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On Post-Evaluation Analysis: Candle-Lighting and Surrogate Models

Author

Listed:
  • Steven O. Kimbrough

    (University of Pennsylvania, Department of Decision Sciences/6366, Philadelphia, Pennsylvania 19104-6366)

  • Jim R. Oliver

    (University of Pennsylvania, Department of Decision Sciences/6366, Philadelphia, Pennsylvania 19104-6366)

  • Clark W. Pritchett

    (US Coast Guard R&D Center, Avery Point, Groton, Connecticut 06340-6096)

Abstract

Gleaning information from a model to guide the design of new and better options is an important, underemphasized facet of post-evaluation analysis. We call this facet candle-lighting analysis . We structure this analysis as a series of questions which we incorporate into a DSS that uses standard mathematical and artificial intelligence techniques. Creating new options typically requires action by an organization. The DSS stores models of these actions, their cost and benefits, and information about the source and accuracy of parameters. We applied candle-lighting analysis to a performance evaluation model developed with and for the US Coast Guard that is part of a prototype DSS.

Suggested Citation

  • Steven O. Kimbrough & Jim R. Oliver & Clark W. Pritchett, 1993. "On Post-Evaluation Analysis: Candle-Lighting and Surrogate Models," Interfaces, INFORMS, vol. 23(3), pages 17-28, June.
    • Handle: RePEc:inm:orinte:v:23:y:1993:i:3:p:17-28
    DOI: 10.1287/inte.23.3.17
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    Cited by:

    1. Steiger, David M. & Sharda, Ramesh, 1996. "Analyzing mathematical models with inductive learning networks," European Journal of Operational Research, Elsevier, vol. 93(2), pages 387-401, September.

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