The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jordan J. Louviere - One of the best experts on this subject based on the ideXlab platform.
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Best worst Discrete Choice experiments in health: methods and an application.
Social Science & Medicine, 2012Co-Authors: Emily Lancsar, Jordan J. Louviere, Cam Donaldson, Gillian Currie, Leonie BurgessAbstract:A key objective of Discrete Choice experiments is to obtain sufficient quantity of high quality Choice data to estimate Choice models to be used to explore various policy/clinically relevant issues. This paper focuses on a relatively new form of Choice experiment, ‘Best Worst Discrete Choice Experiments’ (BWDCEs) and their relevance to health research as a new way to meet such an objective. We explain what BWDCEs are, how and when to apply them and we present several analytical approaches to model the resulting data. We demonstrate this preference elicitation approach in an empirical application exploring preferences of 898 members of the general public in Edmonton and Calgary, Canada for treatment of cardiac arrest occurring in a public place and show the gains achieved compared to traditional analysis of first best data. We suggest that BWDCEs are a valuable way to investigate preferences in the health sector and discuss implications for task design, analysis and areas for future research.
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Discrete Choice Experiments Are Not Conjoint Analysis
Journal of Choice Modelling, 2010Co-Authors: Jordan J. Louviere, Terry N. Flynn, Richard T. CarsonAbstract:We briefly review and discuss traditional conjoint analysis (CA) and Discrete Choice experiments (DCEs), widely used stated preference elicitation methods in several disciplines. We pay particular attention to the origins and basis of CA, and show that it is generally inconsistent with economic demand theory, and is subject to several logical inconsistencies that make it unsuitable for use in applied economics, particularly welfare and policy assessment. We contrast this with DCEs that have a long-standing, well-tested theoretical basis in random utility theory, and we show why and how DCEs are more general and consistent with economic demand theory. Perhaps the major message, though, is that many studies that claim to be doing conjoint analysis are really doing DCE.
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detecting attribute by covariate interactions in Discrete Choice models
2010Co-Authors: Kyuseop Kwak, Paul Wang, Jordan J. LouviereAbstract:This paper introduces a simple way to identify attribute by covariate interactions in Discrete Choice models. This is important because modelling such interactions is an effective way to account for systematic taste variation or preference heterogeneity across different consumers. Using a simulated data set to mimic a well-known phenomenon of selective attention to design attributes, we tested our proposed approach in the banking service context. Our proposed approach was successful in detecting the attribute by covariate interactions implied by the data generation process and was found to outperform both full and stepwise interaction models. Such findings have implications for both academics and practitioners of the marketing research community in general and Choice modelling field in particular.
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Designing Discrete Choice Experiments: Do Optimal Designs Come at a Price?
Journal of Consumer Research, 2008Co-Authors: Jordan J. Louviere, Towhidul Islam, Nada Wasi, Deborah J. Street, Leonie BurgessAbstract:In Discrete Choice experiments, design decisions are crucial for determining data quality and costs. While high statistical efficiency designs are desirable, they may come at a price if they increase the cognitive burden for respondents. We address this problem by designing 44 experiments that systematically vary numbers of attributes and attribute level differences. Our results for two product categories suggest that respondents systematically are less consistent in answering Choice questions as statistical efficiency increases. This relationship holds regardless of the number of attributes and is statistically significant even if one accommodates preference heterogeneity. Implications for practice and future research are discussed.
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what influences participation in genetic carrier testing results from a Discrete Choice experiment
Journal of Health Economics, 2006Co-Authors: Jane Hall, Ishrat Hossain, Denzil G Fiebig, Madeleine King, Jordan J. LouviereAbstract:This study explores factors that influence participation in genetic testing programs and the acceptance of multiple tests. Tay Sachs and cystic fibrosis are both genetically determined recessive disorders with differing severity, treatment availability, and prevalence in different population groups. We used a Discrete Choice experiment with a general community and an Ashkenazi Jewish sample; data were analysed using multinomial logit with random coefficients. Although Jewish respondents were more likely to be tested, both groups seem to be making very similar tradeoffs across attributes when they make genetic testing Choices.
Leonie Burgess - One of the best experts on this subject based on the ideXlab platform.
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valuing sf 6d health states using a Discrete Choice experiment
Medical Decision Making, 2014Co-Authors: Richard Norman, Rosalie Viney, Madeleine King, John Brazier, Leonie Burgess, Julie Ratcliffe, Paula Cronin, Deborah J. StreetAbstract:Background. SF-6D utility weights are conventionally produced using a standard gamble (SG). SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques. Recent advances in Discrete Choice methods have allowed estimation of utility weights. The objective was to produce Australian utility weights for the SF-6D and to explore the application of Discrete Choice experiment (DCE) methods in this context. We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D. Methods. We designed an online DCE and administered it to an Australia-representative online panel (n = 1017). A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a random-effects probit model. The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0, respectively, to provide an algorithm for Australian cost-utility...
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Best worst Discrete Choice experiments in health: methods and an application.
Social Science & Medicine, 2012Co-Authors: Emily Lancsar, Jordan J. Louviere, Cam Donaldson, Gillian Currie, Leonie BurgessAbstract:A key objective of Discrete Choice experiments is to obtain sufficient quantity of high quality Choice data to estimate Choice models to be used to explore various policy/clinically relevant issues. This paper focuses on a relatively new form of Choice experiment, ‘Best Worst Discrete Choice Experiments’ (BWDCEs) and their relevance to health research as a new way to meet such an objective. We explain what BWDCEs are, how and when to apply them and we present several analytical approaches to model the resulting data. We demonstrate this preference elicitation approach in an empirical application exploring preferences of 898 members of the general public in Edmonton and Calgary, Canada for treatment of cardiac arrest occurring in a public place and show the gains achieved compared to traditional analysis of first best data. We suggest that BWDCEs are a valuable way to investigate preferences in the health sector and discuss implications for task design, analysis and areas for future research.
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Designing Discrete Choice Experiments: Do Optimal Designs Come at a Price?
Journal of Consumer Research, 2008Co-Authors: Jordan J. Louviere, Towhidul Islam, Nada Wasi, Deborah J. Street, Leonie BurgessAbstract:In Discrete Choice experiments, design decisions are crucial for determining data quality and costs. While high statistical efficiency designs are desirable, they may come at a price if they increase the cognitive burden for respondents. We address this problem by designing 44 experiments that systematically vary numbers of attributes and attribute level differences. Our results for two product categories suggest that respondents systematically are less consistent in answering Choice questions as statistical efficiency increases. This relationship holds regardless of the number of attributes and is statistically significant even if one accommodates preference heterogeneity. Implications for practice and future research are discussed.
Ewout W Steyerberg - One of the best experts on this subject based on the ideXlab platform.
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patients and urologists preferences for prostate cancer treatment a Discrete Choice experiment
British Journal of Cancer, 2013Co-Authors: E W De Bekkergrob, Marielouise Essinkbot, Ida J. Korfage, Michiel C J Bliemer, Bas Donkers, Chris H Bangma, Monique J Roobol, Ewout W SteyerbergAbstract:Patients’ and urologists’ preferences for prostate cancer treatment: a Discrete Choice experiment
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patients preferences for osteoporosis drug treatment a Discrete Choice experiment
Osteoporosis International, 2008Co-Authors: E W De Bekkergrob, Marielouise Essinkbot, Willem Jan Meerding, H A P Pols, Bart W Koes, Ewout W SteyerbergAbstract:Summary Active case finding for osteoporosis is used to identify patients at high fracture risk who may benefit from preventive drug treatment. We investigated the relative weight that women place on various aspects of preventive drugs in a Discrete Choice experiment. Our patients said they were prepared to take preventive drugs even if side effects were expected.
Mogens Fosgerau - One of the best experts on this subject based on the ideXlab platform.
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Discrete Choice and rational inattention a general equivalence result
International Economic Review, 2020Co-Authors: Mogens Fosgerau, Emerson Melo, Andre De Palma, Matthew ShumAbstract:This paper establishes a general equivalence between Discrete Choice and rational inattention models. We show that the Choice probabilities emerging from any random utility Discrete Choice model can be obtained from a class of suitably generalized rational inattention models, and vice versa. Thus any Discrete Choice model can be given an interpretation in terms of boundedly rational behavior. The underlying idea is that the surplus function of a Discrete Choice model has a convex conjugate that is a generalized entropy (which is a suitable generalization of the Shannon entropy function). These generalized entropies are used to construct an information cost function for a generalized rational inattention model. We denote this class of rational inattention problems as Generalized Entropic Rational Inattention (GERI) models.
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Discrete Choice and rational inattention a general equivalence result
Social Science Research Network, 2020Co-Authors: Mogens Fosgerau, Emerson Melo, Andre De Palma, Matthew ShumAbstract:This note establishes a general equivalence between Discrete Choice and rational inattention models. We exploit convex-analytic properties of these models to show that the Choice probabilities emerging from any additive random utility Discrete Choice model can be rationalized from a class of suitably generalized rational inattention models, and vice versa. Thus any Discrete Choice model can be given an interpretation in terms of boundedly rational behavior.
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Discrete Choice and Rational Inattention: A General Equivalence Result
2017Co-Authors: Mogens Fosgerau, Emerson Melo, Andre De Palma, Matthew ShumAbstract:This paper establishes a general equivalence between Discrete Choice and rational inattention models. Matejka and McKay (2015, AER) showed that when information costs are modelled using the Shannon entropy function, the resulting Choice probabilities in the rational inattention model take the multinomial logit form. By exploiting convex-analytic properties of the Discrete Choice model, we show that when information costs are modelled using a class of generalized entropy functions, the Choice probabilities in any rational inattention model are observationally equivalent to some additive random utility Discrete Choice model and vice versa. Thus any additive random utility model can be given an interpretation in terms of boundedly rational behavior. This includes empirically relevant specifications such as the probit and nested logit models.
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a practical test for the Choice of mixing distribution in Discrete Choice models
Transportation Research Part B-methodological, 2007Co-Authors: Mogens Fosgerau, Michel BierlaireAbstract:The Choice of a specific distribution for random parameters of Discrete Choice models is a critical issue in transportation analysis. Indeed, various pieces of research have demonstrated that an inappropriate Choice of the distribution may lead to serious bias in model forecast and in the estimated means of random parameters. In this paper, we propose a practical test, based on seminonparametric techniques. The test is analyzed both on synthetic and real data, and is shown to be simple and powerful.
Matthew Shum - One of the best experts on this subject based on the ideXlab platform.
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duality in dynamic Discrete Choice models
arXiv: Econometrics, 2021Co-Authors: Khai Xiang Chiong, Alfred Galichon, Matthew ShumAbstract:Using results from convex analysis, we investigate a novel approach to identification and estimation of Discrete Choice models which we call the Mass Transport Approach (MTA). We show that the conditional Choice probabilities and the Choice-specific payoffs in these models are related in the sense of conjugate duality, and that the identification problem is a mass transport problem. Based on this, we propose a new two-step estimator for these models; interestingly, the first step of our estimator involves solving a linear program which is identical to the classic assignment (two-sided matching) game of Shapley and Shubik (1971). The application of convex-analytic tools to dynamic Discrete Choice models, and the connection with two-sided matching models, is new in the literature.
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Discrete Choice and rational inattention a general equivalence result
International Economic Review, 2020Co-Authors: Mogens Fosgerau, Emerson Melo, Andre De Palma, Matthew ShumAbstract:This paper establishes a general equivalence between Discrete Choice and rational inattention models. We show that the Choice probabilities emerging from any random utility Discrete Choice model can be obtained from a class of suitably generalized rational inattention models, and vice versa. Thus any Discrete Choice model can be given an interpretation in terms of boundedly rational behavior. The underlying idea is that the surplus function of a Discrete Choice model has a convex conjugate that is a generalized entropy (which is a suitable generalization of the Shannon entropy function). These generalized entropies are used to construct an information cost function for a generalized rational inattention model. We denote this class of rational inattention problems as Generalized Entropic Rational Inattention (GERI) models.
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Discrete Choice and rational inattention a general equivalence result
Social Science Research Network, 2020Co-Authors: Mogens Fosgerau, Emerson Melo, Andre De Palma, Matthew ShumAbstract:This note establishes a general equivalence between Discrete Choice and rational inattention models. We exploit convex-analytic properties of these models to show that the Choice probabilities emerging from any additive random utility Discrete Choice model can be rationalized from a class of suitably generalized rational inattention models, and vice versa. Thus any Discrete Choice model can be given an interpretation in terms of boundedly rational behavior.
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Discrete Choice and Rational Inattention: A General Equivalence Result
2017Co-Authors: Mogens Fosgerau, Emerson Melo, Andre De Palma, Matthew ShumAbstract:This paper establishes a general equivalence between Discrete Choice and rational inattention models. Matejka and McKay (2015, AER) showed that when information costs are modelled using the Shannon entropy function, the resulting Choice probabilities in the rational inattention model take the multinomial logit form. By exploiting convex-analytic properties of the Discrete Choice model, we show that when information costs are modelled using a class of generalized entropy functions, the Choice probabilities in any rational inattention model are observationally equivalent to some additive random utility Discrete Choice model and vice versa. Thus any additive random utility model can be given an interpretation in terms of boundedly rational behavior. This includes empirically relevant specifications such as the probit and nested logit models.
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duality in dynamic Discrete Choice models
Quantitative Economics, 2016Co-Authors: Khai Xiang Chiong, Alfred Galichon, Matthew ShumAbstract:Using results from Convex Analysis, we investigate a novel approach to identification and estimation of Discrete-Choice models that we call the mass transport approach. We show that the conditional Choice probabilities and the Choice-specific payoffs in these models are related in the sense of conjugate duality, and that the identification problem is a mass transport problem. Based on this, we propose a new two-step estimator for these models; interestingly, the first step of our estimator involves solving a linear program that is identical to the classic assignment (two-sided matching) game of Shapley and Shubik (1971). The application of convex-analytic tools to dynamic Discrete-Choice models and the connection with two-sided matching models is new in the literature. Monte Carlo results demonstrate the good performance of this estimator, and we provide an empirical application based on Rust's (1987) bus engine replacement model.
