A survey on agent-based modelling assisted by machine learning

Agent-based models have diversified their applications across various domains due to the ease with which different phenomena can be represented and simulated. These models incorporate heterogeneous, autonomous agents, local interactions, bounded rationality, and often feature explicit spatial representations. However, certain challenges have been identified in their application, including the complexity of design, difficulty in calibrating parameters, and interpreting and analysing results. Therefore, incorporating machine learning (ML) tools in the various stages of the agent-based modelling and simulation process presents a promising approach for current and future research. The main hypothesis of this study is that integrating ML techniques and tools into agent-based modelling can help address challenges encountered during different stages of implementation, ultimately leading to more accurate and effective simulations. The methodology employed in this study involves a comprehensive search and analysis of relevant literature on the topic. This survey reviews significant developments in the integration of ML into the agent-based modelling and simulation process in recent years. The results of this study summarize the fundamental concepts of ML and its applications in agent-based modelling, and provide insights into the prospects and challenges for ML-assisted agent-based modelling in the near future.