Calibrating Agent-Based Models Using a Genetic Algorithm

Abstract

We present a Genetic Algorithm (GA)-based tool that calibrates Agent-based Models (ABMs). The GA searches through a user-defined set of input parameters of an ABM, delivering values for those parameters so that the output time series of an ABM may match the real system's time series to certain precision. Once that set of possible values has been available, then a domain expert can select among them, the ones that better make sense from a practical point of view and match the explanation of the phenomenon under study. In developing the GA, we have had three main goals in mind. First, the GA should be easily used by non-expert computer users and allow the seamless integration of the GA with different ABMs. Secondly, the GA should achieve a relatively short convergence time, so that it may be practical to apply it to many situations, even if the corresponding ABMs exhibit complex dynamics. Thirdly, the GA should use a few data points of the real system's time series and even so, achieve a sufficiently good match with the ABM's time series to attaining relational equivalence between the real system under study and the ABM that models it. That feature is important since social science longitudinal studies commonly use few data points. The results show that all of those goals have been accomplished.

Agent-Based Simulations Using Genetic Algorithm Calibration: A Children’s Services Application

“…The points demonstrated by Heppenstall et al, can also be seen in others attempting the same approach [33]- [35]. With this in mind, these limitations will be considered in this report, in particular when designing how this approach is implemented.…”

“…In order to examine and evaluate the potential for the proposed approach, an experimental methodology was used where a sample of LAs were selected and used; as per existing literature approaches [25], [33]- [35], [37], [40]. Each LA had an ABM calibrated using a multi objective fitness function.…”

“…This approach can be seen to be used in optimising other ABM based simulations in other domains. [33]- [37] Furthermore, using such an optimisation method may be advantageous route that can help in creating additional value from the sparse public data already available, as the values approximated for each LA can serve as new data points that can be further analysed for insight. Machine Learning regression techniques were also observed when optimising an ABM [38], [39], this however represents a small and emerging section of the literature.…”