APPLYING INFORMATION ECONOMICS AND IMPRECISE PROBABILITIES TO DATA COLLECTION IN DESIGN

One important aspect of the engineering design process is the sequence of design decisions, each consisting of a formulation phase and a solution phase. As part of the decision formulation, engineers must decide what information to use to support the decision. Since information comes at a cost, a cost-benefit trade-off must be made. Previous work has considered these trade-offs in cases in which all relevant probability distributions were precisely known. However, engineers frequently must estimate these distributions by gathering sample data during the information collection phase of the decision process. In this paper, we introduce principles of information economics to guide decisions on information collection. We present a method that enables designers to bound the value of information in the case of unknown distributions by using imprecise probabilities to characterize the current state of information. We illustrate this method with an example material strength characterization for a pressure vessel design problem, in which we explore the basic performance, subtleties, and limitations of the method.Copyright © 2005 by ASME

[1]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[2]  Ram D. Sriram,et al.  The Role of Knowledge in Next-generation Product Development Systems , 2001, J. Comput. Inf. Sci. Eng..

[3]  Matthias C. M. Troffaes,et al.  Decision Making with Imprecise Probabilities: A Short Review , 2004 .

[4]  Thomas Müller-Bohn,et al.  Cost-Benefit Analysis , 2015 .

[5]  Jacob Marschak,et al.  Economic information, decision, and prediction : selected essays , 1974 .

[6]  Wolfgang Beitz,et al.  Engineering Design: A Systematic Approach , 1984 .

[7]  Scott Ferson,et al.  Probability bounds analysis , 1998 .

[8]  Donald W. King Information economics and policy in the United States: M.R. Rubin, Libraries Unlimited, Inc., Littleton, Colorado (1983). xiv + 340 pp., $35.00 U.S., $42.00 elsewhere. ISBN 0-87287-378-1. , 1985 .

[9]  William Et.Al Hines,et al.  Probability and Statistics in Engineering , 2003 .

[10]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[11]  David B. Lawrence The Economic Value of Information , 1999 .

[12]  L. J. Savage,et al.  The Foundation of Statistics , 1956 .

[13]  M Cliff Joslyn and Jane Booker,et al.  Generalized Information Theory for Engineering Modeling and Simulation , 2004 .

[14]  P. Walley Statistical Reasoning with Imprecise Probabilities , 1990 .

[15]  Scott Ferson,et al.  Probability Bounds Analysis Solves the Problem of Incomplete Specification in Probabilistic Risk and Safety Assessments , 2001 .

[16]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[17]  Bipin Chadha,et al.  The business community adopted process based metho gies in the early 1990 ’ s through the work of , 2001 .

[18]  Jacob Marschak,et al.  Economic Information, Decision, and Prediction , 1974 .

[19]  G. Hazelrigg Systems Engineering: An Approach to Information-Based Design , 1996 .

[20]  R. Layard,et al.  Cost-benefit analysis; second edition , 1994 .

[21]  Marc Lieberman,et al.  Introduction to Economics , 1999 .

[22]  Judy M. Vance,et al.  Assessment of VR Technology and its Applications to Engineering Problems , 2001, J. Comput. Inf. Sci. Eng..

[23]  R Dransfield Introduction to economics , 2007 .

[24]  B. D. Finetti,et al.  Foresight: Its Logical Laws, Its Subjective Sources , 1992 .

[25]  Amy Sundermier,et al.  Interconnection of Distributed Components: An Overview of Current Middleware Solutions , 2001, J. Comput. Inf. Sci. Eng..

[26]  Scott Ferson,et al.  Probability bounds analysis in environmental risk assessments , 2003 .

[27]  Michael Grüninger,et al.  Ontologies for Integrating Engineering Applications , 2001, J. Comput. Inf. Sci. Eng..

[28]  Kenneth J. Arrow,et al.  Studies in Resource Allocation Processes: Appendix: An optimality criterion for decision-making under ignorance , 1977 .

[29]  George A. Hazelrigg,et al.  A Framework for Decision-Based Engineering Design , 1998 .

[30]  Alice M. Agogino,et al.  An Intelligent Real Time Design Methodology for Component Selection: An Approach to Managing Uncertainty , 1994 .

[31]  Robert L. Winkler,et al.  Uncertainty in probabilistic risk assessment , 1996 .

[32]  Scott Ferson,et al.  Constructor:synthesizing information about uncertain variables. , 2005 .

[33]  Christiaan J. J. Paredis,et al.  The Value of Using Imprecise Probabilities in Engineering Design , 2006 .

[34]  Daniel A. McAdams,et al.  A Methodology for Model Selection in Engineering Design , 2005 .

[35]  K. Vind A foundation for statistics , 2003 .

[36]  Satyandra K. Gupta,et al.  Estimating the Optimal Number of Alternatives to Be Explored in Large Design Spaces: A Step Towards Incorporating Decision Making Cost in Design Decision Models , 2002 .