Agent-based modeling (ABM) involves developing models in which agents make adaptive
decisions in a changing environment. Machine-learning (ML) based inference models can …

This paper presents a bibliometric analysis within the research domain dedicated to the
utilization of agent-based modeling (ABM) in the field of transportation. By employing …

The recycling of secondary resources is complicated as consumers, recyclers and
governments are all involved in this process. In developing countries, compared to legal …

Computational experiments have emerged as a valuable method for studying complex
systems, involving the algorithmization of counterfactuals. However, accurately representing …

Airport terminals are complex sociotechnical systems, in which humans interact with diverse
technical systems. A natural way to represent them is through agent-based modeling …

Abstract Machine learning is commonly used to predict and implement pattern recognition
and the relationship between variables. Causal machine learning combines approaches for …

Agent-based modeling (ABM) has become popular since it allows a direct representation of
heterogeneous individual entities, their decisions, and their interactions, in a given space …

In the emergency under smoke or gas, the visual field of pedestrians is limited,
unreasonable evacuation guidance methods will affect the efficiency of evacuation, and …

We present Causal ABM, a methodology to derive causal structures describing complex
underlying behavioral phenomena. Agent-based models (ABMs) have powerful advantages …

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 …