Representation and Inference of Lexicographic Preference Models and Their Variants

References

• Bridges D. S. A numerical representation of intransitive preferences on a countable set. J. Econom. Theory (1983) 30:213–217Crossref, Google Scholar
• Broder A. Assessing the empirical validity of the “take-the-best” heuristic as a model of human probabilistic inference. J. Experiment. Psych.: Learn., Memory, Cognition (2000) 26(5):1332–1346Crossref, Google Scholar
• Chateauneuf A. Continuous representation of a preference relation on a connected topological space. J. Math. Econom. (1987) 16:139–146Crossref, Google Scholar
• Colman A. M., Stirk J. A. Singleton bias and lexicographic preferences among equally valued alternatives. J. Econom. Behav. Organ. (1999) 40(4):337–351Crossref, Google Scholar
• Debreu G., Thrall R. M., Coombs C. H., Davis R. L. Representation of a preference ordering by a numerical function. Decision Processes (1954) (John Wiley, New York) 159–165Google Scholar
• DeSarbo W. S., Fong D. K. H., Liechty J. C. Dynamic models incorporating individual heterogeneity: Utility evolution in conjoint analysis. Marketing Sci. (2005) 24(2):285–293Link, Google Scholar
• Dhar R., Nowlis S. M. The effect of time pressure on consumer choice deferral. J. Consumer Res. (1999) 25(4):369–384Crossref, Google Scholar
• Drolet A., Luce M. F. Cognitive load and trade-off avoidance. J. Consumer Res. (2004) 31:63–77Crossref, Google Scholar
• Evgeniou T., Boussios C., Zacharia G. Generalized robust conjoint estimation. Marketing Sci. (2005) 24(3):415–429Link, Google Scholar
• Fishburn P. C. Lexicographic orders, utilities and decision rules: A survey. Management Sci. (1974) 20(11):1442–1471Link, Google Scholar
• Fishburn P. C. Axioms for lexicographic preferences. Rev. Econom. Stud. (1975) 42(3):415–419Crossref, Google Scholar
• Ford J. K., Schmitt N., Schechtman S. L., Hults B. M., Doherty M. L. Process tracing methods: Contributions, problems, and neglected research questions. Organ. Behav. Human Decision Processes (1989) 43:75–117Crossref, Google Scholar
• Garey M. R., Johnson D. S.Computer and Intractability (1979) (W. H. Freeman and Company, New York) Google Scholar
• Gigerenzer G., Hoffrage U., Kleinbolting H. Probabilistic mental models: A Brunswikian theory of confidence. Psych. Rev. (1991) 98:506–528Crossref, Google Scholar
• Gilbride T. J., Allenby G. M. A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Sci. (2004) 23(3):391–406Link, Google Scholar
• Gonzalez-Vallejo C., Bonazzi A., Shapiro A. J. Effects of vague probabilities and of vague payoffs on preference: A model comparison analysis. J. Math. Psych. (1996) 40:130–140Crossref, Google Scholar
• Grover R., Srinivasan V. A simultaneous approach to market segmentation and market structure. J. Marketing Res. (1987) 24(May):139–153Crossref, Google Scholar
• Hauser J. R., Shugan S. M. Intensity measures of consumer preference. Oper. Res. (1980) 28(2):278–320Link, Google Scholar
• Hauser J. R., Toubia O. The impact of utility balance and endogeneity in conjoint analysis. Marketing Sci. (2005) 24(3):498–507Link, Google Scholar
• Jedidi K., Kohli R. Probabilistic subset-conjunctive models for heterogeneous consumers. J. Marketing Res. (2005) 42(November):483–494Crossref, Google Scholar
• Kahn B., Baron J. An exploratory study of choice rules favored for high-stakes decisions. J. Consumer Psych. (1995) 4(4):305–328Crossref, Google Scholar
• Kannan P., Wright G. Modeling and testing structured markets: A nested logit approach. Marketing Sci. (1991) 10(Winter):58–82Link, Google Scholar
• Knoblauch V. Lexicographic orders and preference representation. J. Math. Econom. (2000) 34(2):255–267Crossref, Google Scholar
• Kohli R. Lexicographic systems. Complexity (1999) 4(4):15–25Crossref, Google Scholar
• Kohli R., Jedidi K. Probabilistic subset conjunction. Psychometrika (2005) 70(4):737–757Crossref, Google Scholar
• Kohli R., Krishnamurti R., Jedidi K. Subset conjunctive rules for breast-cancer diagnosis. Discrete Appl. Math. (2006) 154(7):1100–1132Crossref, Google Scholar
• Kruskal J. B. Analysis of factorial experiments by estimating monotone transformations of the data. J. Roy. Statist. Soc., Ser. B (Methodological) (1965) 27(2):251–263Google Scholar
• Manrai A. K., Sinha P. K. Elimination-by-cutoffs. Marketing Sci. (1989) 8(2):133–152Link, Google Scholar
• Martignon L., Hoffrage U., Gigerenzer G., Todd P. M.ABC Research Group Why does one-reason decision making work? A case study in ecological rationality. Simple Heuristics That Make Us Smart (1999) (Oxford University Press, New York) 119–140Google Scholar
• Martignon L., Hoffrage U. Fast, frugal and fit: Simple heuristics for paired comparison. Theory Decision (2002) 52:29–71Crossref, Google Scholar
• Martignon L., Schmitt M. Simplicity and robustness of fast and frugal heuristics. Minds Machines (1999) 9:565–593Crossref, Google Scholar
• Mosier C. I. Problems and designs of cross-validation. Educational Psych. Measurement (1951) 11:5–11Crossref, Google Scholar
• Payne J. W., Bettman J. R., Johnson E. L. Adaptive strategy selection in decision making. J. Experiment. Psych.: Learn., Memory, Cognition (1988) 14:534–552Crossref, Google Scholar
• Randall T., Terwiesch C., Ulrich K. T. User design of customized products. Marketing Sci. (2007) 26(2):268–280Link, Google Scholar
• Roedder-John D. Consumer socialization of children: A retrospective look at twenty-five years of research. J. Consumer Res. (1999) 26(3):183–213Crossref, Google Scholar
• Russell G., Kamakura W. Understanding brand competition using micro and macro scanner data. J. Marketing Res. (1994) 31(May):289–303Crossref, Google Scholar
• Schmitt M., Martignon L. Complexity of lexicographic strategies on binary cues. (1999) . Working paper, Lehrstuhl Mathematik und Informatik, Ruhr Universitat, D-44780, Bochum, GermanyGoogle Scholar
• Simon H. A. Rational choice and the structure of the environment. Psych. Rev. (1956) 63:129–138Crossref, Google Scholar
• Slovic P. Choice between equally valued alternatives. J. Experiment. Psych.: Human Perception Performance (1975) 1:280–287Crossref, Google Scholar
• Srinivasan V., Shocker A. D. Estimating the weights for multiple attributes in a composite criterion using pairwise judgments. Psychometrika (1973) 38(4):473–493Crossref, Google Scholar
• Toubia O., Hauser J. R., Simester D. I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41(1):116–131Crossref, Google Scholar
• Toubia O., Hauser J. R., Simester D. I., Dahan E. Fast polyhedral adaptive conjoint estimation. Marketing Sci. (2003) 22(3):273–303Link, Google Scholar
• Tversky A. Elimination by aspects: A theory of choice. Psych. Rev. (1972) 79:281–299Crossref, Google Scholar
• Tversky A., Sattah S., Slovic P. Contingent weighting in judgment and choice. Psych. Rev. (1988) 95:371–384Crossref, Google Scholar
• Urban G. L., Johnson P. L., Hauser J. R. Testing competitive market structures. Marketing Sci. (1984) 3(2):83–112Link, Google Scholar
• Varian H. R.Microeconomic Analysis (1984) (W. W. Norton, New York) Google Scholar
• Wakker P. Continuity of preference relations for separable topologies. Internat. Econom. Rev. (1988) 29(1):105–110Crossref, Google Scholar
• Westenberg M. R. M., Koele P. Multi-attribute evaluation processes: Methodological and conceptual issues. Acta Psychologica (1994) 87:65–84Crossref, Google Scholar
• Yee M., Dahan E., Hauser J. R., Orlin J. Greedoid-based noncompensatory consideration-then-choice inference. Marketing Sci. (2007) . ForthcomingGoogle Scholar