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RESEARCH PAPERS

Managing the Collection of Information Under Uncertainty Using Information Economics

[+] Author and Article Information
Jay M. Ling

Systems Realization Laboratory, G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405gtg004v@mail.gatech.edu

Jason Matthew Aughenbaugh

Systems Realization Laboratory, G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405gtg224k@mail.gatech.edu

Christiaan J. Paredis

Systems Realization Laboratory, G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405chris.paredis@me.gatech.edu

J. Mech. Des 128(4), 980-990 (Jan 16, 2006) (11 pages) doi:10.1115/1.2205878 History: Received September 15, 2005; Revised January 16, 2006

An important element of successful engineering design is the effective management of resources to support design decisions. Design decisions can be thought of as having two phases—a formulation phase and a solution phase. As part of the formulation phase, engineers must decide what information to collect and use to support the design decision. Since information comes at a cost, a cost-benefit tradeoff must be made. Previous work has considered such tradeoffs in cases in which all relevant probability distributions were precisely known. However, engineers frequently must characterize these distributions by gathering sample data during the information collection phase of the decision process. This characterization is crucial in high-risk design problems where uncommon events with severe consequences play a significant role in decisions. In this paper, we introduce the principles of information economics to guide decisions on information collection. We investigate how designers can bound the value of information in the case of distributions with unknown parameters by using imprecise probabilities to characterize the current state of information. We explore the basic performance, subtleties, and limitations of the approach in the context of characterizing the strength of a novel material for the design of a pressure vessel.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Calculating the value of information with known probabilities

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Figure 2

Net gain in payoff per sample

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Figure 3

Net expected payoff of the design

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Figure 4

Box plots for various sample sizes

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Figure 5

Example p-box and distributions

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Figure 6

Overview of approach using imprecise probabilities to bound the value of information

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Figure 7

Various distributions in the p-box

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Figure 8

Example high-level behavior of gross value

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Figure 9

Two example traces of gross value

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Figure 10

Actual expected net payoffs for trace A

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Figure 11

Actual expected net payoffs for trace B

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Figure 12

Actual expected net payoffs, additional trace

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