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Optimal strategy for time-limited sequential search.

V V Krishnan1

  • 1School of Engineering, San Francisco State University,1600 Holloway Ave., San Francisco, CA 94132, USA. krishnan@sfsu.edu

Computers in Biology and Medicine
|June 6, 2006
PubMed
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This study presents an optimal strategy for maximizing benefits in time-limited searches with rare candidates. Higher-benefit candidates generally dictate the best search expansion times.

Area of Science:

  • Operations Research
  • Decision Science
  • Applied Mathematics

Background:

  • Sequential search processes often face challenges with low candidate encounter rates.
  • Maximizing expected benefit under time constraints is crucial in various search applications.
  • Prior knowledge of candidate types, benefits, and encounter rates is often available.

Purpose of the Study:

  • To develop an optimal strategy for time-limited sequential search with low encounter rates.
  • To model search processes involving multiple candidate types with known characteristics.
  • To determine optimal times for expanding the acceptable candidate pool during a search.

Main Methods:

  • Formulation of a mathematical model for sequential search.
  • Analysis of candidate types, their benefits, and encounter rates.

Related Experiment Videos

  • Development of a strategy based on dynamic pool expansion.

    • Main Results:

    • An optimal strategy was developed for maximizing expected benefit.
    • The strategy involves strategically expanding the candidate pool over time.
    • Higher-benefit candidate types were found to generally dominate the optimal strategy.

    Conclusions:

    • The developed strategy effectively maximizes expected benefits in challenging search scenarios.
    • Dynamic expansion of the candidate pool is key to optimizing search efficiency.
    • Benefit-driven decision-making is central to effective sequential search strategies.