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Lightening the performance burden of individual‐based models through dimensional analysis and scale modeling
Author(s) -
Osgood Nathaniel
Publication year - 2009
Publication title -
system dynamics review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 57
eISSN - 1099-1727
pISSN - 0883-7066
DOI - 10.1002/sdr.417
Subject(s) - computer science , scale (ratio) , discretization , homogeneity (statistics) , limiting , scaling , population , population model , homogeneous , scale model , mathematical optimization , mathematics , machine learning , engineering , mechanical engineering , mathematical analysis , physics , geometry , demography , quantum mechanics , combinatorics , sociology , aerospace engineering
While individual‐based models are attractive for some modeling problems, the lengthy times required for simulating large populations can impose high opportunity costs by limiting model comprehension, refinement and user interaction. This paper demonstrates a novel technique for using dimensional analysis and scale modeling to reduce the performance barriers associated with individual‐based model simulation. Given a dimensionally homogeneous simulation model with a large population, we propose a rigorous, systematic and general‐purpose technique to formulate a “reduced‐scale” individual‐based model that simulates a smaller population. Outputs of the reduced‐scale models can be precisely transformed to yield results representative of a full‐scale model—without the need to run the full‐scale model. While discretization effects and heterogeneity limit the degree of scaling that can be achieved, these techniques are notable in relying only upon dimensional homogeneity of the full‐scale model, and not on the specifics of model behavior or use of a particular mathematical framework. Copyright © 2009 John Wiley & Sons, Ltd.