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Representation and Inference of Lexicographic Preference Models and Their Variants


  • Rajeev Kohli

    () (Graduate School of Business, Columbia University, 506 Uris Hall, New York, New York 10027)

  • Kamel Jedidi

    () (Graduate School of Business, Columbia University, 518 Uris Hall, New York, New York 10027)


The authors propose two variants of lexicographic preference rules. They obtain the necessary and sufficient conditions under which a linear utility function represents a standard lexicographic rule, and each of the proposed variants, over a set of discrete attributes. They then: (i) characterize the measurement properties of the parameters in the representations; (ii) propose a nonmetric procedure for inferring each lexicographic rule from pairwise comparisons of multiattribute alternatives; (iii) describe a method for distinguishing among different lexicographic rules, and between lexicographic and linear preference models; and (iv) suggest how individual lexicographic rules can be combined to describe hierarchical market structures. The authors illustrate each of these aspects using data on personal-computer preferences. They find that two-thirds of the subjects in the sample use some kind of lexicographic rule. In contrast, only one in five subjects use a standard lexicographic rule. This suggests that lexicographic rules are more widely used by consumers than one might have thought in the absence of the lexicographic variants described in the paper. The authors report a simulation assessing the ability of the proposed inference procedure to distinguish among alternative lexicographic models, and between linear-compensatory and lexicographic models.

Suggested Citation

  • Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.
  • Handle: RePEc:inm:ormksc:v:26:y:2007:i:3:p:380-399

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    References listed on IDEAS

    1. Chateauneuf, Alain, 1987. "Continuous representation of a preference relation on a connected topological space," Journal of Mathematical Economics, Elsevier, vol. 16(2), pages 139-146, April.
    2. Colman, Andrew M. & Stirk, Jonathan A., 1999. "Singleton bias and lexicographic preferences among equally valued alternatives," Journal of Economic Behavior & Organization, Elsevier, vol. 40(4), pages 337-351, December.
    3. Peter C. Fishburn, 1975. "Axioms for Lexicographic Preferences," Review of Economic Studies, Oxford University Press, vol. 42(3), pages 415-419.
    4. Bridges, Douglas S., 1983. "Numerical representation of intransitive preferences on a countable set," Journal of Economic Theory, Elsevier, vol. 30(1), pages 213-217, June.
    5. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    6. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    7. V. Srinivasan & Allan Shocker, 1973. "Estimating the weights for multiple attributes in a composite criterion using pairwise judgments," Psychometrika, Springer;The Psychometric Society, vol. 38(4), pages 473-493, December.
    8. John, Deborah Roedder, 1999. " Consumer Socialization of Children: A Retrospective Look at Twenty-Five Years of Research," Journal of Consumer Research, Oxford University Press, vol. 26(3), pages 183-213, December.
    9. Taylor Randall & Christian Terwiesch & Karl T. Ulrich, 2007. "Research Note—User Design of Customized Products," Marketing Science, INFORMS, vol. 26(2), pages 268-280, 03-04.
    10. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    11. Peter C. Fishburn, 1974. "Exceptional Paper--Lexicographic Orders, Utilities and Decision Rules: A Survey," Management Science, INFORMS, vol. 20(11), pages 1442-1471, July.
    12. Dhar, Ravi & Nowlis, Stephen M, 1999. " The Effect of Time Pressure on Consumer Choice Deferral," Journal of Consumer Research, Oxford University Press, vol. 25(4), pages 369-384, March.
    13. Wakker, Peter, 1988. "Continuity of Preference Relations for Separable Topologies," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(1), pages 105-110, February.
    14. Glen L. Urban & Philip L. Johnson & John R. Hauser, 1984. "Testing Competitive Market Structures," Marketing Science, INFORMS, vol. 3(2), pages 83-112.
    15. P. K. Kannan & Gordon P. Wright, 1991. "Modeling and Testing Structured Markets: A Nested Logit Approach," Marketing Science, INFORMS, vol. 10(1), pages 58-82.
    16. John R. Hauser & Olivier Toubia, 2005. "The Impact of Utility Balance and Endogeneity in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(3), pages 498-507, August.
    17. Laura Martignon & Ulrich Hoffrage, 2002. "Fast, frugal, and fit: Simple heuristics for paired comparison," Theory and Decision, Springer, vol. 52(1), pages 29-71, February.
    18. Knoblauch, Vicki, 2000. "Lexicographic orders and preference representation," Journal of Mathematical Economics, Elsevier, vol. 34(2), pages 255-267, October.
    19. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
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    Cited by:

    1. Paola Manzini & Marco Mariotti, 2009. "Consumer choice and revealed bounded rationality," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 41(3), pages 379-392, December.
    2. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
    3. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    4. Allenby, Greg M., 2017. "Structural forecasts for marketing data," International Journal of Forecasting, Elsevier, vol. 33(2), pages 433-441.
    5. Wilfred Amaldoss & James R. Bettman & John W. Payne, 2008. "—Biased but Efficient: An Investigation of Coordination Facilitated by Asymmetric Dominance," Marketing Science, INFORMS, vol. 27(5), pages 903-921, 09-10.
    6. Anja Dieckmann & Katrin Dippold & Holger Dietrich, 2009. "Compensatory versus noncompensatory models for predicting consumer preferences," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 4(3), pages 200-213, April.
    7. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    8. Luís Cabral, 2012. "Lock in and switch: Asymmetric information and new product diffusion," Quantitative Marketing and Economics (QME), Springer, vol. 10(3), pages 375-392, September.
    9. Qing Liu & Neeraj Arora, 2011. "Efficient Choice Designs for a Consider-Then-Choose Model," Marketing Science, INFORMS, vol. 30(2), pages 321-338, 03-04.
    10. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    11. John Hauser, 2011. "A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 396-408, July.
    12. repec:eee:jobhdp:v:141:y:2017:i:c:p:29-42 is not listed on IDEAS
    13. Bräuning, Michael & Hüllermeier, Eyke & Keller, Tobias & Glaum, Martin, 2017. "Lexicographic preferences for predictive modeling of human decision making: A new machine learning method with an application in accounting," European Journal of Operational Research, Elsevier, vol. 258(1), pages 295-306.
    14. Heiman, Amir & Lowengart, Oded, 2011. "The effects of information about health hazards in food on consumers' choice process," Journal of Econometrics, Elsevier, vol. 162(1), pages 140-147, May.
    15. Peter Stüttgen & Peter Boatwright & Robert T. Monroe, 2012. "A Satisficing Choice Model," Marketing Science, INFORMS, vol. 31(6), pages 878-899, November.
    16. Pantelis P. Analytis & Amit Kothiyal & Konstantinos Katsikopoulos, 2014. "Multi-attribute utility models as cognitive search engines," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(5), pages 403-419, September.
    17. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.


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