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Digital
Library of the European Council for Modelling and Simulation |
Title: |
Comparative Analysis Of Metamodeling Techniques Based On An Agent-Based Supply
Chain Model |
Authors: |
Mert Edali,
Gonenc Yucel |
Published in: |
(2018). ECMS 2018
Proceedings Edited by: Lars Nolle, Alexandra
Burger, Christoph Tholen,
Jens Werner, Jens Wellhausen European Council for
Modeling and Simulation. doi:
10.7148/2018-0005 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) 32nd European Conference on Modelling and Simulation, Wilhelmshaven, Germany, May 22nd
– May 265h, 2018 |
Citation
format: |
Mert Edali, Gonenc
Yucel (2018). Comparative Analysis Of Metamodeling Techniques Based On An Agent-Based Supply
Chain Model, ECMS 2018 Proceedings Edited
by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner,
Jens Wellhausen European Council for Modeling and
Simulation. doi:
10.7148/2018-0114 |
DOI: |
https://doi.org/10.7148/2018-0114 |
Abstract: |
Agent-based models comprise interacting
autonomous entities, and generate both individual and emergent system-level
outputs. These models generally have a large set of free parameters, whose
impact on output needs to be explored. Considering also the need for
replication due to stochasticity, a proper analysis
requires a very large set of simulation runs. Therefore, obtaining a simpler
representation of a simulation model (e.g., metamodel)
can prove useful. We primarily focus on the potential utilization of various metamodeling approaches, namely Decision Trees, Random
Forests, k-Nearest Neighbor Regression, and Support Vector Regression in
predicting the two different types of outputs of an agent-based model. Results
show that system-level outputs are predicted with higher accuracy compared to
individual-level outputs under equal sample sizes. Although there is no
single metamodeling technique performing best in
all cases, we observe that support vector regression is more robust to the
increase in the dimension of the problem. |
Full
text: |