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Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives

机译:在选择退出的情况下效用最大化和后悔最小化之间的对比

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摘要

An increasing number of studies of choice behaviour are looking at Random Regret Minimisation (RRM) as an alternative to the well established Random Utility Maximisation (RUM) framework. Empirical evidence tends to show small differences in performance between the two approaches, with the implied preference between the models being data-set specific. In the present paper, we discuss how in the context of choice tasks involving an opt out alternative, the differences are potentially more clear cut. Specifically, we hypothesise that when opt out alternatives are framed as a rejection of all the available alternatives, this is likely to have a detrimental impact on the performance of RRM, while the performance of RUM suffers more than RRM when the opt out is framed as a respondent being indifferent between the alternatives on offer. We provide empirical support for these hypotheses through two case studies, using the two different types of opt out alternatives. Our findings suggest that analysts need to carefully evaluate their choice of model structure in the presence of opt out alternatives, while any a priori preference for a given model structure should be taken into account in survey framing.
机译:关于选择行为的越来越多的研究正在将随机后悔最小化(RRM)替代成熟的随机效用最大化(RUM)框架。经验证据倾向于显示两种方法之间在性能上的细微差别,其中模型之间的隐含偏好是特定于数据集的。在本文中,我们讨论在涉及选择退出选择的选择任务的情况下,如何更清楚地区别差异。具体来说,我们假设,当选择退出选择作为拒绝所有可用选择的框架时,这可能会对RRM的性能产生不利影响,而当选择退出的框架为RUM时,RUM的性能遭受的损害要大于RRM。受访者对所提供的替代方案漠不关心。我们通过两个案例研究,使用两种不同类型的选择退出选择,为这些假设提供了经验支持。我们的发现表明,在选择退出的情况下,分析师需要仔细评估他们对模型结构的选择,而在调查框架中应考虑对给定模型结构的任何先验偏好。

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