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Title: Influence Metrics for Value and Utility Functions.

Abstract

Methods are proposed to measure the sensitivity of utility or value function to the variations of attribute values for Multi-Criteria Decision Analyses that are based on functions that cannot be expressed as a weighted sum of the attribute values. These measures, called factor Influence Metrics, can be used to examine the characteristics of the option scoring algorithm and help verify the algorithm is consistent with decision makers value structure and processes. ACKNOWLEDGEMENTS This work is based on ideas developed with a team that included Sean Derosa (Sandia), Megan Keeling (ZRA), Trisha Miller (Sandia), Dustin Ward-Dahl (ZRA), and Lynn Yang (Sandia). The authors wish to thank Gregory Wyss (Sandia), Jason Reinhardt (Sandia) and John Lathrop (Decision Strategies LLC) for reviews and comments on drafts of this paper. We also wish to thank Noel Nachtigal and Rossitza Homan for their support and encouragement for this effort.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
Department of Homeland Security
OSTI Identifier:
1561156
Report Number(s):
SAND2019-10404
679078
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Imbro, Dennis R., and Pless, Daniel J. Influence Metrics for Value and Utility Functions.. United States: N. p., 2019. Web. doi:10.2172/1561156.
Imbro, Dennis R., & Pless, Daniel J. Influence Metrics for Value and Utility Functions.. United States. doi:10.2172/1561156.
Imbro, Dennis R., and Pless, Daniel J. Sun . "Influence Metrics for Value and Utility Functions.". United States. doi:10.2172/1561156. https://www.osti.gov/servlets/purl/1561156.
@article{osti_1561156,
title = {Influence Metrics for Value and Utility Functions.},
author = {Imbro, Dennis R. and Pless, Daniel J.},
abstractNote = {Methods are proposed to measure the sensitivity of utility or value function to the variations of attribute values for Multi-Criteria Decision Analyses that are based on functions that cannot be expressed as a weighted sum of the attribute values. These measures, called factor Influence Metrics, can be used to examine the characteristics of the option scoring algorithm and help verify the algorithm is consistent with decision makers value structure and processes. ACKNOWLEDGEMENTS This work is based on ideas developed with a team that included Sean Derosa (Sandia), Megan Keeling (ZRA), Trisha Miller (Sandia), Dustin Ward-Dahl (ZRA), and Lynn Yang (Sandia). The authors wish to thank Gregory Wyss (Sandia), Jason Reinhardt (Sandia) and John Lathrop (Decision Strategies LLC) for reviews and comments on drafts of this paper. We also wish to thank Noel Nachtigal and Rossitza Homan for their support and encouragement for this effort.},
doi = {10.2172/1561156},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}