Greedoid-Based Noncompensatory Inference
Greedoid languages provide a basis to infer best-fitting noncompensatory decision rules from full-rank conjoint data or partial-rank data such as consider-then-rank, consider-only, or choice data. Potential decision rules include elimination by aspects, acceptance by aspects, lexicographic by features, and a mixed-rule lexicographic by aspects (LBA) that nests the other rules. We provide a dynamic program that makes estimation practical for a moderately large numbers of aspects. We test greedoid methods with applications to SmartPhones (339 respondents, both full-rank and consider-then-rank data) and computers (201 respondents from Lenk et al. 1996). We compare LBA to two compensatory benchmarks: hierarchical Bayes ranked logit (HBRL) and LINMAP. For each benchmark, we consider an unconstrained model and a model constrained so that aspects are truly compensatory. For both data sets, LBA predicts (new task) holdouts at least as well as compensatory methods for the majority of the respondents. LBA's relative predictive ability increases (ranks and choices) if the task is full rank rather than consider then rank. LBA's relative predictive ability does not change if (1) we allow respondents to presort profiles, or (2) we increase the number of profiles in a consider-then-rank task from 16 to 32. We examine trade-offs between effort and accuracy for the type of task and the number of profiles.
Volume (Year): 26 (2007)
Issue (Month): 4 (07-08)
|Contact details of provider:|| Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA|
Web page: http://www.informs.org/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003.
"Fast Polyhedral Adaptive Conjoint Estimation,"
INFORMS, vol. 22(3), pages 273-303.
- Toubia, Olivier & Simester, Duncan & Hauser, John & Dahan, Ely, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Working papers 4279-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Toubia, Olivier & Simester, Duncan & Hauser, John & Dahan, Ely, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Working papers 4171-01, Massachusetts Institute of Technology (MIT), Sloan School of Management.
- 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.
- Shugan, Steven M, 1980. " The Cost of Thinking," Journal of Consumer Research, Oxford University Press, vol. 7(2), pages 99-111, Se.
- Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
- Nakamura, Yutaka, 2002. "Lexicographic quasilinear utility," Journal of Mathematical Economics, Elsevier, vol. 37(3), pages 157-178, May.
- Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. " Constructive Consumer Choice Processes," Journal of Consumer Research, Oxford University Press, vol. 25(3), pages 187-217, December.
- Dennis H. Gensch, 1987. "A Two-Stage Disaggregate Attribute Choice Model," Marketing Science, INFORMS, vol. 6(3), pages 223-239.
- Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
- V. Srinivasan & Allan Shocker, 1973. "Linear programming techniques for multidimensional analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 38(3), pages 337-369, September.
- Wayne DeSarbo & Donald Lehmann & Gregory Carpenter & Indrajit Sinha, 1996. "A stochastic multidimensional unfolding approach for representing phased decision outcomes," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 485-508, September.
- 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.
- Bettman, James R & Park, C Whan, 1980. " Effects of Prior Knowledge and Experience and Phase of the Choice Process on Consumer Decision Processes: A Protocol Analysis," Journal of Consumer Research, Oxford University Press, vol. 7(3), pages 234-48, December.
- Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
- Peter C. Fishburn, 1974. "Exceptional Paper--Lexicographic Orders, Utilities and Decision Rules: A Survey," Management Science, INFORMS, vol. 20(11), pages 1442-1471, July.
- Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
- Hauser, John R & Wernerfelt, Birger, 1990. " An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Oxford University Press, vol. 16(4), pages 393-408, March.
- Johnson, Eric J & Meyer, Robert J, 1984. " Compensatory Choice Models of Noncompensatory Processes: The Effect of Varying Context," Journal of Consumer Research, Oxford University Press, vol. 11(1), pages 528-41, June.
When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:26:y:2007:i:4:p:532-549. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc)
If references are entirely missing, you can add them using this form.