Biased Information Passing Between Subsystems Over Time in Complex System Design
The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal system-level results are...
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| Format: | Article |
| Language: | en_US |
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American society of Mechanical Engineers
2017
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| Online Access: | http://hdl.handle.net/1721.1/108563 https://orcid.org/0000-0001-7891-1187 https://orcid.org/0000-0002-7776-3423 |
| _version_ | 1811088784791437312 |
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| author | Austin-Breneman, Jesse Yu, Bo Yang Yang, Maria |
| author2 | MIT Institute for Data, Systems, and Society |
| author_facet | MIT Institute for Data, Systems, and Society Austin-Breneman, Jesse Yu, Bo Yang Yang, Maria |
| author_sort | Austin-Breneman, Jesse |
| collection | MIT |
| description | The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal system-level results are often reached due to factors such as satisficing, ill-defined problems or other project constraints. Twelve sub-system and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate sub-systems. Responses showed sub-system team members often presented conservative, worst-case scenarios to other sub-systems when negotiating a trade-off as a way of hedging their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled 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 to reach 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. |
| first_indexed | 2024-09-23T14:07:30Z |
| format | Article |
| id | mit-1721.1/108563 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:07:30Z |
| publishDate | 2017 |
| publisher | American society of Mechanical Engineers |
| record_format | dspace |
| spelling | mit-1721.1/1085632022-10-01T19:22:33Z Biased Information Passing Between Subsystems Over Time in Complex System Design Austin-Breneman, Jesse Yu, Bo Yang Yang, Maria MIT Institute for Data, Systems, and Society Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Engineering Systems Division Austin-Breneman, Jesse Yu, Bo Yang Yang, Maria The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal system-level results are often reached due to factors such as satisficing, ill-defined problems or other project constraints. Twelve sub-system and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate sub-systems. Responses showed sub-system team members often presented conservative, worst-case scenarios to other sub-systems when negotiating a trade-off as a way of hedging their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled 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 to reach 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. National Science Foundation (U.S.). Graduate Research Fellowship Program 2017-05-02T13:14:33Z 2017-05-02T13:14:33Z 2014-08 Article http://purl.org/eprint/type/ConferencePaper 978-0-7918-4640-7 http://hdl.handle.net/1721.1/108563 Austin-Breneman, Jesse, Bo Yang Yu, and Maria C. Yang. “Biased Information Passing Between Subsystems Over Time in Complex System Design.” Volume 7: 2nd Biennial International Conference on Dynamics for Design; 26th International Conference on Design Theory and Methodology (August 17, 2014). https://orcid.org/0000-0001-7891-1187 https://orcid.org/0000-0002-7776-3423 en_US http://dx.doi.org/10.1115/DETC2014-34433 Volume 7: 2nd Biennial International Conference on Dynamics for Design; 26th International Conference on Design Theory and Methodology 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. application/pdf American society of Mechanical Engineers American Society of Mechanical Engineers (ASME) |
| spellingShingle | Austin-Breneman, Jesse Yu, Bo Yang Yang, Maria Biased Information Passing Between Subsystems Over Time in Complex System Design |
| title | Biased Information Passing Between Subsystems Over Time in Complex System Design |
| title_full | Biased Information Passing Between Subsystems Over Time in Complex System Design |
| title_fullStr | Biased Information Passing Between Subsystems Over Time in Complex System Design |
| title_full_unstemmed | Biased Information Passing Between Subsystems Over Time in Complex System Design |
| title_short | Biased Information Passing Between Subsystems Over Time in Complex System Design |
| title_sort | biased information passing between subsystems over time in complex system design |
| url | http://hdl.handle.net/1721.1/108563 https://orcid.org/0000-0001-7891-1187 https://orcid.org/0000-0002-7776-3423 |
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