Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Allocating Attribute-Specific Information-Gathering Resources to Improve Selection Decisions

Published

Author(s)

Dennis D. Leber, Jeffery W. Herrmann

Abstract

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

Keywords

Proceedings of the 2013 Winter Simulation Conference Decision Analysis, Sample Allocation, Experiment Design, Attribute Value Uncertainty.

Citation

Leber, D. and Herrmann, J. (2013), Allocating Attribute-Specific Information-Gathering Resources to Improve Selection Decisions, Proceedings of the 2013 Winter Simulation Conference, Washington, DC, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=913907 (Accessed February 26, 2024)
Created December 8, 2013, Updated January 27, 2020