Journal of Choice Modelling最新文献

This paper uses a discrete choice experiment to elicit winemakers' preferences towards climate change adaptation options in the Spanish Rioja region. The experiment includes different potential adaptation strategies such as relocation, the use of various grape clones, the installation of an irrigation system, the construction of vegetative or artificial structures to shade the vines, and oenological adaptations. The results show that the most widely accepted strategy is the installation of irrigation and shading structures. In contrast, the least accepted strategy is relocating, which is a costly and long-term solution. The monetary measures obtained are useful for policymakers because they show how much financial assistance will be required to adapt to climate change and maintain the high-quality wine production of the region. We also investigate the precision that can be expected from choice models with small samples through a simulation study, demonstrating the possibility of recovering true parameter values with small sample sizes using a specific experimental design tailored to the attributes and levels of the study.

Discrete choice (DC) methods provide a convenient approach for preference elicitation and they lead to unbiased estimates of preference model parameters if the parameterization of the value function allows for a good description of the preferences. On the other hand, indifference elicitation (IE) has been suggested as a direct trade-off estimator for preference elicitation in decision analysis decades ago, but has not found widespread application in statistical analysis frameworks as for discrete choice methods. We develop a hierarchical, probabilistic model for IE that allows us to do Bayesian inference similar to DC methods. A case study with synthetically generated data allows us to investigate potential bias and to estimate parameter uncertainty over a wide range of numbers of replies and elicitation uncertainties for both DC and IE. Through an empirical case study with laboratory-scale choice and indifference experiments, we investigate the feasibility of the approach and the excess time needed for indifference replies. Our results demonstrate (i) the absence of bias of the suggested methodology, (ii) a reduction in the uncertainty of estimated parameters by about a factor of three or a reduction of the required number of replies to achieve a similar accuracy as with DC by about a factor of ten, (iii) the feasibility of the approach, and (iv) a median increase in time needed for indifference reply of about a factor of three. If the set of respondents is small, the higher elicitation effort may be worth to achieve a reasonable accuracy in estimated value function parameters.

Discrete choice (DCE) and rating scale experiments (RSE) are commonly applied procedures for eliciting preference judgments in a plethora of applied settings such as consumer choices, health care, and transport economics. An almost universal assumption is that actual “ground truth” preferences do not depend on which elicitation procedure is used. It is usually not possible to test this assumption, because typical studies feature response options for which there is no objectively correct response. To make progress on testing this assumption, we conducted a perceptual discrimination experiment where response options varied on a single attribute – stimulus saturation level – with a known objectively correct response. We had the same participants complete both a choice task (CT) and rating scale (RS) version of the experiment, allowing a direct examination of the assumption of a common representation. Our CT featured many characteristics that define a DCE, however, in order to have a known objectively correct response, it also differed in a few important ways. To test the assumption of a common representation, we developed a cognitive model with a response mechanism for both CT and RS. This enabled us to compare a model version that featured one shared latent stimulus representation across CT and RS versus a version which featured separate representations. Our results support the assumption that a single internal state supports both CT and RS responses, and also suggest that the CT method might provide more sensitive measurement of internal states than the RS method.

In this study, we employ a choice experiment to analyze New York City residents’ preferences for online grocery shopping at the beginning of the COVID-19 pandemic. We employ a latent class specification to identify three market segments and estimate consumers’ willingness to pay for a variety of attributes of online grocery services related to the quality of the stock, delivery characteristics, and the cost of the online order. We characterize consumers in each segment by their observed characteristics as well as fear-related latent variables. On the one hand, we find that individuals who are actively protecting themselves against COVID-19 have a higher willingness to pay for almost all attributes. On the other hand, consumers who avoid crowds have a lower willingness to pay, but they assign relatively higher importance to no-contact delivery.

Advanced econometric models used in the field of transport or marketing are becoming increasingly sophisticated and able to capture complex decision making and outcomes. In this paper, we apply state-of-the-art discrete-continuous choice models to the field of Ecology, in particular to model activity engagement of the population of Swedish Brown bears. Using data from GPS collars that track wild animals over time, we estimate a Multiple Discrete-Continuous Extreme Value (MDCEV) model to understand activity engagement and duration as a function of both bear characteristics and other external factors. We show that the methodology is not only suitable to address this aim, but also allows us to produce insights into the connection between the animal's age and gender and activity engagement as well as the links with climate variables (temperature and precipitation) and human activity (hunting).

Many multivariate model systems involve the estimation of a covariance matrix that must be positive-definite. A common strategy to ensure positive definiteness of the covariance matrix is through the use of a Cholesky parameterization of the covariance matrix. However, several model systems require imposing restrictions on the elements of the covariance elements. For instance, modelling systems may require fixing some (or all) of the diagonal elements in the covariance matrix to unity due to identification considerations. However, imposing such restrictions using the traditional Cholesky decomposition approach is not feasible and requires the additional parameterization of the Cholesky elements.

In this paper, we explore a separation-based strategy with spherical parameterization of the Cholesky matrix to impose restrictions on the covariance matrix. Importantly, using this separation-based parameterization strategy, we also explore the possibility of restricting some covariance (or correlation) terms to zero. The effectiveness of the proposed strategy is assessed through extensive simulation experiments. The results from the simulation experiments highlight better performance of the separation-based strategy in terms of recovery of model parameters – particularly those in the covariance matrix, than the traditional Cholesky parameterization approach. Finally, the proposed strategy is implemented in a joint multivariate binary probit ordered probit model system to analyze the usage (and the extent of use) of non-private modes of transportation in Bengaluru, India. In doing so, the proposed strategy is implemented to restrict several correlations to zero, thus avoiding the estimation of a profligate correlation matrix and substantially easing the estimation process.

Sales of deer licenses, one of the most important revenue sources for wildlife management at the Indiana Department of Natural Resources (IDNR), have been declining for a decade. To increase its revenue, the IDNR is considering introducing a new lifetime deer license for sale. This license would allow hunters to harvest deer (and possibly other species) each year for the rest of their lives in exchange for a relatively large up-front fee. The forward-looking nature of the decision to buy a lifetime license means hunters' choice behavior is necessarily dynamic. Prior work estimates preferences for long-lived, durable goods using standard discrete choice experiments underpinned by static models. We derive a dynamic discrete choice model of lifetime license purchases. Our model informs the design of a novel, dynamic discrete choice experiment, generating data that allows us to consistently estimate individuals’ forward-looking preferences for lifetime hunting licenses. We use our model to estimate the price of lifetime licenses that maximizes IDNR revenues.

Estimation of discrete outcome specifications involves significant hypothesis testing, including multiple modelling decisions which could affect results and interpretation. Model development is generally time-bound, and decisions largely rely on experience, knowledge of the problem context and statistics. There is often a risk of adopting restricted specifications, which could preclude important insights and valuable behavioral patterns. This study proposes a framework to assist in testing hypotheses and discovering mixed-Logit specifications that best capture discrete outcome behavior. The proposed framework includes a mathematical programming formulation and a bi-level constrained optimization algorithm to simultaneously test various modelling assumptions and produce meaningful specifications within a reasonable time. The bi-level framework illustrates the integration of a population-based metaheuristic with model estimation procedures. In addition, the optimization algorithm allows the analyst to impose assumptions on the models to test specific hypotheses or to ensure compliance with literature. Numerical experiments are conducted using different datasets and behavioral processes to illustrate the efficacy of the proposed extensive hypothesis testing in terms of interpretability and goodness-of-fit. Results illustrate the ability of the proposed algorithm to reveal important insights that can potentially be overlooked due to limited and/or biased hypothesis testing. In addition, the proposed extensive hypothesis testing generates multiple acceptable solutions, thereby suggesting potential directions for further investigation. The proposed framework can serve as a decision-assistance modelling tool in various applications, involving many variables and outcomes, such as road safety analysis, consumer choice behavior, and integrated land-use and travel choice models.

When estimating random coefficients models from choice data, decisions relating to the multivariate density function assumed to describe preference heterogeneity across the population raise questions about stochastic (in)dependence between preference dimensions, uni- vs. multi-modality, potential point masses, bounds and/or constraints on support regions, among other concerns. Parametric representations of population distributions have generally implied uncomfortable compromises to achieve estimation tractability. It would seem preferable to sidestep such issues by estimating individual preferences in a distribution-free manner, but this freedom of form implies a large number of parameters since we lose the parsimony enabled by parametric densities and must deal directly with estimation of individual decision maker preferences. I propose a hybrid distribution-free estimator for individual parameter logit models that uses a genetic algorithm as first stage, the solution from which becomes a starting point for a gradient-based search to obtain the final posterior maximum likelihood estimates of individual preferences. This estimator is described in detail, its parameter recovery capability is tested with Monte Carlo data generation simulations, and a case study is developed in some detail to illustrate its use in policy analysis. The estimator can be applied to both stated and revealed preference data, requiring only that sufficient choice replications be available for individual observation units consistent with extant estimation methods. Computational experience shows the estimator to require CPU times comparable to extant simulation-based estimation methods, meaning that its use is practical for the exploration of the parameter space through multiple trials.

Revealed and stated choice data are fundamental inputs to understanding individuals’ preferences. Owning to the distinctive characteristics and complementary nature of these two types of data, making joint inference based on their combined information content represents an attractive approach to preference studies. However, complications may arise from the different decision protocols under the two distinct choice contexts. In this study, a Bayesian hierarchical model is proposed to make joint inference from combined RP and SP data, with special attention paid to capturing the behavioural differences between the two choice contexts. In addition to the well-recognised issues of decision inertia and scale differences, the proposed model also takes into account other behavioural characteristics such as a decision-maker ignoring situation constraints, non-attending attributes, and misinterpreting attributes. An empirical analysis of a combined RP and SP dataset of travel mode choices is used to demonstrate the advantageous features of the model. Upon examining the empirical evidence, two main advantages emerge: the model provides direct measures of the effect of behavioural issues arising from ignoring situation constraints and non-attending attributes, as well as evidence for the misinterpretation of attributes.