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A knowledge-based model of context-dependent attribute preferences for fast moving consumer goods

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  • Moss, S.
  • Edmonds, B.

Abstract

Modelling techniques which integrate statistical and knowledge-based methods are shown in this paper to provide the basis for a richer discourse about the nature of markets than either statistical methods or verbal analyses alone. This result is demonstrated in relation to a model in which consumer preferences are dependent upon the activity for which purchases are made. This knowledge-base of the model incorporates qualitative judgements of domain experts. The model is then tested against EPOS data on the sales to test the consistency of these judgements. We then exhibit an application of this model to the UK market for certain alcoholic beverages. We show that the context-dependent model is at least as robust as models obtained by conventional statistical analysis. The context-dependent model needs far less data for effective parameterisation and contains more information that is directly useful to domain experts. However, the statistical model is less computationally intensive.

Suggested Citation

  • Moss, S. & Edmonds, B., 1997. "A knowledge-based model of context-dependent attribute preferences for fast moving consumer goods," Omega, Elsevier, vol. 25(2), pages 155-169, April.
    • Handle: RePEc:eee:jomega:v:25:y:1997:i:2:p:155-169
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    Cited by:

    1. Scott Moss, 1998. "Critical Incident Management: an Empirically Derived Computational Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(4), pages 1-1.

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