A Regression-Based Calibration Method for Agent-Based Models

Because of their complexity, taking agent-based models to the data is still an unresolved issue. In this paper we propose a method to calibrate the model parameters on real data that is based on a novel global sensitivity analysis procedure. The innovative feature of this procedure is that it allows to estimate regression meta-models for the relationship between model parameters and model output without resorting to Monte Carlo simulations to eliminate the effect of randomness. This is achieved by sampling at the same time both the parameters and the seed of the random numbers generator in a random fashion. If correctly specified, the meta-models can be directly used to consistently estimate the average response of the ABM to any parameter vector input by the modeler and, as a consequence, also the distance between real and simulated data. The advantage of the proposed method is twofold: it is very parsimonious in terms of computational time and is relatively easy to implement, being it based on elementary econometric techniques.