Sequential Sample Allocation for Multiple Attribute Selection Decisions

نویسندگان

  • Dennis D. Leber
  • Jeffrey W. Herrmann
چکیده

When faced with a limited budget to collect data in support of a multiple attribute selection decision, the decisionmaker must decide how many samples to observe from each alternative and attribute. This allocation decision is of particular importance when the observation process is uncertain, such as with physical measurements. For example, when the U.S. Department of Homeland Security must decide upon a radiation detection system to acquire, a number of performance attributes are of interest and must be measured in order to characterize each of the considered systems. We developed and tested a sequential allocation scheme that uses Bayesian updating and maximizes the probability of selecting the true best alternative when the attribute value observations contain Gaussian measurement error. In this sequential approach, measurements are conducted one at a time. Prior to making a measurement the decision-maker’s current knowledge of the attribute values is used to identify the attribute and alternative pair to sample next. We conducted a simulation study to compare the performance of the proposed sequential allocation scheme and a commonly used uniform allocation approach.

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تاریخ انتشار 2016