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Greedoid-Based Noncompensatory Inference

Author

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  • 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.

Suggested Citation

  • Michael Yee & Ely Dahan & John R. Hauser & James Orlin, 2007. "Greedoid-Based Noncompensatory Inference," Marketing Science, INFORMS, vol. 26(4), pages 532-549, 07-08.
  • Handle: RePEc:inm:ormksc:v:26:y:2007:i:4:p:532-549
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    File URL: http://dx.doi.org/10.1287/mksc.1060.0213
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    References listed on IDEAS

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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), pages 379-392.
    2. Petri, Henrik & Voorneveld, Mark, 2016. "Characterizing lexicographic preferences," Journal of Mathematical Economics, Elsevier, vol. 63(C), pages 54-61.
    3. Jella Pfeiffer & Michael Scholz, 2013. "A Low-Effort Recommendation System with High Accuracy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), pages 397-408.
    4. Mandler, Michael & Manzini, Paola & Mariotti, Marco, 2012. "A million answers to twenty questions: Choosing by checklist," Journal of Economic Theory, Elsevier, pages 71-92.
    5. 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, pages 200-213.
    6. Carson, Richard T. & Louviere, Jordan J., 2014. "Statistical properties of consideration sets," Journal of choice modelling, Elsevier, pages 37-48.
    7. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2008. "The Devil is in the Detail: Hints for Practical Optimisation," Economic Analysis and Policy, Elsevier, pages 345-368.
    8. 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.
    9. Nathan Berg & Gerd Gigerenzer, 2010. "As-if behavioral economics: neoclassical economics in disguise?," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, pages 133-166.
    10. Berg, Nathan, 2014. "Success from satisficing and imitation: Entrepreneurs' location choice and implications of heuristics for local economic development," Journal of Business Research, Elsevier, pages 1700-1709.
    11. Carson, Richard T. & Louviere, Jordan J., 2014. "Statistical properties of consideration sets," Journal of choice modelling, Elsevier, pages 37-48.
    12. Simon P. Anderson & Régis Renault, 2013. "The Advertising Mix for a Search Good," Management Science, INFORMS, pages 69-83.
    13. Dulleck, Uwe & Hackl, Franz & Weiss, Bernhard & Winter-Ebmer, Rudolf, 2008. "Buying Online: Sequential Decision Making by Shopbot Visitors," Economics Series 225, Institute for Advanced Studies.
    14. Brighton, Henry & Gigerenzer, Gerd, 2015. "The bias bias," Journal of Business Research, Elsevier, vol. 68(8), pages 1772-1784.
    15. Nathan Berg & Gerd Gigerenzer, 2010. "As-if behavioral economics: neoclassical economics in disguise?," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, pages 133-166.
    16. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    17. Todd Guilfoos & Andreas Duus Pape, 2016. "Predicting human cooperation in the Prisoner’s Dilemma using case-based decision theory," Theory and Decision, Springer, pages 1-32.
    18. 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, pages 396-408.
    19. Karniouchina, Ekaterina V. & Moore, William L. & van der Rhee, Bo & Verma, Rohit, 2009. "Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research," European Journal of Operational Research, Elsevier, vol. 197(1), pages 340-348, August.
    20. repec:dau:papers:123456789/12407 is not listed on IDEAS
    21. Schlereth, Christian & Eckert, Christine & Schaaf, René & Skiera, Bernd, 2014. "Measurement of preferences with self-explicated approaches: A classification and merge of trade-off- and non-trade-off-based evaluation types," European Journal of Operational Research, Elsevier, vol. 238(1), pages 185-198.

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