Model Structure Analysis through Graph Theory:
Partition Heuristics and Feedback Structure Decomposition

Rogelio Oliva
Mays Business School
Texas A&M University, 4217
College Station, TX 77843
Phone 979-862-3744; Fax 979-845-5653
roliva@tamu.edu

Oliva R. 2004.
Model Structure Analysis through Graph Theory: Partition Heuristics and Feedback Structure Decomposition.
System Dynamics Review 20(4):313-336.


Abstract
The argument of this paper is that it is possible to focus on the structural complexity of system dynamics models to design a partition strategy that maximizes the test points between the model and the real world, and a calibration sequence that permits an incremental development of model confidence. It further argues that graph theory could be used as a basis for making sense of the structural complexity of system dynamics models, and that this structure could be used as a basis for more formal analysis of dynamic complexity. After reviewing the graph representation of system structure, the paper presents the rationale and algorithms for model partitions based on data availability and structural characteristics. Special attention is given the decomposition of cycle partitions that contain all the model's feedback loops, and a unique and granular representation of feedback complexity is derived. The paper concludes by identifying future research avenues in this arena. (Analysis of model structure; Graph theory; Structural complexity; Partial model calibration; Independent loop set)

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Last updated September 27, 2017.