Modelling intransitive preferences: A random-effects approach

Tsai, Rung Ching; Böckenholt, Ulf

doi:10.1016/j.jmp.2005.11.004

Cited by 25 publications

(50 citation statements)

References 23 publications

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“…Preliminary support for this assumption is provided by the lottery data analysis reported by Tsai and Bo¨ckenholt (2006) in which the same covariance structure could be fit to the data of each of the judges. Variability in the within-judge judgments comes solely from the B's.…”

Section: Within-judge Representationmentioning

confidence: 94%

“…The dependent-response (DR) model of Tsai and Bo¨ckenholt (2006) for single-subject data is based on the assumption that evaluations of the same item can differ from trial to trial in systematic ways. This assumption can be incorporated in our framework by decomposing the latent judgments of judge i about options j and k in the ðj; kÞ comparison as…”

Section: Within-judge Representationmentioning

confidence: 99%

“…We adopt Takane's (1987) analysis-of-covariance structure to describe how judges differ in the evaluation of the choice options. The within-subject component is based on the random-effects model recently developed by Tsai and Bo¨ckenholt (2006) for the analysis of single-subject paired comparison data. Their approach does not constrain the latent utility of a choice option to be identical in different pairwise comparisons which in a reported analysis of Tversky's (1969) gambling study proved crucial in modeling inconsistent choice patterns.…”

Section: Introductionmentioning

confidence: 99%

“…By unifying Tsai and Bo¨ckenholt's (2006) approach with Takane's (1987) between-subject models, we obtain a general class of paired comparisons models that can account simultaneously for transitive choice behavior and systematic deviations from this normative standard. The next section discusses the between-and within-subject component of the proposed model class in detail.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the importance of distinguishing between within- and between-subject effects in intransitive intertemporal choice

Tsai

Böckenholt

2008

Journal of Mathematical Psychology

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Section: Within-judge Representationmentioning

confidence: 94%

Section: Within-judge Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the importance of distinguishing between within- and between-subject effects in intransitive intertemporal choice

Tsai

Böckenholt

2008

Journal of Mathematical Psychology

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“…Iverson and Falmagne [22] have a sophisticated study of the distributions of the statistics related to this kind of hypothesis testing. See also Tsai and Böckeholt [59] for a more recent study. 5 In a more structured framework involving choice over risky prospects, studies of the specific patterns of violations of transitivity have been used to find support for regret theory (e.g., Loomes et al [29,30]) and for prospect theory (e.g., Starmer [55]).…”

Section: What Is Tested In Recent Studies?mentioning

confidence: 99%

On the meaningfulness of testing preference axioms in stated preference discrete choice experiments

2011

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A stream of studies on evaluation of health care services and public goods have developed tests of the preference axioms of completeness and transitivity and methods for detecting other preference phenomena such as unstability, learning- and tiredness effects, and random error, in stated preference discrete choice experiments. This methodological paper tries to identify the role of the preference axioms and other preference phenomena in the context of such experiments and discusses whether or how such axioms and phenomena can be subject to meaningful (statistical) tests.

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The Role of Independence and Stationarity in Probabilistic Models of Binary Choice

Regenwetter

Davis‐Stober

2017

Behavioral Decision Making

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After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within-respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties. Copyright © 2017 John Wiley & Sons, Ltd.Additional Supporting Information may be found online in the supporting information tab for this article.key words probabilistic choice models; iid assumptions; modeling heterogeneity; statistical testingWhen selecting a probabilistic choice model to use in their work, scholars may want to consider which model class makes what assumptions and which model class warrants or precludes what types of data manipulations. In this conceptual and theoretical synthesis, we summarize, for several prominent probabilistic choice models, whether or not they assume mutually independent (in the sense of probability theory) responses and/or stationary response probabilities. 1 We also unpack whether various models are invariant under varying preferences, varying utility functions, or varying response probabilities, either because of individual differences or because of within-subject heterogeneity in behavior. We discuss what it means to average or pool binary choice data across respondents and/or across time points. To that end, we spell out, for some prominent statistical tests of probabilistic choice models, whether they, in turn, make additional independence and/or stationarity assumptions even when the models themselves do not require such properties to hold. We concentrate on those models, statistical tests, and data-generating processes whose basic observational unit-one observation-is one person's selection of one choice alternative among two, at one moment in time. Knowing a model's interconnected assumptions helps the applied scholar better understand their role in empirical evaluations. In particular, when a model performs or replicates poorly, knowing all assumptions is crucial for revising the theory in question. Motivation and backgroundConsider the following two questions posed in an experiment. We use these questions for various illustrations thr...

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Modelling intransitive preferences: A random-effects approach

Cited by 25 publications

References 23 publications

On the importance of distinguishing between within- and between-subject effects in intransitive intertemporal choice

On the importance of distinguishing between within- and between-subject effects in intransitive intertemporal choice

On the meaningfulness of testing preference axioms in stated preference discrete choice experiments

The Role of Independence and Stationarity in Probabilistic Models of Binary Choice

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