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RAB when Gains and Losses are Possible

  • Harvey J. Langholtz
  • Antoinette T. Marty
  • Christopher T. Ball
  • Eric C. Nolan

Abstract

In Chapters 1 through 4 we introduced the concept of resource-allocation and in Chapter 5 we saw how people performed in simple resource-allocation situations. We found that participants could learn to perform simple resource-allocation tasks surprisingly well, often exceeding 90% of the optimal LP solution after a few learning trials. Our data showed that participants performed best under Certainty, worst under Uncertainty, and that performance improved with learning. One interpretation of these results, and those of previous researchers (Gingrich & Soli, 1984; Busemeyer, Swenson, & Lazarte, 1986), is that people are capable resource-allocators, with an efficiency and accuracy almost as good as the optimal LP model.

Keywords

Loss Condition Coast Guard Loss Group Gain Group Gain Loss 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Harvey J. Langholtz
    • 1
  • Antoinette T. Marty
    • 1
  • Christopher T. Ball
    • 1
  • Eric C. Nolan
    • 2
  1. 1.The College of William and MaryWilliamsburgUSA
  2. 2.University of California, DavisDavisUSA

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