A novel choice model combining utility maximization and the disjunctive decision rules, application to two case studies

IF 2.8 3区 经济学 Q1 ECONOMICS
Laurent Cazor , David Paul Watling , Lawrence Christopher Duncan , Otto Anker Nielsen , Thomas Kjær Rasmussen
{"title":"A novel choice model combining utility maximization and the disjunctive decision rules, application to two case studies","authors":"Laurent Cazor ,&nbsp;David Paul Watling ,&nbsp;Lawrence Christopher Duncan ,&nbsp;Otto Anker Nielsen ,&nbsp;Thomas Kjær Rasmussen","doi":"10.1016/j.jocm.2024.100510","DOIUrl":null,"url":null,"abstract":"<div><p>Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual’s choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"52 ","pages":"Article 100510"},"PeriodicalIF":2.8000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000423/pdfft?md5=400c13f9f97d02380fcb53d69fcb1b23&pid=1-s2.0-S1755534524000423-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Choice Modelling","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755534524000423","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual’s choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.

结合效用最大化和非连续性决策规则的新型选择模型,在两个案例研究中的应用
大多数选择模型,如多叉 Logit(MNL),都依赖于随机效用理论,该理论假定补偿性效用最大化决策规则可以解释个人的选择行为。然而,研究表明,非补偿性决策规则有时能更好地解释行为。虽然有些研究利用潜类选择模型(LCCMs)来解释多重决策规则,但其中许多规则(如非连续规则)仍有待探索。本文将 MNL 与我们开发的新选择模型--广义随机分条件模型 (GRDM) 结合起来,制定、估计和评估了一种 LCCM。GRDM 解决了现有分离选择模型的不足,允许属性之间的相对重要性,并且对无关属性不敏感。与大多数非补偿模型不同的是,它具有可操作性,并包含随机误差项,可捕捉不同选择情况下未观察到的异质性。GRDM 可表示为通用 Logit(UL)模型,这有助于得出边际替代率和弹性等福利指标,并能用传统软件包对模型进行估计。结合 GRDM 和 MNL 的 LCCM 在两个大型案例研究中进行了估算:骑自行车者的路线选择和公共交通路线选择。我们将结果与其他相关的 LCCM 规范和个人选择模型进行了比较,发现 MNL + GRDM LCCM 与数据的拟合效果最佳。我们还解释了拟合参数并计算了边际替代率,这与行为预期相一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信