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Quantifying Structural Complexity, Effort, and Performance: An Early Experiment Using Network Design Tasks
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DOI：10.1002/sys.70027.png)
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Abstract
En 中文
Structural Complexity is perceived as driving cost in system development, yet managing it effectively requires empirical understanding. This study investigates human decision-making using a toy transportation-style network design task, focusing on how Structural Complexity, Effort, and Performance interact. Seventy-four participants (primarily systems engineers) worked in small groups to design simple transportation-style networks, drawing 188 designs across 20 node sets (each representing a distinct spatial configuration). This was a trial of a new type of experimental setup aiming to empirically test hypotheses in Systems Engineering, and though it is broadly promising, the design needs significant iteration for better controls. Such iteration could greatly help SE theory.Four hypotheses grounded in the proposed Conservation of Complexity law were explored, with early results showing that additional Effort was generally associated with modest improvements in Performance; Performance rises with Structural Complexity, but sees diminishing returns; additional Effort did not reliably reduce Structural Complexity at fixed Performance; and the Effort-Complexity relationship followed a sub-linear trend.In parallel, we outline a practical, repeatable quantification method for structural complexity to support a more robust and comparable measurement in human-in-the-loop experiments. Future experimental designs with standardized constraints, behavioral controls, and normalized performance metrics will be necessary to generalize these insights.
Keywords:
conservation of complexity
structural complexity
systems engineering
transportation networks
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