Abstract
In this paper, we give a succinct introduction to some basic concepts imported from the fields of Machine and Statistical Learning that can be useful in the analysis of complex agent-based models (ABM). The paper presents some guidelines in the design of experiments. It then focuses on considering an ABM simulation as a computational experiment relating parameters with a response variable of interest, i.e. a statistic obtained from the simulation. This perspective gives the opportunity of using a supervised learning algorithm to fit the response with the parameters. The fitted model can be used to better interpret and understand the relation between the parameters of the ABM and the results in the simulation.
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Altmann, A., Tolosi, L., Sander, O., & Lengauer, T. (2010). Permutation importance: A corrected feature importance measure. Bioinformatics, 26(10), 1340–1347. doi:10.1093/bioinformatics/btq134
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324
Chang, M. H. (2011). Agent-based modeling and computational experiments in industrial organization: Growing firms and industries in silico. Eastern Economic Journal, 37(1), 28.
Criminisi, A., Shotton, J., & Konukoglu, E. (2011). Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Computer Vision, 7(2–3), 81–227. doi:10.1561/0600000035
Galán, J. M., Izquierdo, L. R., Izquierdo, S. S., Santos, J. I., del Olmo, R., López-Paredes, A., & Edmonds, B. (2009). Errors and artefacts in agent-based modelling. Journal of Artificial Societies and Social Simulation, 12(1), 1. http://jasss.soc.surrey.ac.uk/12/1/1.html
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York, NY: Springer.
Hernández, C., Galán, J. M., López-Paredes, A., & del Olmo, R. (2014). Economía Artificial: Métodos de inspiración social en la resolución de problemas complejos. Revista Española de Física, 28(3), 23–30.
Izquierdo, L. R., Izquierdo, S. S., Galán, J. M., & Santos, J. I. (2009). Techniques to understand computer simulations: Markov chain analysis. Journal of Artificial Societies and Social Simulation, 12(1), 6. http://jasss.soc.surrey.ac.uk/12/1/6.html
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. New York: Springer.
Lee, J. S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, G., Sun, Z., & Parker, D. C. (2015). The complexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation 18, 4. http://jasss.soc.surrey.ac.uk/18/4/4.html
Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–166.
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245. doi:10.2307/1268522
Santos, J. I., Pereda, M., Zurro, D., Álvarez, M., Caro, J., Galán, J. M., & Briz i Godino, I. (2015). Effect of resource spatial correlation and Hunter-Fisher-Gatherer mobility on social cooperation in Tierra del Fuego. PLoS ONE, 10(4), e0121888. doi:10.1371/journal.pone.0121888
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307. doi:10.1186/1471-2105-9-307
Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(1), 25. doi:10.1186/1471-2105-8-25
Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7(1), 91. doi:10.1186/1471-2105-7-91
Viana, F. A. C. (2013). Things you wanted to know about the Latin hypercube design and were afraid to ask. In 10th World Congress on Structural and Multidisciplinary Optimization, Orlando, Florida, USA.
Wei, P., Lu, Z., & Song, J. (2015). Variable importance analysis: A comprehensive review. Reliability Engineering and System Safety, 142, 399–432. doi:10.1016/j.ress.2015.05.018
Acknowledgements
The authors would like to thank Dr. L.R. Izquierdo for some advice and comments on this paper. The authors acknowledge support from the Spanish MICINN Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and by the Junta de Castilla y León GREX251-2009.
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Pereda, M., Santos, J.I., Galán, J.M. (2017). A Brief Introduction to the Use of Machine Learning Techniques in the Analysis of Agent-Based Models. In: Hernández, C. (eds) Advances in Management Engineering. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-55889-9_11
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