Biased Information Passing Between Subsystems Over Time in Complex System Design
Name
md_138_01_011101.pdf
Size
1.4 MB
Format
Adobe PDF
Checksum (MD5)
b722e97b574402c2bda629695621387c
Author(s)
Yang, Maria C.
Austin-Breneman, Jesse
Yu, Bo Yang
Yang, Maria C.
Date Issued
November 2015
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Austin-Breneman, Jesse, Bo Yang Yu, and Maria C. Yang. “Biased Information Passing Between Subsystems Over Time in Complex System Design.” Journal of Mechanical Design 138, no. 1 (November 4, 2015): 011101.
Version
Final published version
Abstract
During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding "margins," is modeled in this paper with a series of optimization simulations. Three "bias" conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Ocean Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
MIT Edgerton Center
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4031745
