Journal of Choice Modelling最新文献

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.

This paper builds upon the work of Professor Marley, who, since the beginning of his long research career, has proposed rigorous axiomatics in the area of probabilistic choice models. Our study concentrates on models that can be applied to best and worst choice scaling experiments. We focus on those among these models that are based on strong assumptions about the underlying ranking of the alternatives with which the individual is assumed to be endowed when making the choice. Taking advantage of an inclusion–exclusion identity that we showed a few years ago, we propose a variety of best–worst choice probability models that could be implemented in software packages that are flourishing in this field.

The Cube model (Mallahi-Karai and Diederich, 2019) is a dynamic-stochastic approach for decision making situations including multiple alternatives. The underlying model is a multivariate Wiener process with drift, and its dimension is related to the number of alternatives in the choice set. Here we modify the model to account for Best–Worst settings. The choices are made in a number of episodes allowing the alternatives to be ranked from best to worst or from worst to best. The model makes predictions with respect to choice probabilities and (mean) choice response times. We show how the model can be implemented using Markov chains and test the model and a simpler variation of it on data from Hawkins et al. (2014b).

This paper investigates the role of the outside good utility function on admissible substitution patterns in multiple discrete/continuous demand models. We first present a set of novel results that characterize the functional form of quantity price effects within this class of models. The results highlight the relative inflexibility of many standard outside good utility functions. We then propose a new outside good utility function that admits more flexible marginal utility curves. Our empirical analysis uses household scanner panel data from the potato chip category, where we find empirical support for non-standard rates of satiation for the outside good. We then show how the restrictive substitution patterns induced by standard utility specifications may distort price elasticities and the evaluation of loyalty coupon targeting programs.

Previous studies on modelling the microscopic behaviour of pedestrians have focused on conflict resolution among pedestrians in pedestrian-pedestrian interactions. Many of these models propose alternate mechanisms to avoid conflicts by introducing repulsive forces between pedestrians or a set of predefined rules stating the precedence of movements to sidestep obstacles and other pedestrians. However, the possibility of formulating the decision-making mechanism pedestrians use to overcome conflicts as a gap-seeking behaviour has not been explored. In this study, resolving conflicts between opposing pedestrians is modelled as gap choice decisions made by individuals. Pedestrians looking for gaps or spaces in a crowd to facilitate their movement form the basis for such an analysis. The study compares pedestrians' gap acceptance behaviour across two scenarios: pedestrian movement in a field setup (Kumbh Mela) and a controlled experiment. Multiple gap choice decisions of individuals are modelled to understand the effect of individual-level heterogeneity on gap choices. Apart from the gap duration, spacing, position of gap, linear density, age, and presence of luggage significantly influenced the gap choices. Model validation is done using appropriate methods for both field and experimental data. The bootstrap method of internal validation and holdout validation is used to assess the performance of the estimated model on field data and experimental data, respectively. It is seen that the models have reasonable predictive and discriminative abilities. The analysis results also indicate that pedestrians tend to force gaps to facilitate movement in their natural state. Consequently, controlled experiments might have limitations in reproducing or motivating the participants to behave like a crowd.

In discrete choice experiment (DCE) studies, selecting the appropriate sample size remains a challenge. The question of the required sample size for a DCE is addressed in the literature in two distinct approaches: a rule-of-thumb approach and an approach based on the statistical error of the parameter of interest. The former is less accurate and does not depend on the desired power and significance level, whereas the latter requires knowing the complete design which may not be known at the planning stage. This paper proposes a new rule of thumb as well as a new regression-based method that requires knowing certain design characteristics rather than the complete design and takes into account the power and significance level. We compare the sample size estimated using the proposed methods with the true required sample size based on the statistical error of the parameter of interest and the approximations given by the existing rules of thumb. The results show that both the new rule of thumb and the regression-based approach improve the magnitude and proportion of underestimation compared to the most commonly used rule of thumb of Orme. Though the proposed approaches perform in general similarly to Tang’s rule which improves Orme’s rule, they seem to do better for large settings in terms of the number of choice sets and the number of alternatives per choice set in reducing underestimation. Moreover, we have demonstrated the possibility to adapt the regression-based approaches to take into account other scenarios and choice set complexity.

We propose a modeling framework that uses only cross-sectional data to disentangle labor supply and demand choices simultaneously. This modeling framework extends the labor-market analytical toolkits to adapt to environments where data are limited, flexibility in working hours is lacking, or structural changes are present, as is the case in most emerging and low-income countries. We showcase our model by using the 2011 China Household Finance Survey to decipher labor market choices in urban China. We find that the main discrepancies in labor supply between males and females are driven by the number and age of children, the lower utility of working rather than fewer job opportunities for females, and larger impacts of education and work experience on females’ job opportunities. Household wealth in the form of ‘cash inflow’ incentivizes individuals not to work, while wealth in the form of ‘stock’ induces higher utility to work for both males and females. The interpretation of empirical findings hinges on particular assumptions that might be disputed.