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The study of respondent heterogeneity is one of the main areas of research in the eld of choice modelling. The general emphasis is on variations across respondents in relative taste parameters while maintaining the assumption of homogeneous utility maximising decision rules. While recent work has allowed for di erences in the utility speci cation across respondents in the context of looking at heterogeneous information processing strategies, the underlying assumption that all respondents employ the same choice paradigm remains. This is despite evidence in the literature that di erent paradigms work di erently well on given datasets. In this paper, we argue that such di erences may in fact extend to respondents within a single dataset. We accommodate these differences in a latent class model, where individual classes make use of di erent underlying paradigms.We present four applications using three di erent datasets, showing mixtures between standard" random utility maximisation models and lexicography based models, models with multiple reference points, elimination by aspects models and random regret minimisation models. In each of the case studies, the behavioural mixing model obtains signi cant gains in t over the base structure where all respondents are hypothesised to use the same rule. The ndings o er important further insights into the behavioural patterns of respondents. There is also evidence that what is retrieved as taste heterogeneity in standard models may in fact be heterogeneity in decision rules.

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.It's not that I don't care, I just don't care very much: confounding between attribute non-attendance and taste heterogeneityStephane Hess * Amanda StathopoulosAbstract With the growing interest in the topic of attribute non-attendance, there is now widespread use of latent class (LC) structures aimed at capturing such behaviour, across a number of different fields. Specifically, these studies rely on a confirmatory LC model, using two separate values for each coefficient, one of which is fixed to zero while the other is estimated, and then use the obtained class probabilities as an indication of the degree of attribute non-attendance. In the present paper, we argue that this approach is in fact misguided, and that the results are likely to be affected by confounding with regular taste heterogeneity. We contrast the confirmatory model with an exploratory LC structure in which the values in both classes are estimated. We also put forward a combined latent class mixed logit model (LC-MMNL) which allows jointly for attribute non-attendance and for continuous taste heterogeneity. Across three separate case studies, the exploratory LC model clearly rejects the confirmatory LC approach and suggests that rates of non-attendance may be much lower than what is suggested by the standard model, or even zero. The combined LC-MMNL model similarly produces significant improvements in model fit, along with substantial reductions in the implied rate of attribute non-attendance, in some cases even eliminating the phenomena across the sample population. Our results thus call for a reappraisal of the large body of recent work that has implied high rates of attribute non-attendance for some attributes. Finally, we also highlight a number of general issues with attribute non-attendance, in particular relating to the computation of willingness to pay measures.

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An increasing number of studies are concerned with the use of alternatives to random utility maximisation as a decision rule in choice models, with a particular emphasis on regret minimisation over the last few years. The initial focus was on revealing which paradigm fits best for a given dataset, while later studies have looked at variation in decision rules across respondents within a dataset. However, only limited effort has gone towards understanding the potential drivers of decision rules, i.e. what makes it more or less likely that the choices of a given respondent can be explained by a particular paradigm. The present paper puts forward the notion that unobserved character traits can be a key source of this type of heterogeneity and proposes to characterise these traits through a latent variable within a hybrid framework. In an empirical application on stated choice data, we make use of a mixed random utility-random regret structure, where the allocation to a given class is driven in part by a latent variable which at the same time explains respondents' stated satisfaction with their real world commute journey. Results reveal a linkage between the likely decision rule and the stated satisfaction with the real world commute conditions. Notably, the most regret-prone respondents in our sample are more likely to have aligned their real-life commute performance more closely with their aspirational values.