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Title: Optimizing in a complex world: A statistician's role in decision making

Abstract

As applied statisticians increasingly participate as active members of problem-solving and decision-making teams, our role continues to evolve. Historically, we may have been seen as those who can help with data collection strategies or answer a specific question from a set of data. Nowadays, we are or strive to be more deeply involved throughout the entire problem-solving process. An emerging role is to provide a set of leading choices from which subject matter experts and managers can choose to make informed decisions. A key to success is to provide vehicles for understanding the trade-offs between candidates and interpreting the merits of each choice in the context of the decision-makers priorities. To achieve this objective, it is helpful to be able (a) to help subject matter experts identify quantitative criteria that match their priorities, (b) eliminate non-competitive choices through the use of a Pareto front, and (c) provide summary tools from which the trade-offs between alternatives can be quantitatively evaluated and discussed. A structured but flexible process for contributing to team decisions is described for situations when all choices can easily be enumerated as well as when a search algorithm to explore a vast number of potential candidates is required. Inmore » conclusion, a collection of diverse examples ranging from model selection, through multiple response optimization, and designing an experiment illustrate the approach.« less

Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Defense Programs (DP)
OSTI Identifier:
1325658
Report Number(s):
LA-UR-16-26615
Journal ID: ISSN 0898-2112
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Quality Engineering
Additional Journal Information:
Journal Name: Quality Engineering; Journal ID: ISSN 0898-2112
Publisher:
American Society for Quality Control
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS; Mathematics

Citation Formats

Anderson-Cook, Christine M. Optimizing in a complex world: A statistician's role in decision making. United States: N. p., 2016. Web. doi:10.1080/08982112.2016.1217120.
Anderson-Cook, Christine M. Optimizing in a complex world: A statistician's role in decision making. United States. doi:10.1080/08982112.2016.1217120.
Anderson-Cook, Christine M. Tue . "Optimizing in a complex world: A statistician's role in decision making". United States. doi:10.1080/08982112.2016.1217120. https://www.osti.gov/servlets/purl/1325658.
@article{osti_1325658,
title = {Optimizing in a complex world: A statistician's role in decision making},
author = {Anderson-Cook, Christine M.},
abstractNote = {As applied statisticians increasingly participate as active members of problem-solving and decision-making teams, our role continues to evolve. Historically, we may have been seen as those who can help with data collection strategies or answer a specific question from a set of data. Nowadays, we are or strive to be more deeply involved throughout the entire problem-solving process. An emerging role is to provide a set of leading choices from which subject matter experts and managers can choose to make informed decisions. A key to success is to provide vehicles for understanding the trade-offs between candidates and interpreting the merits of each choice in the context of the decision-makers priorities. To achieve this objective, it is helpful to be able (a) to help subject matter experts identify quantitative criteria that match their priorities, (b) eliminate non-competitive choices through the use of a Pareto front, and (c) provide summary tools from which the trade-offs between alternatives can be quantitatively evaluated and discussed. A structured but flexible process for contributing to team decisions is described for situations when all choices can easily be enumerated as well as when a search algorithm to explore a vast number of potential candidates is required. In conclusion, a collection of diverse examples ranging from model selection, through multiple response optimization, and designing an experiment illustrate the approach.},
doi = {10.1080/08982112.2016.1217120},
journal = {Quality Engineering},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 09 00:00:00 EDT 2016},
month = {Tue Aug 09 00:00:00 EDT 2016}
}

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