Categorical surrogation of agent-based models: A comparative study of machine learning classifiers

Abstract

Agent-based modelling has gained recognition in the last years because it provides a natural way to explore the behaviour of social systems. However, agent-based models usually have a considerable number of parameters that make it computationally prohibitive to explore the complete space of parameter combinations. A promising approach to overcome the computational constraints of agent-based models is the use of machine learning-based surrogates or metamodels, which can be used as efficient proxies of the original agent-based model. As the use of metamodels of agent-based simulations is still an incipient area of research, there are no guidelines on which algorithms are the most suitable candidates. In order to contribute to filling this gap, we conduct here a systematic comparative analysis to evaluate different machine learning-based approaches to agent-based model surrogation. A key innovation of our work is the focus on classification methods for categorical metamodeling, which is highly relevant because agent-based simulations are very often validated in a qualitative way. To analyse the performance of the classifiers we use three types of indicators—measures of correctness, efficiency, and robustness—and compare their results for different datasets and sample sizes using an agent-based artificial market as a case study.