Abstract
The SimpopLocal model exposes 6 free parameters that cannot be set using empirical data. This chapter presents how to evaluate SimpopLocal in spite of these degrees of freedom. A first evaluation establishes whether the model has the capacity to produce acceptable dynamics. To achieve this evaluation, the quality of the simulated dynamics is made explicit using a quantitative analysis. Based on this quantitative evaluation, an automated calibration algorithm is designed using a state-of-the-art multi-objective genetic algorithm. The results show that the model is able to produce acceptable dynamics. A second evaluation exposes the contribution of each free parameter to the capacity of the model to produce these acceptable dynamics. A novel sensitivity analysis algorithm called calibration profile is then applied. The results of this analysis show that the model can be simplified by removing one superfluous mechanism and one superfluous parameter and that all the remaining mechanisms are mandatory in the model and all the remaining parameters can be better constrained by narrowing down their definition domains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Computed with \(\alpha = 1.36\).
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
Note that the profile algorithm iteratively refines the computed profiles from high values toward lower ones through through an iterative process, therefore the proposed bounds are more restrictive than the exact ones.
- 12.
References
Archaeomedes: Des oppida aux métropoles. Anthropos, Paris (1998)
Bairoch, P.: De Jéricho à Mexico: villes et économie dans l’histoire. Gallimard, Paris (1985)
Beaumont, M.A.: Approximate Bayesian computation in evolution and ecology. Annu. Rev. Ecol. Evol. Syst. 41(1), 379–406 (2010)
Belding, T.C.: The distributed genetic algorithm revisited. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 114–121 (1995)
Branke, J.: Creating robust solutions by means of evolutionary algorithms. Parallel Problem Solving from Nature — PPSN V: 5th International Conference Amsterdam, The Netherlands September 27–30, 1998 Proceedings, pp. 119–128. Springer, Berlin (1998)
Chérel, G., Cottineau, C., Reuillon, R.: Beyond corroboration: strengthening model validation by looking for unexpected patterns. PLoS ONE 10(9), 1–28 (2015)
Christaller, W.: Die Zentralen Orte in Süddeutschland. Fischer, Jena (1933)
Clune, J., Mouret, J.-B., Lipson, H.: The evolutionary origins of modularity. Proc. R. Soc. Lond. B: Biol. Sci. 280(1755), 20122863 (2013)
Cottineau, C., Chapron, P., Reuillon, R.: Growing models from the bottom up. An evaluation-based incremental modelling method (EBIMM) applied to the simulation of systems of cities. J. Artif. Soc. Soc. Simul. (JASSS), 18(4), 9. (2015) doi:10.18564/jasss.2828. http://jasss.soc.surrey.ac.uk/18/4/9.html
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lect. Notes Comput. Sci. 1917, 849–858 (2000)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, p. 412. Addison-Wesley Press, Reading (1989)
Johnson, G.: Aspects of regional analysis in archaeology. Annu. Rev. Anthr. 6, 479–508 (1977)
Kleijnen, J.: Design and Analysis of Simulation Experiments. Springer Publishing Company, Incorporated, New York (2007)
Lenormand, M., Jabot, F., Deffuant, G.: Adaptive approximate Bayesian computation for complex models (2012)
Liu, L.: Settlement patterns, chiefdom variability, and the development of early states in north China. J. Anthropol. Archaeol. 15(3), 237–288 (1996)
Marcus, J., Sabloff, J.A.: The Ancient City: New Perspectives on Urbanism in the Old and New World. Resident Schoolar Book, vol. 2005. School for Advanced Research, Santa Fe (2008)
Mouret, J.-B.: An algorithm to create phenotype-fitness maps. In: Proceedings of the Thirteenth International Conference on the Simulation and Synthesis of Living Systems (ALIFE 13), pp. 593–594 (2013)
Reuillon, R., Schmitt, C., Aldama, R.D., Mouret, J.: A new method to evaluate simulation models: the calibration profile (cp) algorithm. J. Artif. Soc. Soc. Simul. 18(1), 12 (2015)
Rudolph, G.: A partial order approach to noisy fitness functions. In: Congress on Evolutionary Computation, Seoul, Korea, pp. 318–325. Press (2001)
Sanders, L.: Regards croisés de géographes, économistes et archéologues sur la hiérarchie des systémes de peuplement: de l’empirie aux systémes complexes. Région et Développement, 36, (2012)
Sano, Y., Kita, H.: Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC’02, vol. 1, pp. 360–365 (2002)
Schmitt, C.: Modélisation de la dynamique des systémes de peuplement: de SimpopLocal à SimpopNet. Ph.D. thesis, Université Paris 1 Panthéon-Sorbonne (2014)
Stonedahl, F.J.: Genetic algorithms for the exploration of parameter spaces in agent-based models. Ph.D. thesis, Northwestern University of Illinois (2011)
Tanooka, K., Tamaki, H., Abe, S., Kitamura, S.: A continuous age model of genetic algorithms applicable to optimization problems with uncertainties. In: IEEE International Conference on Systems, Man, and Cybernetics. IEEE SMC’99 Conference Proceedings, vol. 1, pp. 637–642 (1999)
Weise, T.: Global Optimization Algorithms - Theory and Application, 3rd edn. (2011). www.it-weise.de
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Pumain, D., Reuillon, R. (2017). Evaluation of the SimpopLocal Model. In: Urban Dynamics and Simulation Models. Lecture Notes in Morphogenesis. Springer, Cham. https://doi.org/10.1007/978-3-319-46497-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-46497-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46495-4
Online ISBN: 978-3-319-46497-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)