2026-09-19T09:07:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1447012022-08-30T03:14:16Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
de Weck, Olivier L.
Bortot Hopker, Ricardo
System Design and Management Program.
2022-08-29T16:05:48Z
2022-08-29T16:05:48Z
2022-05
2022-06-28T20:26:52.606Z
https://hdl.handle.net/1721.1/144701
https://orcid.org/0000-0003-2445-5774
Systems are constantly increasing complexity. Being able to quantify the system complexity and how it relates to human effort and cognition can bring numerous benefits for product development and project management. In this thesis, 25 people were part of an experiment using the travel salesperson problem, they completed 13 problems each with varying complexity. The results were summarized and through a series of statistical analysis it was found that the human effort scales super-linear with complexity in the form e = AC {superscript 1.47} + d, where A and d are constants. Additionally, based on the results in this study and previous, it is proposed an objective function for optimization of system architecture decomposition which uses the heuristics learned to reduce the human effort to understand the system.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
A Canonical Experiment on System Complexity Metric and Its Impact on Engineering Management
Thesis
application/pdf
Master
Master of Science in Engineering and Management
Publication
unknown
unknown
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<Abstract>Systems are constantly increasing complexity. Being able to quantify the system complexity and how it relates to human effort and cognition can bring numerous benefits for product development and project management. In this thesis, 25 people were part of an experiment using the travel salesperson problem, they completed 13 problems each with varying complexity. The results were summarized and through a series of statistical analysis it was found that the human effort scales super-linear with complexity in the form e = AC {superscript 1.47} + d, where A and d are constants. Additionally, based on the results in this study and previous, it is proposed an objective function for optimization of system architecture decomposition which uses the heuristics learned to reduce the human effort to understand the system.</Abstract>
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