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To pool or not to pool: Accounting for task non-attendance in subgroup analysis J. Choice Model. (IF 4.164) Pub Date : 2024-03-07 Juan Marcos Gonzalez, F. Reed Johnson, Eric Finkelstein
Pooling data from different subgroups offers advantages of shrinking standard errors and simplifying characterization of the data structure. The ability to pool data also facilitates meta-analysis to evaluate consensus among multiple studies and to inform benefit transfer to new choice settings. Testing for poolability requires accounting for differences in response variance or scale among subgroups
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Departure time choices and a modeling framework for a guidance system J. Choice Model. (IF 4.164) Pub Date : 2024-03-02 Navid Khademi, Hamed Kharrazi, Anthony Chen, Krisada Chaiyasarn, Seghir Zerguini
Departure time choice is a key component of travel behavior that directly influences the spatial and temporal distribution of travel demand. This paper tries to develop a modeling framework for choosing the departure time that minimizes travel costs. In this regard, a modeling framework for generating departure time recommendations is proposed and applied to real commuting trips. The methodology is
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Exploring the behavioral stage transition of traveler's adoption of carsharing: An integrated choice and latent variable model J. Choice Model. (IF 4.164) Pub Date : 2024-02-26 Shunchao Wang, Zhanguo Song
This study investigates the process of stage transition in traveler's adoption of carsharing. The carsharing adoption behaviors are classified into five stages using the transtheoretical model: precontemplation (PC), contemplation (C), preparation (PA), action (A), and maintenance (M). In Beijing, a comprehensive survey combining stated preference and revealed preference methods is conducted to collect
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Discrete choice experiments with eye-tracking: How far we have come and ways forward J. Choice Model. (IF 4.164) Pub Date : 2024-02-26 Prateek Bansal, Eui-Jin Kim, Semra Ozdemir
With the increased affordability of eye-tracking technology, its applications in discrete choice experiments (DCEs) are rapidly increasing. It is critical to understand the current state of research, challenges, and potential value of this technology for future studies. This article provides an interdisciplinary perspective on three main themes of this literature – (i) utilizing visual attention measures
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Australian community preferences for hotel quarantine options within the Logit Mixed Logit Model framework J. Choice Model. (IF 4.164) Pub Date : 2024-02-22 Andrea Pellegrini, Antonio Borriello, John M. Rose
In response to the Covid-19 pandemic, many countries have adopted measures to contain the spread of the virus, including mandatory quarantine for inbound travellers. This research investigates the preferences of residents of New South Wales, Australia, towards the mandatory quarantine protocol adopted in the state. Heterogeneity in individual preferences is explored by advancing the Logit Mixed Logit
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Responsibility attribution and community support of coastal adaptation to climate change: Evidence from a choice experiment in the Maldives J. Choice Model. (IF 4.164) Pub Date : 2024-02-06 Susann Adloff, Katrin Rehdanz
Community support for climate change adaptation projects markedly benefits effective protection. A relevant driver of community support is the perceived attribution of responsibility to individuals. If individuals attribute responsibility for adaptation to others, e.g. public authorities, this reduces the adaptation efforts of the individual, might induce preference uncertainty, and can lead to maladaptation
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A Bayesian generalized rank ordered logit model J. Choice Model. (IF 4.164) Pub Date : 2024-02-05 Haotian Cheng, John N. Ng'ombe, Dayton M. Lambert
Using rank-ordered logit regression, researchers typically analyze consumer preference data collected with Best-Worst Scaling (BWS) surveys. We propose a generalized rank-ordered logit (GROL) model that allows flexibility in modeling preference heterogeneity. The GROL and mixed rank-ordered logit model (MROL) accommodate preference heterogeneity. However, the GROL also allows one to model heterogeneity
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Travel behaviour and game theory: A review of route choice modeling behaviour J. Choice Model. (IF 4.164) Pub Date : 2024-02-01 Furkan Ahmad, Luluwah Al-Fagih
Route choice models are a vital tool for evaluating the impact of transportation policies and infrastructure improvements, such as the addition of new roads, tolls, or congestion charges. They can also be used to predict traffic flow and congestion levels, which is essential for traffic management and control. The aim of this manuscript is to provide a comprehensive analysis of the effectiveness and
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Predicting choices of street-view images: A comparison between discrete choice models and machine learning models J. Choice Model. (IF 4.164) Pub Date : 2024-01-30 Wei Zhu, Wei Si
Recently, there has been a growing interest in comparing machine learning models and Discrete Choice Models. However, no studies have been conducted on image choice problems. This study aims to fill this gap by conducting a stated preference experiment that involves choosing streets for cycling based on real-world street-view images. The choice data obtained were used to estimate and compare four models:
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The role of reinforcement learning in shaping the decision policy in methamphetamine use disorders J. Choice Model. (IF 4.164) Pub Date : 2024-01-23 Sadegh Ghaderi, Mohammad Hemami, Reza Khosrowabadi, Jamal Amani Rad
The prevalence of methamphetamine use disorder (MUD) as a major public health problem has increased dramatically over the last two decades, reaching epidemic levels, which pose high costs to the health care systems worldwide and is commonly associated with experience-based decision-making (EDM) aberrant. However, precise mechanisms underlying such non-optimally in choice patterns still remain poorly
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Revealing and reducing bias when modelling choice behaviour on imbalanced panel datasets J. Choice Model. (IF 4.164) Pub Date : 2024-01-18 Mirosława Łukawska, Laurent Cazor, Mads Paulsen, Thomas Kjær Rasmussen, Otto Anker Nielsen
The emergence of modern tools and technologies gives a unique opportunity to collect large amounts of data for understanding behaviour. However, the generated datasets are often imbalanced, as individuals might contribute to the datasets at different frequencies and periods. Models based on these datasets are challenging to estimate, and the results are not straightforward to interpret without considering
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The rise of best-worst scaling for prioritization: A transdisciplinary literature review J. Choice Model. (IF 4.164) Pub Date : 2024-01-05 Anne L.R. Schuster, Norah L. Crossnohere, Nicola B. Campoamor, Ilene L. Hollin, John F.P. Bridges
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
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Resampling estimation of discrete choice models J. Choice Model. (IF 4.164) Pub Date : 2024-01-03 Nicola Ortelli, Matthieu de Lapparent, Michel Bierlaire
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
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On the impact of decision rule assumptions in experimental designs on preference recovery: An application to climate change adaptation measures J. Choice Model. (IF 4.164) Pub Date : 2024-01-03 Sander van Cranenburgh, Jürgen Meyerhoff, Katrin Rehdanz, Andrea Wunsch
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
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Guilt, gender, and work-life balance: A choice experiment J. Choice Model. (IF 4.164) Pub Date : 2023-12-15 Chie Aoyagi, Alistair Munro
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
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Ordinal-ResLogit: Interpretable deep residual neural networks for ordered choices J. Choice Model. (IF 4.164) Pub Date : 2023-12-03 Kimia Kamal, Bilal Farooq
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
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The route choices of pedestrians under crowded and non-emergency conditions: Two-route experiments and modeling J. Choice Model. (IF 4.164) Pub Date : 2023-11-30 Cheng-Jie Jin, Chenyang Wu, Yuchen Song, Tongfei Liu, Dawei Li, Rui Jiang, Shuyi Fang
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
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The effect of perceived risk of false diagnosis on preferences for COVID-19 testing: Evidence from the United States J. Choice Model. (IF 4.164) Pub Date : 2023-11-23 Tomás Rossetti, Ricardo A. Daziano
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
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Open system model of choice and response time J. Choice Model. (IF 4.164) Pub Date : 2023-10-26 Gunnar P. Epping, Peter D. Kvam, Timothy J. Pleskac, Jerome R. Busemeyer
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
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Attitudes and Latent Class Choice Models using Machine Learning J. Choice Model. (IF 4.164) Pub Date : 2023-10-10 Lorena Torres Lahoz, Francisco Camara Pereira, Georges Sfeir, Ioanna Arkoudi, Mayara Moraes Monteiro, Carlos Lima Azevedo
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
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On-demand transit user preference analysis using hybrid choice models J. Choice Model. (IF 4.164) Pub Date : 2023-09-29 Nael Alsaleh, Bilal Farooq, Yixue Zhang, Steven Farber
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
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Best, worst, and best&worst choice probabilities for logit and reverse logit models J. Choice Model. (IF 4.164) Pub Date : 2023-09-25 André de Palma, Karim Kilani
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
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Outside good utility and substitution patterns in direct utility models J. Choice Model. (IF 4.164) Pub Date : 2023-09-19 Chul Kim, Adam N. Smith, Jaehwan Kim, Greg M. Allenby
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
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Cube model: Predictions and account for best–worst choice situations with three choice alternatives J. Choice Model. (IF 4.164) Pub Date : 2023-09-20 Adele Diederich, Keivan Mallahi-Karai
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
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Evaluating the gap choice decisions of pedestrians in conflict situations in mass religious gatherings and controlled experimental setup – A pilot study J. Choice Model. (IF 4.164) Pub Date : 2023-09-18 Karthika P S, Ashish Verma
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
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Sample size selection for discrete choice experiments using design features J. Choice Model. (IF 4.164) Pub Date : 2023-09-08 Samson Yaekob Assele, Michel Meulders, Martina Vandebroek
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
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Do choice tasks and rating scales elicit the same judgments? J. Choice Model. (IF 4.164) Pub Date : 2023-08-31 Quentin F. Gronau, Murray S. Bennett, Scott D. Brown, Guy E. Hawkins, Ami Eidels
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,
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Climate change adaptation preferences of winemakers from the Rioja wine appellation J. Choice Model. (IF 4.164) Pub Date : 2023-07-28 Ainhoa Vega-Bayo, Petr Mariel, Jürgen Meyerhoff, Armando Maria Corsi, Milan Chovan
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
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A cross-sectional exploration of labor supply, gender, and household wealth in urban China J. Choice Model. (IF 4.164) Pub Date : 2023-07-24 Xuehui Han, Tao Zhang, John K. Dagsvik, Yuan Cheng
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
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A randomized group approach to identifying label effects J. Choice Model. (IF 4.164) Pub Date : 2023-07-24 Brandon R. McFadden, Jayson L. Lusk, Adam Pollack, Joy N. Rumble, Kathryn A. Stofer, Kevin M. Folta
Motivated by the National Bioengineered Food Disclosure Standard (NBFDS), which requires companies to label bioengineered food products, this paper examines the choice effects of using a symbol approved by the standard relative to using text to disclose that a food product has bioengineered contents. Choice effects were determined using a randomized group design that assigned respondents to one-of-two
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Theory-driven or data-driven? Modelling ride-sourcing mode choices using integrated choice and latent variable model and multi-task learning deep neural networks J. Choice Model. (IF 4.164) Pub Date : 2023-07-20
Ride-sourcing services have had a disruptive impact on urban mobility. However, the perceived risk of contracting the COVID-19 virus while using these services has negatively affected people's willingness to travel by this mode. Therefore, it is essential to understand the factors influencing ride-sourcing usage during and after the pandemic. This study utilized data collected through stated preference
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Preference estimation from point allocation experiments J. Choice Model. (IF 4.164) Pub Date : 2023-07-17
Point allocation experiments are widely used in the social sciences. In these experiments, survey respondents distribute a fixed total number of points across a fixed number of alternatives. This paper reviews the different perspectives in the literature about what respondents do when they distribute points across options. We find three main alternative interpretations in the literature, each having
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One or two-step? Evaluating GMM efficiency for spatial binary probit models J. Choice Model. (IF 4.164) Pub Date : 2023-07-15
In this article we propose two-step generalized method of moment (GMM) procedure for a Spatial Binary Probit Model. In particular, we propose a series of two-step estimators based on different choices of the weighting matrix for the moments conditions in the first step, and different estimators for the variance–covariance matrix of the estimated coefficients. In the context of a Monte Carlo experiment
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Integrating a choice experiment into an agent-based model to simulate climate-change induced migration: The case of the Mekong River Delta, Vietnam J. Choice Model. (IF 4.164) Pub Date : 2023-07-15
Forecasting the future impact of climate change on migration is difficult, for many reasons, including the interactive and dynamic nature of many decisions and the heterogeneity of behavior. One popular solution, agent-based models (ABM) cope well with dynamics and heterogeneity, but often lack rigorous foundations in terms of individual behavior. Moreover, given limited exposure to actual climate
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Consumer preferences for country of origin labeling: Bridging the gap between research estimates and real-world behavior J. Choice Model. (IF 4.164) Pub Date : 2023-07-13
Studies investigating preferences for country-of-origin labeling (COOL) often overemphasize this attribute, which risks inflating estimated market value. We address this issue by studying consumer preferences for Florida versus Mexico tomatoes in a shopping environment that allows freedom to notice or ignore COOL when making decisions. A significant portion of subjects failed to notice COOL in the
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Reducing sample size requirements by extending discrete choice experiments to indifference elicitation J. Choice Model. (IF 4.164) Pub Date : 2023-06-19 Ambuj Sriwastava, Peter Reichert
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
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NP4VTT: A new software for estimating the value of travel time with nonparametric models J. Choice Model. (IF 4.164) Pub Date : 2023-06-10 José Ignacio Hernández, Sander van Cranenburgh
Two-attribute-two-alternative stated choice experiments are widely used to infer the Value-of-Travel-Time (VTT) distribution. Two-attribute-two-alternative stated choice experiments have the advantage that their data can be analysed using nonparametric models, which allow for the inference of the VTT distribution without having to impose assumptions on its shape. However, a software package that enables
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How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices J. Choice Model. (IF 4.164) Pub Date : 2023-05-19 Konstantina Sokratous, Anderson K. Fitch, Peter D. Kvam
Subjective value has long been measured using binary choice experiments, yet responses like willingness-to-pay prices can be an effective and efficient way to assess individual differences risk preferences and value. Tony Marley’s work illustrated that dynamic, stochastic models permit meaningful inferences about cognition from process-level data on paradigms beyond binary choice, yet many of these
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A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices J. Choice Model. (IF 4.164) Pub Date : 2023-05-13 Zili Li, Simon P. Washington, Zuduo Zheng, Carlo G. Prato
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
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Formative versus reflective attitude measures: Extending the hybrid choice model J. Choice Model. (IF 4.164) Pub Date : 2023-05-14 J.M. Rose, A. Borriello, A. Pellegrini
The inclusion of attitudinal indicator variables within discrete choice models is now largely common practice. Typically, this involves the estimation of multiple indicator multiple cause (MIMIC) type models which are used to construct latent attitudinal variables that are then employed as independent variables within standard discrete choice models. Such models, collectively termed hybrid choice models
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Preferences for online grocery shopping during the COVID-19 pandemic — the role of fear-related attitudes J. Choice Model. (IF 4.164) Pub Date : 2023-05-10 Wiktor Budziński, Ricardo Daziano
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
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A discrete choice modeling framework of heterogenous decision rules accounting for non-trading behavior J. Choice Model. (IF 4.164) Pub Date : 2023-05-10 Evanthia Kazagli, Matthieu de Lapparent
We present a discrete choice modeling framework with heterogeneous decision rules accounting for non-trading behavior. The proposed approach builds upon the state-of-the-art probabilistic finite mixture models and tackles non-trading behavior while accounting for inertia effects and serial correlation in the SP data, and contextual effects on the probability of an individual employing a specific decision
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Estimating a model of forward-looking behavior with discrete choice experiments: The case of lifetime hunting license demand J. Choice Model. (IF 4.164) Pub Date : 2023-05-03 Yusun Kim, Carson Reeling, Nicole J.O. Widmar, John G. Lee
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
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Modelling activity patterns of wild animals - An application of the multiple discrete-continuous extreme value (MDCEV) model J. Choice Model. (IF 4.164) Pub Date : 2023-05-01 Chiara Calastri, Marek Giergiczny, Andreas Zedrosser, Stephane Hess
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
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Separation-based parameterization strategies for estimation of restricted covariance matrices in multivariate model systems J. Choice Model. (IF 4.164) Pub Date : 2023-03-17 Shobhit Saxena, Chandra R. Bhat, Abdul Rawoof Pinjari
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
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Building a life-course intertemporal discrete choice model to analyze migration biographies J. Choice Model. (IF 4.164) Pub Date : 2023-03-06 Weiyan Zong, Junyi Zhang, Xiaoguang Yang
Individual migration mobilities over the life course have not been well understood in existing studies, and therefore ways to represent the underlying intertemporal dynamics and heterogeneities have remained unclear. To fill this research gap, this study investigates the domestic migration of people residing in the Capital Area of Japan, which has suffered from various issues caused by the over-concentration
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Extensive hypothesis testing for estimation of mixed-Logit models J. Choice Model. (IF 4.164) Pub Date : 2023-02-27 Prithvi Bhat Beeramoole, Cristian Arteaga, Alban Pinz, Md Mazharul Haque, Alexander Paz
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
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Intra-household bargaining for a joint vacation J. Choice Model. (IF 4.164) Pub Date : 2023-02-22 David Boto-García, Petr Mariel, José Francisco Baños-Pino
Taking a holiday trip is a common couple-based leisure activity in which both partners tend to be actively involved. This paper studies the intra-household bargaining for the choice of a vacation destination within couples. We conduct a discrete choice experiment in which we elicit both individual and couple preferences for different hypothetical travel portfolios in a two-stage experimental design
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Cost vector effects in discrete choice experiments with positive status quo cost J. Choice Model. (IF 4.164) Pub Date : 2023-02-02 Heini Ahtiainen, Eija Pouta, Wojciech Zawadzki, Annika Tienhaara
An important component of the design phase of a discrete choice experiment (DCE) is formulating the cost vector, which specifies the costs of the alternatives and enables the calculation of marginal willingness to pay (WTP) estimates. If the cost vector affects choice behaviour, welfare estimates may depend on the choice of the cost vector, which leads to problems with the validity and reliability
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A Bayesian instrumental variable model for multinomial choice with correlated alternatives J. Choice Model. (IF 4.164) Pub Date : 2023-01-19 Hajime Watanabe, Takuya Maruyama
Endogeneity and correlated alternatives are major concerns to be addressed in travel behavior analysis. However, these issues have rarely been dealt with simultaneously in advanced discrete choice models. This study proposes a multinomial probit model that incorporates the instrumental variable method, namely, a fully parametric instrumental variable model for a multinomial choice. The proposed model
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Heterogeneity in choice experiment data: A Bayesian investigation J. Choice Model. (IF 4.164) Pub Date : 2023-01-04 Lendie Follett, Brian Vander Naald
Discrete mixture (DM) models recognize the presence of heterogeneity across individuals in a given population. In the context of a public land use discrete choice experiment, we use DM models to allow for respondent behavior to probabilistically mix over multiple competing process heuristics. We pairwise combine the Random Utility Model (RUM), Contextual Concavity Model (CCM), and Random Regret Minimization
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A control-function correction for endogeneity in random coefficients models: The case of choice-based recommender systems J. Choice Model. (IF 4.164) Pub Date : 2022-12-31 Mazen Danaf, C. Angelo Guevara, Moshe Ben-Akiva
Applications of discrete choice models in personalization are becoming increasingly popular among researchers and practitioners. However, in such systems, when users are presented with successive menus (or choice situations), the alternatives and attributes in each menu depend on the choices made by the user in the previous menus. This gives rise to endogeneity which can result in inconsistent estimates
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Multiple discrete choice and quantity with order statistic marginal utilities J. Choice Model. (IF 4.164) Pub Date : 2022-11-26 Scott Webster
This paper presents a random utility maximization model for individuals selecting discrete quantities from a set of n alternatives. Multiple alternatives with positive quantities may be selected. Diminishing marginal utility to quantity of each alternative is modeled via order statistics of independent Gumbel random variables. The model is parsimonious and tractable, admitting closed-form expressions
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Distribution-free estimation of individual parameter logit (IPL) models using combined evolutionary and optimization algorithms J. Choice Model. (IF 4.164) Pub Date : 2022-11-24 Joffre Swait
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
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R packages and tutorial for case 1 best–worst scaling J. Choice Model. (IF 4.164) Pub Date : 2022-11-23 Hideo Aizaki, James Fogarty
Case 1 best–worst scaling (BWS1) has been used in a wide variety of research fields. BWS1 is attractive, relative to discrete choice experiments, because individual's preferences for items can be easily measured. Despite the relative ease of implementation, BWS1 analysis still requires the use of software packages. When used in conjunction with other packages, the new and revised functions in the package
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Modeling preference heterogeneity using model-based decision trees J. Choice Model. (IF 4.164) Pub Date : 2022-11-21 Álvaro A. Gutiérrez-Vargas, Michel Meulders, Martina Vandebroek
This article investigates the usage of a general model-based recursive partitioning algorithm to model preference heterogeneity. We use the algorithm to grow a decision tree based on statistical tests of the stability of individuals’ preference parameters. In particular, we used a Mixed Logit (MIXL) model with alternative-specific attributes at the end leaves of the tree while using individual characteristics
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Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments J. Choice Model. (IF 4.164) Pub Date : 2022-11-18 Jose Ignacio Hernandez, Sander van Cranenburgh, Caspar Chorus, Niek Mouter
We propose three procedures based on association rules (AR) learning and random forests (RF) to support the specification of a portfolio choice model applied in data from complex choice experiment data, specifically a Participatory Value Evaluation (PVE) choice experiment. In a PVE choice experiment, respondents choose a combination of alternatives, subject to a resource constraint. We combine a m