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    Managing the Collection of Information Under Uncertainty Using Information Economics

    Source: Journal of Mechanical Design:;2006:;volume( 128 ):;issue: 004::page 980
    Author:
    Jay M. Ling
    ,
    Jason Matthew Aughenbaugh
    ,
    Christiaan J. Paredis
    DOI: 10.1115/1.2205878
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: 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.
    keyword(s): Design , Economics AND Probability ,
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      Managing the Collection of Information Under Uncertainty Using Information Economics

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    https://yetl.yabesh.ir/yetl1/handle/yetl/134317
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    contributor authorJay M. Ling
    contributor authorJason Matthew Aughenbaugh
    contributor authorChristiaan J. Paredis
    date accessioned2017-05-09T00:20:59Z
    date available2017-05-09T00:20:59Z
    date copyrightJuly, 2006
    date issued2006
    identifier issn1050-0472
    identifier otherJMDEDB-27829#980_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/134317
    description abstractAn 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleManaging the Collection of Information Under Uncertainty Using Information Economics
    typeJournal Paper
    journal volume128
    journal issue4
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.2205878
    journal fristpage980
    journal lastpage990
    identifier eissn1528-9001
    keywordsDesign
    keywordsEconomics AND Probability
    treeJournal of Mechanical Design:;2006:;volume( 128 ):;issue: 004
    contenttypeFulltext
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