When collecting data to select an alternative from a finite set of alternatives that are described by multiple attributes, one must allocate effort to activities that provide information about the value of each attribute. This is a particularly relevant problem when the attribute values are estimated using experimental data. This paper discusses the problem of allocating an experimental budget amongst two attributes when the non-dominated decision alternatives form a concave efficient frontier. The results of a simulation study suggested allocation rules that take advantage of knowledge of the decision model and, when available, knowledge about the general shape of the frontier. These rules were compared to a default rule that equally allocated the experimental budget across the attributes. A proportional rule that allocated samples based on the value function weights performed well only in some cases; a more sophisticated step rule increased the frequency of correct selection across all weights.
Proceedings Title: Proceedings of the 2013 Winter Simulation Conference
Conference Dates: December 8-11, 2013
Conference Location: Washington, DC, -1
Pub Type: Conferences
Proceedings of the 2013 Winter Simulation Conference Decision Analysis, Sample Allocation, Experiment Design, Attribute Value Uncertainty.