Choice modelling techniques have established themselves as a key analysis tool in health economics and have been used to understand patient and practitioner preferences across a wide variety of settings. A key interest in recent years has been the incorporation of ever more flexible levels of heterogeneity in preferences across individual decision makers, and in particular a growing interest in the

Modelling people's behaviour is a complex task, not only because of their intrinsic complexity but also because of their interaction with the environment and other individuals. The traditional format for discrete choice experiments involves the use of text and images. However, there is a growing tendency for using tools that offer a more realistic representation of complex and dynamic attributes in

Discrete choice analysis is a popular method of estimating heterogeneous customer preferences. Although model accuracy can be increased by including more choice data, this option is untenable when the obtaining of choice data from target customers is costly and time-consuming.. We thus propose a method for choice data generation for commercial products whose expected money value is a key factor in

This study focuses on the menus typically found in the marketplace (e.g., restaurants and Internet vendors), where the consumer may choose one or more from dozens of options or menu items, each at a posted price or fee. We show that modeling choices out of the typical menu leads to the “curse of dimensionality,” which transpires in two ways. First, the choice set (all possible menu selections) grows

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the estimation of statistical Discrete Choice Models based on the random utility framework. In this article, we provide new ways of dealing with large datasets

In this study, we apply the multiple discrete–continuous extreme value (MDCEV) model with triple constraints to identify the value of leisure time during weekends and long holidays. Our approach models the economic behavior of leisure trips with the triple constraints of budget, duration of weekend, and duration of holiday. The econometric model is developed to construct an estimation using the observed

Theatrical productions are supposed to be perishable good, since the tickets for a particular play cannot be inventoried and sold after a time of play. In the revenue management of a perishable good price discrimination is widely used. Since the theatre audience is heterogeneous in terms of visit purpose, ability to perceive quality, willingness-to-pay, the strategy of price discrimination is developed

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices. While the nested logit (NL) model is the classical way to address the question, this study presents multitask learning deep neural networks (MTLDNNs) as an alternative framework, and discusses its theoretical foundation, empirical performance, and behavioral intuition. We

Estimating installed-base effects for product adoption in the presence of unobserved heterogeneity is challenging since the typical solution of including fixed effects leads to inconsistent estimates in models with installed base. Narayanan and Nair (2013) highlight this problem and propose a bias correction method as a solution to the problem. This research note proposes an alternative solution: Borrowing

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research questions. However, the research and the methodologies used are fragmented. Whilst systematic reviews on RUMs for mode choice prediction have long existed

This paper tests for the existence of loss aversion, reference dependence and diminishing sensitivity in a discrete choice experiment (DCE). A status quo alternative is introduced in a DCE of nurses' job choices and modeled as an individual-specific third alternative representing the respondent's current job. This provides a feasible method for including a status quo, which changes the reference point

In a referendum in March 2016, the British public voted by a margin of 52 per cent to 48 per cent to leave the European Union. Unfortunately, the referendum question provides little information on the sort of relationship Britons desire with the EU. The purpose of this study is to use stated choice experiments (CEs) to understand and quantify what aspects of the relationship between Britain and the

Wildlife is widely becoming an important vehicle for rural development in most third-world countries across the globe. With wildlife, as with other conservation and development policies, policymakers are usually not informed about the needs and wants of poor rural households and roll out programmes that are not tailor made to suit their desires, which often results in policy failure. We use a survey-based

Quantum probability, first developed in theoretical physics, has recently been successfully used in cognitive psychology to model data from experiments that previously resisted effective modelling by classical methods. This has led to the development of choice models based on quantum probability, which have greater flexibility than standard models due to the implementation of complex numbers through

Researchers using discrete choice experiments (DCE) are often faced with the difficult decision of selecting which are the key attributes that must be included into their analysis. Previous literature on methods for attribute selection is not particularly well documented, frequently leaving researchers with a wide choice of attributes that could lead to complex choice tasks. Moreover, selecting attributes

This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV) services. Specifically, we compare the multivariate normal, the finite mixture of normals and the Dirichlet process mixture of normals (DP-MON)

Individual-specific posterior distributions are an attractive tool for disentangling the tastes for each person in the sample. However, there exists some risks and certain limitations regarding their use. This study reviews and summarizes the theoretical literature about the individual-specific posterior distributions derived from the Mixed Logit model, focusing on their properties, limitations and

The proneness of stated preference methods to different biases has increased the popularity of incentivized choice experiments (ICE). ICEs, however, are not free from challenges. One such challenge when using ICEs in market valuation is that some product alternatives may not be available to incentivize experiments. In this context, withholding information about product availability could be considered

The aim of the study is to examine the potential effect of attitudes towards physical activity on bus utility in the context of a rural area where studies have shown that the level and opportunity for physical activity are generally low. The need to analyze attitudes remains a strong motivation for the application of integrated choice and latent variable models. As such, we integrated attitudes towards

This paper proposes the use of Most Typical (MT) and Most Ideal (MI) levels when an adaptive choice-based conjoint (ACBC) survey can only obtain a small sample size n from a small population size N. This situation arises when expert decision makers are surveyed from among important small populations such as executives of large companies or political leaders, for which the expert decision maker assumption

This paper presents an agent-based model of team reasoning in a social dilemma game. Starting from the conundrum of empirically high levels of cooperation in dilemma games, contradicting traditional utility maximisation assumptions of game theory, Bacharach (1999, 2006) developed a theory of team reasoning. The idea behind team reasoning is that agents do not try to maximise their own utility but make

Combining choice modelling with social network analysis, we show how the stochastic actor-oriented model for the co-evolution of networks and behavior (SAOM) can be used as a powerful statistical framework to empirically analyze network-related choices. We discuss the underlying assumptions of SAOMs and show that they can be interpreted to represent a random utility maximization model (RUM). Network-related

In linear regressions, the ecological fallacy—the erroneous belief that aggregate-level coefficients coincide with individual-level coefficients—arises when individual outcomes depend on the group environment. This paper suggest that such “group effects” determine also the circumstances under which the ecological fallacy vanishes from basic discrete-choice models. In particular, when controlling for

Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same

Health economists often use discrete choice experiments (DCEs) to predict behavior, as actual market data is often unavailable. Manski (1990) argues that due to the incompleteness of the hypothetical scenarios used in DCEs, substantial uncertainty surrounds stated choice. Uncertainty can be decomposed into “resolvable” and “unresolvable”; the former is expected to become resolved in actual choice,

Images, videos, and virtual reality have been widely used in the literature to portray complex attributes to survey respondents. It is reasonable to expect immersive videos will be increasingly used in the future due to their decreasing costs and potentially more accurate representation of reality. Nevertheless, the literature has not sufficiently tested their ecological validity, which can be defined

Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively)

Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis, being appreciated in particular for their strong predictive power. However, many choice modellers are critical – and rightfully so – about using ANNs, for the reason that they are hard to diagnose. That is, for analysts it is hard to see whether a trained (estimated) ANN has learned intuitively reasonable relationships

One of the central topics in crime research, and one in which discrete choice modelling has been relatively recently introduced, is the study of where offenders choose to commit crime. Since the introduction of this approach in 2003, it has become relatively popular and used in over 25 published studies covering a range of crime types and study areas. However, in most of these analyses the conditional

The huge increase in higher education coverage in many developing countries has gone hand-in-hand with an additional supply of private colleges and with the enrolment of low to middle-class students, previously excluded from a historically elitist education segment. The larger diversity of both “suppliers and consumers”, unseen a few years ago, calls for methodological approaches that recognize heterogenous

In this paper, we estimate the relative value of different employment characteristics when choosing between apprenticeship and job offers. Further, we test assumptions derived from sociological rational choice theory on preference heterogeneity by individual and context characteristics. For this purpose, we analyse data from two discrete choice experiments, one focusing on the choice of an apprenticeship

Case 2 (profile case) best–worst scaling (BWS) is a question-based survey method for measuring preferences for attribute levels. Several existing R packages help to implement the construction of Case 2 BWS questions (profiles) and the discrete choice analysis of the responses to the questions. Structuring the dataset for Case 2 BWS analysis is, however, complicated: there are several model variants

The community of choice modellers has expanded substantially over recent years, covering many disciplines and encompassing users with very different levels of econometric and computational skills. This paper presents an introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users. Apollo also incorporates

This paper aims at better understanding passenger valuation of subway crowding in New York City. To this end, we conducted a stated preference survey with a discrete choice experiment where New Yorkers chose an alternative from a set of two hypothetical unlabeled subway routes based on occupancy levels and other attributes. We used the collected data to estimate crowding multipliers that quantify the

The Multinomial Logit (MNL) model is popular, but a semi-parametric specification of its link/utility function has seldom been used in empirical applications. This is primarily because of the resource intensive nature of semi-parametric estimation. In this paper we propose and implement a parallel computation algorithm to estimate the semi-parametric kernel MNL model. This algorithm reduces model estimation

Maximum simulated likelihood is the preferred estimator of most researchers who deal with discrete choice. It allows estimation of models such as mixed multinomial logit (MXL), generalized multinomial logit, or hybrid choice models, which have now become the state-of-practice in the microeconometric analysis of discrete choice data. All these models require simulation-based solving of multidimensional

At the time of creating an experimental design for a stated choice experiment, the analyst often does not precisely know which model, or decision rule, he or she will estimate once the data are collected. This paper presents two new software tools for creating stated choice experimental designs that are simultaneously efficient for regret minimisation and utility maximisation decision rules. The first

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based

The authors conduct an extensive simulation study to substantially contribute to the question how HB prior parameter settings (i.e. the prior variance and the prior degrees of freedom) affect the performance of HB-CBC models. The statistical performance of HB is evaluated under experimentally varying conditions based on six experimental factors using criteria for goodness-of-fit, parameter recovery

Best-worst scaling (BWS) has become so useful that practitioners feel pressure to include ever more items in their experiments. Researchers wanting more items and enough observations of each item by each respondent to support individual respondent-level utility models may greatly increase the burden on respondents, resulting in respondent fatigue and potentially in lower quality responses. Wirth and

Mixed logit is a widely used discrete outcome model that requires for the analyst to make three important decisions that affect the quality of the model specification. These decisions are: 1) what variables are considered in the analysis, 2) which variables are to be modeled with random parameters, and 3) what density function do these parameters follow. The literature provides guidance; however, a

Using maximum likelihood (ML) estimation for discrete choice modeling of small datasets causes two problems. The first problem is that the data may exhibit separation, in which case the ML estimates do not exist. Also, provided they exist, the ML estimates are biased. In this paper, we show how to adapt Firth's penalized likelihood estimation for use in discrete choice modeling. A powerful advantage

In the case of a medical emergency, a failure to provide timely care could have serious health consequences and even cause death. When the waiting time increases, the patient perceives the loss of well-being associated with the lack of primary care. Considering the willingness to pay (WTP) for avoiding human suffering in the context of emergency medical services (EMS) within the impact assessment of

There has been an increasing interest in studying patient preference heterogeneity to support regulatory decision-making. While the traditional mixed logit (MXL) and the latent class logit (LCL) models have been commonly used to analyze preference heterogeneity in discrete choice data, they have limitations. This study empirically compares a random effects latent class logit (RELCL) model to the traditional

It is well recognized that consistent estimation of peer effects faces formidable identification challenges. The confounding factors the literature usually focuses on are endogenous group formation, correlated unobservables and simultaneity. In this paper, I show that another significant source of bias arises when a researcher examines peer effects in product adoption. When people differ in their valuations

In this paper the properties of the maximum approximate composite marginal likelihood (MACML) approach to the estimation of multinomial probit models (MNP) proposed by Chandra Bhat and coworkers is investigated with respect to asymptotic properties. It is shown that, if the choice proportions are normalized to sum to one, a variant of the method provides consistent estimates of the choice proportions

Stated preference surveys are usually carried out in one session, without any follow-up interview after respondents have had the opportunity to experience the public goods or policies they were asked to value. Consequently, a stated preference survey needs to be designed so as to provide respondents with all the relevant information, and to help them process this information so they can perform the

Emerging transportation services are quickly changing the way individuals travel by expanding the set of transportation alternatives available for a trip, allowing for more flexibility in travel schedules and providing access to transportation without incurring the costs of auto ownership. Among the most controversial and rapidly growing shared-mobility services are ridehailing services, such as those

Consumer undervaluation of energy cost savings is a common explanation for the energy efficiency gap, where markets fail to adopt fuel-saving technologies even though the value of the savings exceeds the costs. This paper presents empirical evidence on the relationship between a possible cause of empirical studies finding undervaluation – consumer inattention – and the demand for fuel cost savings

This paper explores how changes in survey design influence the conclusions reached from discrete choice models, a topic which is of particular interest in the context of stated and revealed preference comparisons investigating potential hypothetical bias. We systematically test the WTP of a good with no related market value, using two standard, hypothetical stated preference data collections and an

This research note presents the first study to implement a real choice experiment in a web survey. In a case study on ethical food consumption, we find statistically significant lower willingness-to-pay values for the attributes “organic production” and “fair trade” in a choice experiment involving real payments compared to a choice experiment without real payments. This holds only true for respondents

This paper extends discrete residential choice models by incorporating choice set formation. Most discrete residential choice models make relatively arbitrary assumptions about the choice set – the set of houses to be considered by the purchaser. In this paper we explore several formulations of endogenous choice sets in which the decision maker's selection of a choice set is based on certain attributes

Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis. But, at present, it is unknown what sample size requirements are appropriate when using ANNs in this particular context. This paper fills this knowledge gap: we empirically establish a rule-of-thumb for ANN-based discrete choice analysis based on analyses of synthetic and real data. To investigate the effect of complexity

Unbalanced panel data or panel data with missing observations are common in empirical research. In this paper, we consider an unbalanced panel data Tobit model with interactive effects, and provide an estimator based on the iteration of Tobit factor analysis and maximum likelihood estimation. Monte Carlo studies are carried out to investigate the finite sample performance of the proposed method in

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs

Since the introduction of prospect theory, reference-dependence and loss-aversion have become widely acknowledged as important elements affecting decision-making. Nevertheless, establishing and determining reference frames are not extensively analyzed in the literature; rather, in most applications, it is simply assumed that the reference frames can be represented through the status quo. This assumption

Consumers increasingly face decisions among discrete and continuous choice alternatives. Deciding what to wear, watch, read and drive often includes alternatives that allow access for a period of time, as opposed to outright ownership of a good. Consumers may also want both, where access provides a wider variety of offerings than possible with ownership, and ownership provides greater assurance of

Information acquired by individuals over time plays a crucial role in their decision-making process. Gained experience updates beliefs on expected payoffs and is used in a range of decisions each individual is facing. In this paper, we focus on the formation of beliefs about the payoffs associated with spatial choices set in an uncertain environment. We task ourselves with better understanding of day-to-day

This paper exploits differences in fuel efficiency between hybrid vehicles and their gasoline counterparts to investigate two key questions relating to fuel economy standards: whether consumers properly value fuel economy (the energy paradox) and whether improved fuel efficiency increases travel (the rebound effect). Several vehicles, such as the Honda Civic, are available in both hybrid and gasoline