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Greedoid-Based Noncompensatory Inference

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Author Info

  • Michael Yee

    ()
    (Lincoln Laboratory, Massachusetts Institute of Technology, 244 Wood Street, Lexington, Massachusetts 02420-9108)

  • Ely Dahan

    ()
    (UCLA Anderson School, 110 Westwood Plaza, B-514, Los Angeles, California 90095)

  • John R. Hauser

    ()
    (Massachusetts Institute of Technology, E40-179, 50 Memorial Drive, Cambridge, Massachusetts 02142)

  • James Orlin

    ()
    (Massachusetts Institute of Technology, E53-363, 50 Memorial Drive, Cambridge, Massachusetts 02142)

Abstract

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.

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File URL: http://dx.doi.org/10.1287/mksc.1060.0213
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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 26 (2007)
Issue (Month): 4 (07-08)
Pages: 532-549

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Handle: RePEc:inm:ormksc:v:26:y:2007:i:4:p:532-549

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Related research

Keywords: lexicography; noncompensatory decision rules; choice heuristics; optimization methods in marketing; conjoint analysis; product development; consideration sets;

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Citations

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Cited by:
  1. Anderson, Simon P & Renault, Régis, 2012. "The advertising mix for a search good," CEPR Discussion Papers 8756, C.E.P.R. Discussion Papers.
  2. Bernhard Weiss & Uwe Dulleck & Franz Hackl & Rudolf Winter-Ebmer, 2008. "Buying Online: Sequential Decision Making by Shopbot Visitors," Economics working papers 2008-10, Department of Economics, Johannes Kepler University Linz, Austria.
  3. Mandler, Michael & Manzini, Paola & Mariotti, Marco, 2012. "A million answers to twenty questions: Choosing by checklist," Journal of Economic Theory, Elsevier, vol. 147(1), pages 71-92.
  4. Berg, Nathan & Gigerenzer, Gerd, 2010. "As-if behavioral economics: Neoclassical economics in disguise?," MPRA Paper 26586, University Library of Munich, Germany.
  5. Paola Manzini & Marco Mariotti, 2009. "Consumer choice and revealed bounded rationality," Economic Theory, Springer, vol. 41(3), pages 379-392, December.
  6. Jella Pfeiffer & Michael Scholz, 2013. "A Low-Effort Recommendation System with High Accuracy," Business & Information Systems Engineering, Springer, vol. 5(6), pages 397-408, December.
  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. Berg, Nathan, 2010. "Success from Satisficing and Imitation: Entrepreneurs’ Location Choice and Implications of Heuristics for Local Economic Development," MPRA Paper 26594, University Library of Munich, Germany.
  9. 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.
  10. Nicolas Houy, 2011. "Common characterizations of the untrapped set and the top cycle," Theory and Decision, Springer, vol. 70(4), pages 501-509, April.

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