2023 in the fields of health (n = 195), agriculture (n = 163), environment (n = 50), business (n = 50), linguistics (n = 24), transportation (n = 24), and other fields (n = 24). The application of BWS has been doubling every four years. BWS is applied globally with greatest frequency in North America (27.0%). Most studies had a clearly stated purpose (94.7%) that was empirical in nature (89.9%) with choices elicited in the present tense (90.9%). Apart from linguistics, most studies: applied at least one instrument development method (94.3%), used BWS to assess importance (63.1%), used ‘most/least’ anchors (85.7%), and conducted heterogeneity analysis (69.0%). Studies predominantly administered surveys online (58.0%) and infrequently included formal sample size calculations (2.9%). BWS designs in linguistics differed significantly from other fields regarding the average number of objects (p < 0.01), average number of tasks (p < 0.01), average number of objects per task (p = 0.03), and average number of tasks presented to participants (p < 0.01). On a 5-point scale, the average PREFS score was 3.0. This review reveals the growing application of BWS for prioritization and promises to foster new transdisciplinary avenues of inquiry.

In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that is specifically designed to preserve the richness of observations originally present in a dataset, while reducing the computational complexity of the estimation process. Our approach, called LSH-DR, leverages locality-sensitive hashing to create homogeneous clusters, from which representative observations are then sampled and weighted. We demonstrate the efficacy of our approach by applying it on a real-world mode choice dataset: the obtained results show that the samples generated by LSH-DR allow for substantial savings in estimation time while preserving estimation efficiency at little cost.

Efficient experimental designs aim to maximise the information obtained from stated choice data to estimate discrete choice models' parameters statistically efficiently. Almost without exception efficient experimental designs assume that decision-makers use a Random Utility Maximisation (RUM) decision rule. When using such designs, researchers (implicitly) assume that the decision rule used to generate the design has no impact on respondents' choice behaviour. This study investigates whether the decision rule assumption underlying an experimental design affects respondents' choice behaviour. We use four stated choice experiments on coastal adaptation to climate change: Two are based on experimental designs optimised for utility maximisation and two are based on experimental designs optimised for a mixture of RUM and Random Regret Minimisation (RRM). Generally, we find that respondents place value on adaptation measures (e.g., dykes and beach nourishments). We evaluate the models' fits and investigate whether some choice tasks particularly invoke RUM or RRM decision rules. For the latter, we develop a new sampling-based approach that avoids the confounding between preference and decision rule heterogeneity. We find no evidence that RUM-optimised designs invoke RUM-consistent choice behaviour. However, we find a relationship between some of the attributes and decision rules, and compelling evidence that some choice tasks invoke RUM consistent behaviour while others invoke RRM consistent behaviour. This implies that respondents’ choice behaviour and choice modelling outcomes are not exogenous to the choice tasks, which can be particularly critical when information on preferences is used to inform actual decision-making on a sensitive issue of common interest as climate change.

Japan is amongst those countries known for long hours and an inflexible working culture that makes it difficult to pursue work-life balance. The question is what aspects of job market flexibility are most valuable to Japanese women and men and to what extent are these values are driven by feelings of guilt. Using a nationwide sample of 1046 working-age adults, we conduct a choice experiment that examines willingness to trade wages against changes in job characteristics such as the extent of overtime, job security, the possibility of work transfer and relocation. Our results suggest that: i) workers have high WTP (willingness to pay) to avoid extreme overtime and internal transfers but not to safeguard job security or to avoid relocation, ii) women have higher WTP than men, and iii) the gap is driven only in part by feelings of guilt. Perhaps surprisingly, women's preferences are generally not affected by the presence or absence of children in the household while men's WTP for work-life balance is generally lower in the presence of children, but less influenced by guilt.

This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified as a binary classification algorithm, to develop a fully interpretable deep learning-based ordinal regression model. As the formulation of the Ordinal-ResLogit model enjoys the Residual Neural Networks concept, our proposed model addresses the main constraint of machine learning algorithms, known as black-box. Moreover, the Ordinal-ResLogit model, as a binary classification framework for ordinal data, guarantees consistency among binary classifiers. We showed that the resulting formulation is able to capture underlying unobserved heterogeneity from the data as well as being an interpretable deep learning-based model. Formulations for market share, substitution patterns, and elasticities are derived. We compare the performance of the Ordinal-ResLogit model with an Ordered Logit Model using a stated preference (SP) dataset on pedestrian wait time and a revealed preference (RP) dataset on travel distance. Our results show that Ordinal-ResLogit outperforms the traditional ordinal regression model. Furthermore, the results obtained from the Ordinal-ResLogit RP model show that travel attributes such as driving and transit cost have significant effects on choosing the location of non-mandatory trips. In terms of the Ordinal-ResLogit SP model, our results highlight that the road-related variables and traffic condition are contributing factors in the prediction of pedestrian waiting time such that the mixed traffic condition significantly increases the probability of choosing longer waiting times.

To study the mechanism of pedestrians' route choice behaviors under non-emergency conditions, we conducted a series of route choice experiments. Participants were required to choose between two routes. Possible controls, including bottleneck, social distancing, extra reward, were tested in the experiments. Results shows that the bottleneck effect can dramatically influence the route-choice behaviors, whereas the impact of social distancing and reward were much weaker. Five typical logit models, including Binary Logit (BL) model, Mixed Logit (ML) model, Panel Logit (PL) model, Latent Class Logit (LCL) model and Latent Class Logit including Panel effect (LCL-P) model were employed. PL and LCL models performed better in this study, while the results of LCL-P model were the best. This suggests the existence and importance of heterogeneity in route choice behavior. Two classes of pedestrians were identified, with one being comfort-seeking and the other being speed-seeking. ML model did not perform well in this study, which is contrary to some previous studies. All these results could be helpful for understanding the essence of pedestrians’ route choice behaviors.

At-home antigen (rapid) tests have been successfully deployed in many countries to quickly detect COVID-19 cases. Whereas antigen tests have multiple advantages, they tend to have higher rates of false diagnosis than polymerase chain reaction (PCR) tests. Since individuals tend to process risk non-linearly, an ad-hoc method is required to adequately assess preferences for test features. In this paper, we propose a methodology based on random utility maximization and elements of prospect theory that produces willingness-to-pay estimates for different test attributes while accounting for differences between objective and perceived probabilities of false positive or negative results. We use this methodology to analyze stated preference data for COVID-19 tests in the United States. Results show that, on average, low probabilities were underestimated and mid-range probabilities were overestimated. We also found that false positive results were more burdensome than false negative outcomes, which shows that there is a degree of willful ignorance (Ehrich and Irwin, 2005) in our sample. Finally, our findings indicate that respondents tended to prefer tests with faster turn-around times and less invasive collection methods. In a case study, we show how our results can be used to assess pricing for a given test.

Sequential sampling models have provided accurate accounts of people’s choice, response time, and preference strength in value-based decision-making tasks. Conventionally, these models are developed as Markov-type models (such as random walks or diffusion models) following the Kolmogorov axioms. Quantum probability theory has been proposed as an alternative framework upon which to develop models of cognition, including quantum random walk models. When modeling people’s behavior during decision-making tasks, previous work has demonstrated that both the Markov and quantum models have their respective strengths. Recently, the open system model, which is a hybrid version of the Markov and quantum models, has been shown to provide a more accurate account of preference strength compared to the Markov and quantum models in isolation. In this work, we extend the open system model to make predictions on pairwise choice and response time. We report a new experiment on preferential choice between gift cards, and we compare the fits of the open system model to the pure Markov and pure quantum random walk models using AIC, BIC, and ${\chi }^2$ tests. Although the pure Markov model was favored for most participants, a substantial number required the more complex open system model.

Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities. We present a method of efficiently incorporating attitudinal indicators in the specification of LCCM, by introducing Artificial Neural Networks (ANN) to formulate latent variables constructs. This formulation overcomes structural equations in its capability of exploring the relationship between the attitudinal indicators and the decision choice, given the Machine Learning (ML) flexibility and power in capturing unobserved and complex behavioural features, such as attitudes and beliefs. All of this while still maintaining the consistency of the theoretical assumptions presented in the Generalized Random Utility model and the interpretability of the estimated parameters. We test our proposed framework for estimating a Car-Sharing (CS) service subscription choice with stated preference data from Copenhagen, Denmark. The results show that our proposed approach provides a complete and realistic segmentation, which helps design better policies.

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT services and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.