Journal of Choice Modelling最新文献

{"title":"Australian community preferences for hotel quarantine options within the Logit Mixed Logit Model framework","authors":"Andrea Pellegrini ,&nbsp;Antonio Borriello ,&nbsp;John M. Rose","doi":"10.1016/j.jocm.2024.100473","DOIUrl":"https://doi.org/10.1016/j.jocm.2024.100473","url":null,"abstract":"<div><p>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 (LML) model defined by Train (2016). Two approaches are suggested to decompose individual heterogeneity in this framework and are applied to data collected via a stated preference experiment. The empirical findings demonstrate that on average, the community prefers returned travellers be quarantined in dedicated quarantine facilities rather than be quarantined at home or using hotels, but are mostly indifferent to how long travellers are quarantined for, and how many travellers are allowed to return to Australia. The sample do however have a preference, on average for travellers having to pay less to quarantine, meaning they wish to see greater government subsidies. However, the modelling approach demonstrates that the common use of averages potentially masks diverse preferences, and is not representative of community wants and desires, thus possibly leading to incorrect inferences about policy impacts.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100473"},"PeriodicalIF":2.4,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175553452400006X/pdfft?md5=5d3108f3679923ab3cb80ea059643fee&pid=1-s2.0-S175553452400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139936399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Responsibility attribution and community support of coastal adaptation to climate change: Evidence from a choice experiment in the Maldives","authors":"Susann Adloff ,&nbsp;Katrin Rehdanz","doi":"10.1016/j.jocm.2024.100468","DOIUrl":"https://doi.org/10.1016/j.jocm.2024.100468","url":null,"abstract":"<div><p>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. We study individuals' perceptions of personal responsibility and preferences for coastal protection in a setting in which individuals have little formal responsibility. To do so, we collect data from the Maldives, a small island development state with significant risks of seaborne hazards where responsibility for coastal protection formally rests with the central government without significant involvement of local communities. Using survey measures and a Discrete Choice Experiment (DCE), we investigate respondents' sense of personal responsibility and their preferences for climate change adaptation distinguishing between preferences for hard, man-made structures and soft, working-with-nature protection approaches. The results show that responsibility perception plays an important role for stated willingness to support protective measures. However, they further show a mismatch between formally assigned and perceived responsibility for protection with a majority of respondents having a strong sense of personal responsibility for protection. In addition, the DCE results indicate a misalignment of people's preferences and the measures implemented by the government. While the latter belong to the group of hard protection measures, the majority of respondents show a clear preference for soft protection. We discuss the implications of these findings and highlight the importance of a better understanding of drivers of responsibility perceptions.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100468"},"PeriodicalIF":2.4,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000010/pdfft?md5=b06468e6e6ec30503b68d611598e7dc4&pid=1-s2.0-S1755534524000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian generalized rank ordered logit model","authors":"Haotian Cheng ,&nbsp;John N. Ng'ombe ,&nbsp;Dayton M. Lambert","doi":"10.1016/j.jocm.2024.100475","DOIUrl":"https://doi.org/10.1016/j.jocm.2024.100475","url":null,"abstract":"<div><p>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 as a function of demographic or environmental variables. A Monte Carlo experiment compares the estimates of accuracy and precision of the proposed GROL estimation with the MROL specification. Simulation results suggest that the GROL model performs comparatively well when the GROL or the MROL is the true data-generating process (dgp). Coefficient and willingness-to-pay estimates of the GROL are more precise and accurate compared to the MROL when the MROL is the true dgp. We surmise that the increased precision of the GROL estimator arises from the added flexibility for modeling different sources of heterogeneity. An empirical application analyzes a BWS survey on consumer preferences for single-use eating-ware (SUEW) products made from biobased materials. Findings suggest that consumers value most product degradability and using non-plastic materials to fabricate SUEW. Consumers also valued the rapidity of product degradability and using non-plastic materials to make SUEW plates. Respondent attentiveness also affected willingness-to-pay (WTP) estimates across attributes. Results suggest attentive respondents were about $3.00 more WTP for biodegradable SUEW than inattentive respondents.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100475"},"PeriodicalIF":2.4,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000071/pdfft?md5=fbbb03939426530266c915725dd10ee2&pid=1-s2.0-S1755534524000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139682463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Travel behaviour and game theory: A review of route choice modeling behaviour","authors":"Furkan Ahmad ,&nbsp;Luluwah Al-Fagih","doi":"10.1016/j.jocm.2024.100472","DOIUrl":"10.1016/j.jocm.2024.100472","url":null,"abstract":"<div><p>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 limitations of various game theory (GT) based models used in route choice modelling. The manuscript draws upon the theoretical foundations of game theory to explore the complex decision-making processes of travelers in transportation networks, focusing on factors such as travel time, congestion. The manuscript discusses the challenges and opportunities associated with implementing game theory-based models in practice, including the data requirements, model calibration, and computational complexity. These factors are considered in relation to the suitability of different game theory-based models, including cooperative, non-cooperative, and evolutionary games. The comparative critiques presented in this manuscript provide guidance for future research directions in the field of private route choice modelling, aimed at academic researchers, engineers, policymakers, and industrial communities.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100472"},"PeriodicalIF":2.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000058/pdfft?md5=85b5cf115c5ed0d3360f7d91f5a1a3dc&pid=1-s2.0-S1755534524000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139665444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting choices of street-view images: A comparison between discrete choice models and machine learning models","authors":"Wei Zhu ,&nbsp;Wei Si","doi":"10.1016/j.jocm.2024.100470","DOIUrl":"10.1016/j.jocm.2024.100470","url":null,"abstract":"<div><p>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: Multinomial Logit, Mixed Logit, Deep Neural Network, and Convolutional Neural Network. Additionally, the study tested the effects of different data formats on the models' performances, including semantic interpretation, semantic segmentation, raw image, semantic map, and enriched image. The comparison focused on the models' explainability and out-of-sample predictability with new but similar choice data. The results show that (1) the Discrete Choice Models exhibit nearly equal predictability to the Deep Neural Network models, but significantly outperform the Convolutional Neural Network models; (2) the Discrete Choice Models are more explainable than the Deep Neural Network models; and (3) models trained on semantic interpretation data demonstrate better predictability than those trained on semantic segmentation data and imagery data.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100470"},"PeriodicalIF":2.4,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000034/pdfft?md5=24433587821c1087e62e179a597a41b4&pid=1-s2.0-S1755534524000034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139645496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of reinforcement learning in shaping the decision policy in methamphetamine use disorders","authors":"Sadegh Ghaderi,&nbsp;Mohammad Hemami,&nbsp;Reza Khosrowabadi,&nbsp;Jamal Amani Rad","doi":"10.1016/j.jocm.2024.100469","DOIUrl":"https://doi.org/10.1016/j.jocm.2024.100469","url":null,"abstract":"<div><p>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 understood. In this study, to uncover the latent neurobiological and psychological meaningful processes of such impairment, we apply a reinforcement learning diffusion decision model (RL-DDM) while methamphetamine abuser participants (<span><math><mrow><mi>n</mi><mo>=</mo><mn>18</mn></mrow></math></span>, all men; mean (±SD) age: 27.3±5) and age/sex-matched healthy controls (<span><math><mrow><mi>n</mi><mo>=</mo><mn>25</mn></mrow></math></span>, all men; mean (±SD) age: 26.8.0±3.63) perform choices to resolve uncertainty within a simple probabilistic learning task with rewards and punishments. Preliminary behavior results indicated that addicts made maladaptive patterns of learning that mirrored in both choices and response times (RTs). Furthermore, modeling results revealed that such EDM impairment (maladaptive pattern in optimal selection) in addicts was more imputable to both increased learning rates (more sensitive to outcome fluctuations) and decreased drift rate (less reward sensitivity) compared to healthy. In addition, addicts also showed substantially longer non-decision times (attributed to slower RTs), as well as lower decision boundary criteria (reflection of impulsive choice). Taken together, our findings reveal precise mechanisms associated with EDM impairments in methamphetamine use disorder and confirm the debility of the options values assignment system as the main hub in learning-based decision making.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100469"},"PeriodicalIF":2.4,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000022/pdfft?md5=51b91dba15f58c371ab69e2479d02428&pid=1-s2.0-S1755534524000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139548800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing and reducing bias when modelling choice behaviour on imbalanced panel datasets","authors":"Mirosława Łukawska,&nbsp;Laurent Cazor,&nbsp;Mads Paulsen,&nbsp;Thomas Kjær Rasmussen,&nbsp;Otto Anker Nielsen","doi":"10.1016/j.jocm.2024.100471","DOIUrl":"https://doi.org/10.1016/j.jocm.2024.100471","url":null,"abstract":"<div><p>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 the sample structure. This study investigates the issue of handling imbalanced panel datasets for modelling individual behaviour. It first conducts a simulation experiment to study to which degree mixed logit models with and without panel reproduce the population preferences when using imbalanced data. It then investigates how the application of bias reduction strategies, such as subsampling and likelihood weighting, influences model results and finds that combining these techniques helps to find an optimal trade-off between bias and variance of the estimates. Considering the conclusions from the simulation study, a large-scale case study estimates bicycle route choice models with different correction strategies. These strategies are compared in terms of efficiency, weighted fit measures, and computational burden to provide recommendations that fit the modelling purpose. We find that the weighted panel mixed multinomial logit model, estimated on the entire dataset, performs best in terms of minimising the bias-efficiency trade-off in the estimates. Finally, we propose a strategy that ensures equal contribution of each individual to the estimation results, regardless of their representation in the sample, while reducing the computational burden related to estimating models on large datasets.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100471"},"PeriodicalIF":2.4,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000046/pdfft?md5=7ba46a5a4007cd14820c35c90ef2af12&pid=1-s2.0-S1755534524000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139487267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The rise of best-worst scaling for prioritization: A transdisciplinary literature review","authors":"Anne L.R. Schuster ,&nbsp;Norah L. Crossnohere ,&nbsp;Nicola B. Campoamor ,&nbsp;Ilene L. Hollin ,&nbsp;John F.P. Bridges","doi":"10.1016/j.jocm.2023.100466","DOIUrl":"https://doi.org/10.1016/j.jocm.2023.100466","url":null,"abstract":"<div><p>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 &lt; 0.01), average number of tasks (p &lt; 0.01), average number of objects per task (p = 0.03), and average number of tasks presented to participants (p &lt; 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.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100466"},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534523000672/pdfft?md5=4e08e1a975f664509a1e57b5968d273b&pid=1-s2.0-S1755534523000672-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139108888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resampling estimation of discrete choice models","authors":"Nicola Ortelli ,&nbsp;Matthieu de Lapparent ,&nbsp;Michel Bierlaire","doi":"10.1016/j.jocm.2023.100467","DOIUrl":"10.1016/j.jocm.2023.100467","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100467"},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534523000684/pdfft?md5=1bf006ed1264b0459140eeab28ae0e10&pid=1-s2.0-S1755534523000684-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139093404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the impact of decision rule assumptions in experimental designs on preference recovery: An application to climate change adaptation measures","authors":"Sander van Cranenburgh ,&nbsp;Jürgen Meyerhoff ,&nbsp;Katrin Rehdanz ,&nbsp;Andrea Wunsch","doi":"10.1016/j.jocm.2023.100465","DOIUrl":"10.1016/j.jocm.2023.100465","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"50 ","pages":"Article 100465"},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534523000660/pdfft?md5=c5b2fc344e8fb5e866f202fbccdfca02&pid=1-s2.0-S1755534523000660-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139093283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}