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Jordan J. Louviere - One of the best experts on this subject based on the ideXlab platform.

  • Discrete Choice Experiments Are Not Conjoint Analysis
    Journal of Choice Modelling, 2010
    Co-Authors: Jordan J. Louviere, Terry N. Flynn, Richard T. Carson
    Abstract:

    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.

  • modeling the choices of individual decision makers by combining efficient choice experiment designs with extra preference information
    Journal of choice modelling, 2008
    Co-Authors: Jordan J. Louviere, Deborah J Street, Leonie Burgess, Nada Wasi, Towhidul Islam, A A J Marley
    Abstract:

    Abstract We show how to combine statistically efficient ways to design discrete choice experiments based on Random Utility Theory with new ways of collecting additional information that can be used to expand the amount of available choice information for modeling the choices of individual decision makers. Here we limit ourselves to problems involving generic choice options and linear and additive indirect Utility functions, but the approach potentially can be extended to include choice problems with non-additive Utility functions and non-generic/labeled options/attributes. The paper provides several simulated examples, a small empirical example to demonstrate proof of concept, and a larger empirical example based on many experimental conditions and large samples that demonstrates that the individual models capture virtually all the variance in aggregate first choices traditionally modeled in discrete choice experiments.

  • conjoint preference elicitation methods in the broader context of Random Utility Theory preference elicitation methods
    2007
    Co-Authors: David A Hensher, Jordan J. Louviere, Joffre Swait
    Abstract:

    The purpose of this chapter is to place conjoint analysis techniques within the broader framework of preference elicitation techniques that are consistent with the Random Utility Theory (RUT) paradigm. This allows us to accomplish the following objectives: explain how Random Utility Theory provides a level playing field on which to compare preference elicitation methods, and why virtually all conjoint methods can be treated as a special case of a much broader theoretical framework. We achieve this by: discussing wider issues in modelling preferences in the RUT paradigm, the implications for understanding consumer decision processes and practical prediction, and how conjoint analysis methods fit into the bigger picture. discussing how a level playing field allows meaningful comparisons of a variety of preference elicitation methods and sources of preference data (conjoint methods are only one of many types), which in turn allows us to unify many disparate research streams; discussing how a level playing field allows sources of preference data from various elicitation methods to be combined, including the important case of relating sources of preference elicitation data to actual market behaviour; discussing the pros and cons of relaxing the simple error assumptions in basic choice models, and how these allow one to capture individual differences without needing individual-level effects; using three cases studies to illustrate the themes of the chapter.

  • deleting irrational responses from discrete choice experiments a case of investigating or imposing preferences
    Health Economics, 2006
    Co-Authors: Emily Lancsar, Jordan J. Louviere
    Abstract:

    Investigation of the 'rationality' of responses to discrete choice experiments (DCEs) has been a theme of research in health economics. Responses have been deleted from DCEs where they have been deemed by researchers to (a) be 'irrational', defined by such studies as failing tests for non-satiation, or (b) represent lexicographic preferences. This paper outlines a number of reasons why deleting responses from DCEs may be inappropriate after first reviewing the Theory underpinning rationality, highlighting that the importance placed on rationality depends on the approach to consumer Theory to which one ascribes. The aim of this paper is not to suggest that all preferences elicited via DCEs are rational. Instead, it is to suggest a number of reasons why it may not be the case that all preferences labelled as 'irrational' are indeed so. Hence, deleting responses may result in the removal of valid preferences; induce sample selection bias; and reduce the statistical efficiency and power of the estimated choice models. Further, evidence suggests Random Utility Theory may be able to cope with such preferences. Finally, we discuss a number of implications for the design, implementation and interpretation of DCEs and recommend caution regarding the deletion of preferences from stated preference experiments.

  • using stated preference discrete choice modeling to evaluate health care programs
    Journal of Business Research, 2004
    Co-Authors: Jane Hall, Rosalie Viney, Marion Haas, Jordan J. Louviere
    Abstract:

    Abstract This paper reports on the potential to use stated preference discrete choice modelling (SPDCM) in health program evaluation. Interest is developing in the translation of these techniques from business, marketing, and environmental economics to health to address the prediction of market share and the estimation of societal benefits. The paper provides an overview of current health program evaluation methods, showing that measuring success in terms of clinical effectiveness, survival, or even quality-adjusted survival may not capture important benefits. The appropriate revealed preference data to estimate demand or value benefits are rarely available in the health care context. SPDCM is based in Random Utility Theory (RUT) and the stated preference data are obtained from choice surveys. This allows a wide range of attributes to be included and the independent effect of each to be quantified. Selected applications in the testing for the value of information provided by genetic tests, in assessing how to improve attendance rates in screening programs, in predicting the uptake of a new immunisation, and in understanding patients' preferences for medications for chronic disease are described. SPDCM has much to offer in both the evaluation of health programs and the prediction of their uptake.

Otto Anker Nielsen - One of the best experts on this subject based on the ideXlab platform.

  • Stochastic user equilibrium with a bounded choice model
    Transportation Research Part B: Methodological, 2018
    Co-Authors: David Watling, Thomas Kjær Rasmussen, Carlo Giacomo Prato, Otto Anker Nielsen
    Abstract:

    Stochastic User Equilibrium (SUE) models allow the representation of the perceptual and preferential differences that exist when drivers compare alternative routes through a transportation network. However, as an effect of the used choice models, conventional applications of SUE are based on the assumption that all available routes have a positive probability of being chosen, however unattractive. In this paper, a novel choice model, the Bounded Choice Model (BCM), is presented along with network conditions for a corresponding Bounded SUE. The model integrates an exogenously-defined bound on the Random Utility of the set of paths that are used at equilibrium, within a Random Utility Theory (RUT) framework. The model predicts which routes are used and unused (the choice sets are equilibrated), while still ensuring that the distribution of flows on used routes accords to a Discrete Choice Model. Importantly, conditions to guarantee existence and uniqueness of the Bounded SUE are shown. Also, a corresponding solution algorithm is proposed and numerical results are reported by applying this to the Sioux Falls network.

Kara M Kockelman - One of the best experts on this subject based on the ideXlab platform.

  • the Random Utility based multiregional input output model solution existence and uniqueness
    Transportation Research Part B-methodological, 2004
    Co-Authors: Yong Zhao, Kara M Kockelman
    Abstract:

    A number of operational land use-transportation models make use of spatial input-output (SIO) models, some of which are based on Random-Utility Theory. The Random-Utility-based multiregional input-output (RUBMRIO) model has been solved in practice by iteratively applying a set of equations. Each of the model equations describes relationships among key model variables. This paper examines the existence and uniqueness of the RUBMRIO solution, which represents the spatial allocation of productive activities and commodity flows. Formulating the set of equations as a fixed-point problem illuminates these two key properties, and provides a general solution algorithm. Several numerical examples illustrate the solution uniqueness and algorithm convergence. These results are valuable for efficient application of such models to large-scale problems. By proving that a unique solution does exist and offering an algorithm that is guaranteed to converge, this work adds valuable support to the growing popularity of this integrated transportation-land use modeling framework.

David Watling - One of the best experts on this subject based on the ideXlab platform.

  • Stochastic user equilibrium with a bounded choice model
    Transportation Research Part B: Methodological, 2018
    Co-Authors: David Watling, Thomas Kjær Rasmussen, Carlo Giacomo Prato, Otto Anker Nielsen
    Abstract:

    Stochastic User Equilibrium (SUE) models allow the representation of the perceptual and preferential differences that exist when drivers compare alternative routes through a transportation network. However, as an effect of the used choice models, conventional applications of SUE are based on the assumption that all available routes have a positive probability of being chosen, however unattractive. In this paper, a novel choice model, the Bounded Choice Model (BCM), is presented along with network conditions for a corresponding Bounded SUE. The model integrates an exogenously-defined bound on the Random Utility of the set of paths that are used at equilibrium, within a Random Utility Theory (RUT) framework. The model predicts which routes are used and unused (the choice sets are equilibrated), while still ensuring that the distribution of flows on used routes accords to a Discrete Choice Model. Importantly, conditions to guarantee existence and uniqueness of the Bounded SUE are shown. Also, a corresponding solution algorithm is proposed and numerical results are reported by applying this to the Sioux Falls network.

Stefano De Luca - One of the best experts on this subject based on the ideXlab platform.

  • multilayer feedforward networks for transportation mode choice analysis an analysis and a comparison with Random Utility models
    Transportation Research Part C-emerging Technologies, 2005
    Co-Authors: Giulio Erberto Cantarella, Stefano De Luca
    Abstract:

    Abstract Usually, discrete choice analysis as regards a transportation system is based on Random Utility Theory. Recently, a different approach to choice analysis, based on Artificial Neural Network (actually a multilayer feedforward network—MLFFN) models, has been proposed by several researchers. Such modelling approach can address three main demand simulation issues (trip generation, trip distribution and modal split) and has shown good predictive capability. Most of the papers deal with extra-urban (inter-city or intra-regional) trips and they are calibrated on aggregate data, simulating demand flows, results have been compared with regressive models. An alternative and more stimulating approach can be followed up by using disaggregate data, as only one paper does, to simulate single-user choice. The aim of this paper is twofold, first to describe the main step towards the successful application of MLFFNs to support travel demand analysis, and second to show that they can be fruitfully applied to analyse transportation mode choice. A deep analysis has been carried out to address each of the major issues needed to make operational an MLFFN. The proposed approach relies on disaggregated (revealed preferences survey data-sets) data taken from two different case studies. The two case studies focus on medium distance intercity journeys, and allow to investigate mode choice for two different trip purposes and two different geographical contexts: journey-to-work of commuters within the Italian region of Veneto and journey-to-study of students towards the rural location of the University of Salerno. MLFFNs performances have been compared with the most effective and advanced closed-form Random Utility Models (RUMs) that can be calibrated on the same survey data-sets. Models validation and comparison have been carried out by using indices commonly used in MLFFNs or RUMs applications, and by introducing many others expressly defined to aid results interpretation.