Relevant bibliographies by topics / Random Regret Minimization

Academic literature on the topic 'Random Regret Minimization'

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Published: 10 March 2023

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Journal articles on the topic "Random Regret Minimization"

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Li, Dewei, Yufang Gao, Ruoyi Li, and Weiteng Zhou. "Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment." Journal of Advanced Transportation 2018 (December 18, 2018): 1–28. http://dx.doi.org/10.1155/2018/9789316.

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Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general.
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Chorus, Caspar G. "A Generalized Random Regret Minimization model." Transportation Research Part B: Methodological 68 (October 2014): 224–38. http://dx.doi.org/10.1016/j.trb.2014.06.009.

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van Cranenburgh, Sander, Cristian Angelo Guevara, and Caspar G. Chorus. "New insights on random regret minimization models." Transportation Research Part A: Policy and Practice 74 (April 2015): 91–109. http://dx.doi.org/10.1016/j.tra.2015.01.008.

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Guevara, C. Angelo, Caspar G. Chorus, and Moshe E. Ben-Akiva. "Sampling of Alternatives in Random Regret Minimization Models." Transportation Science 50, no. 1 (February 2016): 306–21. http://dx.doi.org/10.1287/trsc.2014.0573.

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Chorus, Caspar G., Theo A. Arentze, and Harry J. P. Timmermans. "A Random Regret-Minimization model of travel choice." Transportation Research Part B: Methodological 42, no. 1 (January 2008): 1–18. http://dx.doi.org/10.1016/j.trb.2007.05.004.

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Zhao, Lei, Hongzhi Guan, Xinjie Zhang, and Xiongbin Wu. "A regret-based route choice model with asymmetric preference in a stochastic network." Advances in Mechanical Engineering 10, no. 8 (August 2018): 168781401879323. http://dx.doi.org/10.1177/1687814018793238.

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In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.
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Li, Mengjie, Fujian Chen, and Qinze Lin. "Random Regret Minimization Model for Variable Destination-Oriented Path Planning." IEEE Access 8 (2020): 163646–59. http://dx.doi.org/10.1109/access.2020.3021524.

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CONTRERAS SERRANO, CARLOS GABRIEL. "Modelos econométricos de elección desde la economía del comportamiento: Modelamiento de elección discreta basada en costo emocional aleatorio - Aplicación a la industria agroquímica Colombiana." Comunicaciones en Estadística 13, no. 2 (November 1, 2020): 33–50. http://dx.doi.org/10.15332/2422474x.6279.

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Los modelos económicos ortodoxos, proponen que el ser humano es racional, egoísta y maximizador para hacer sus elecciones de consumo. Evidencia desde la economía del comportamiento reta estos supuestos planteando nuevos modelos para estudiar la elección humana. Estudiando el proceso de elección de productos de cuidado de cultivo en productores de tomate en Colombia, la presente investigación busco comparar estadística y conceptualmente los modelos RUM (Random Utility Maximization) y RRM (Random Regret Minimization) construidos vía modelamiento de elección discreta concluyendo que los modelos RRM logran mejor bondad de ajuste para describir el comportamiento de elección y compra de nematicidas en muestras de productores de tomate colombianos por lo que constituyen una alternativa viable para diseñar nuevos productos, estimar su participación potencial en el mercado y fijarles precio. Palabras clave: Modelamiento de elección discreta, RUM (Random Utility Maximization), RRM (Random Regret Minimization), Economía del comportamiento, Comportamiento de elección.
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Jang, Sunghoon, Soora Rasouli, and Harry Timmermans. "Tolerance and Indifference Bands in Regret–Rejoice Choice Models: Extension to Market Segmentation in the Context of Mode Choice Behavior." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (October 9, 2018): 23–34. http://dx.doi.org/10.1177/0361198118787629.

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Random regret minimization models (RRMs), based on seminal work in regret theory, have been introduced into transportation research as an alternative to expected/random utility models. With ample applications in diverse choice contexts, the RRMs have been extended to include the effect of “rejoice,” the counterpart of the emotion of regret. The fundamental assumption of regret–rejoice models is that when the chosen alternative is inferior to non-chosen alternatives with respect to an attribute, individuals feel regret; otherwise, if the chosen alternative is superior to non-chosen alternatives, individuals rejoice. The regret and rejoice functions are assumed to be continuous in attribute differences. However, individuals may tolerate small attribute differences when judging regret and be indifferent to small differences when assessing rejoice. This paper therefore introduces tolerance and indifference bands in random regret–rejoice choice models, and compares the performance of these models against the performance of the original models. Furthermore, it is assumed that tolerance and indifference bands differ by trip purpose. Empirical results testify to the better performance of the models with the tolerance and indifference bands, and show that trip purpose is an important factor affecting tolerance and indifference bands.
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Gutiérrez-Vargas, Álvaro A., Michel Meulders, and Martina Vandebroek. "randregret: A command for fitting random regret minimization models using Stata." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 626–58. http://dx.doi.org/10.1177/1536867x211045538.

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In this article, we describe the randregret command, which implements a variety of random regret minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181–196), the generalized RRM model introduced in Chorus (2014, Transportation Research, Part B 68: 224–238), and also the µRRM and pure RRM models, both introduced in van Cranenburgh, Guevara, and Chorus (2015, Transportation Research, Part A 74: 91–109). We illustrate the use of the randregret command by using stated choice data on route preferences. The command offers robust and cluster standarderror correction using analytical expressions of the score functions. It also offers likelihood-ratio tests that can be used to assess the relevance of a given model specification. Finally, users can obtain the predicted probabilities from each model by using the randregretpred command.
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Dissertations / Theses on the topic "Random Regret Minimization"

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Biondi, Beatrice <1990&gt. "Regret Theory as an Alternative Framework in Consumer Food Choice: an Application of the Random Regret Minimization Model." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8826/1/Biondi_Beatrice_tesi.pdf.

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In consumer behaviour literature, discrete choices of food are usually assumed to be driven by maximization of utility, and modelled through the Random Utility Maximization (RUM) choice model. Nevertheless, other behavioural paradigms have been proposed in marketing literature, which account for loss aversion and regret minimization. Hence, this study investigates the usefulness and potential in the food domain of a discrete choice model that follows the regret minimization principle, the Random Regret Minimization (RRM) model, as an alternative and complement to existing RUM models. The study also investigates whether and to what extent a number of personality traits influence the use of a utility-maximizing, or regret-minimizing decision rule. To the best of our knowledge, this is the first attempt to explore whether and how anticipated regret affect consumers’ choice of food products, while in general a direct and significant impact on future choices has been found. The thesis begins with a conceptual model for the food choice process, which takes into account how values related to food and personal factors contribute to drive food choices. Then, consumer choice strategies developed in the literature are introduced and discussed. Based on data gathered from a discrete choice experiment, the RRM discrete choice model is applied and compared with the RUM classical model. Results show that at the aggregate level the two models have similar goodness of fit to the data and prediction ability. Still, each of them shows better fit for particular subgroups of consumers, based on personality traits. Hence, the present study reveals a potential for the RRM model applications in the food domain. Nonetheless, the two models are subject to different interpretations and feature distinct behavioural properties. As such, the RRM can be seen as a valuable addition to existing methodologies in consumer choice modelling, specifically in the food domain.
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Piracci, Giovanna. "The role of environmental and social sustainability attributes in food choices." Doctoral thesis, 2022. https://hdl.handle.net/2158/1295876.

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The agri-food supply chain is currently far from being sustainable due to its negative contribution to environmental degradation, climate change, public health and social equity. Individuals as consumers play a key role in favouring the transition towards a sustainable food system. Switching towards more sustainable food consumption patterns can trigger changes on the supply side and contribute to policy efforts aimed at pursuing sustainable development. In this context, the aspects such as the drivers and barriers to sustainable consumption, consumer behaviour towards sustainable choices and how to effectively tackle unsustainable food habits have become paramount. Therefore, to fill the research gaps in the existing literature, this thesis aims to provide a better understanding of the effect of environmental and social sustainability attributes on food choices and investigate the decision-making process adopted by food consumers when choosing sustainable products.
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Book chapters on the topic "Random Regret Minimization"

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Chorus, Caspar G. "Empirical Application of Random Regret Minimization-Models." In Random Regret-based Discrete Choice Modeling, 17–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29151-7_3.

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Chorus, Caspar G. "A Random Regret Minimization-based Discrete Choice Model." In Random Regret-based Discrete Choice Modeling, 5–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29151-7_2.

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Chorus, Caspar G. "Applicability of Random Regret Minimization-Models, and Their Strong and Weak Points." In Random Regret-based Discrete Choice Modeling, 35–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29151-7_4.

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Conference papers on the topic "Random Regret Minimization"

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Ji, Xiangfeng, Jian Zhang, Yongkai Hu, and Bin Ran. "Location-Based Route Choice Model under Random Regret Minimization." In 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.305.

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Wang, Lixiao, Xiaoyu Wang, and Zhi Zuo. "A Generalized Random Regret Minimization-Based Commuters’ Mode Choice Model." In 18th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481523.237.

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Irawan, Muhammad Zudhy, Sigit Priyanto, and Dewanti. "Is Random Regret Minimization More Suitable in Predicting Mode Choice Decision for Indonesian Context than Random Utility Maximization?" In International Conference on Applied Science, Engineering and Social Science. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0009880601930199.

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Gao, Yufang, and Dewei Li. "Rail Transit Assignment Model Development Based on Random Regret Minimization under Multi-Class User Conditions." In 17th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784480915.179.

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