Archives par mot-clé : Genetic algorithm

Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns

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diversityNew Paper, in PLoS ONE

Chérel G., Cottineau C., Reuillon R., 2015, « Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns », PLoS ONE 10(9): e0138212. doi:10.1371/journal.pone.0138212

Abstract. Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic.

Key-words. Simulation and Modelling, Evolutionary Algorithm, Population Growth, Space Exploration, Urbanisation, Complex Systems.

A New Method to Evaluate Simulation Models: The Calibration Profile (CP) Algorithm

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New publication, in JASSS : Journal of Artificial Societies and Social Simulation

Reuillon R., Schmitt C., De Aldama R., Mouret J.-B., 2015, « A New Method to Evaluate Simulation Models: The Calibration Profile (CP) Algorithm », JASSS : Journal of Artificial Societies and Social Simulation, Vol. 18, Issue 1, http://jasss.soc.surrey.ac.uk/18/1/12.html

Abstract. Models of social systems generally contain free parameters that cannot be evaluated directly from data. A calibration phase is therefore necessary to assess the capacity of the model to produce the expected dynamics. However, despite the high computational cost of this calibration it doesn’t produce a global picture of the relationship between the parameter space and the behaviour space of the model. The Calibration Profile (CP) algorithm is an innovative method extending the concept of automated calibration processes. It computes a profile that depicts the effect of each single parameter on the model behaviour, independently from the others. A 2-dimensional graph is thus produced exposing the impact of the parameter under study on the capacity of the model to produce expected dynamics. The first part of this paper is devoted to the formal description of the CP algorithm. In the second part,we apply it to an agent based geographical model (SimpopLocal). The analysis of the results brings to light novel insights on the model.

Key-words. Calibration Profile, Model Evaluation

Multi-agent modeling of urban growth distribution in systems of cities

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A strong regularity in urban systems has long been identified : the hierarchical distribution of city sizes. Moreover, a closer observation of the evolution of this distribution shows that in the majority of city systems, there is a trend towards a more and more unequal distribution of city sizes. Why does the majority of urban systems show those strong regularities? What are the common growth processes involved? Several dynamic growth models have been proposed but no consensus has yet been reached because of the under-determination of models by those empirical laws. In this presentation we describe a new method of agent-based parsimonious modeling that we think can contribute to the identification of the common urban growth processes. This modeling method is based  on  intensive model exploration for quantitative evaluation of implemented mechanisms. The exploration tools were first developed for the evaluation of SimpopLocal, a model of the organization of urban systems when cities first emerged. The use of those exploration tools was then generalized into a modeling method that was applied for the first time with the construction of the MARIUS family of models which aims at reproducing the evolution of Soviet urbanisation between 1959 and 1989. Those two examples show how this new modeling method can help the construction of urban theories by helping the evaluation of assumptions made on urban processes.

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Communication at the seminar Quanturb, ISC-PIF (Paris), November 19th.

Clara Schmitt and Paul Chapron

100 years of computation

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It’s what it takes to calibrate the SimpopLocal model, that simulates the dynamical hierarchical and spatial organization of settlements at the time when cities emerged, a few thousand years after the emergence of agriculture. Even if this model has been built using a few simple mechanisms, 7 parameters have no known empirical value. To find suitable values for those parameters, an automated calibration algorithm has been designed. In doing so, three quantitative goals have been defined in order to measure the quality of the output of a simulation, hence the quality of a set of parameters. One goal targets the shape of the distribution of the size of settlements, another one the size of the biggest settlement of the distribution and a last one the length of the simulation, or number of iterations, required to achieve those goals. These criteria are evaluated by computing 30 replications (independent execution) of the model (due to its stochasticity). Using the OpenMOLE framework (www.openmole.org) a genetic algorithm (a global optimization algorithm) has been distributed on the European grid EGI, federating computing power all over the world. After running about 10 million model executions, which would take more than 100 years of computation on a bleeding edge computer, the algorithm has finally converged after one week of computation and found suitable sets of parameters for the model calibration. The modelers have validated them and are now taking benefit from the calibrated model to better understand the implications of the mechanisms chosen to simulate a stylized emergence of urbanism.

Romain Reuillon, Sébastien Rey-Coyrehourcq and Clara Schmitt