Despite reaching a point of acceptance as a research tool across the geographical and
social sciences, there remain significant methodological challenges for agent‐based …

Agent-based modelling and simulation (ABMS), whether simple toy models or complex data-
driven ones, is regularly applied in various domains to study the system-level patterns …

Agent-based modeling (ABM) has been widely employed by researchers in various
domains. Developing valid and useful agent-based models (ABMs) imposes challenges on …

Agent-based models establish a suitable simulation technique to recreate real complex
systems, such as those approached in marketing. Reinforcement learning is about learning …

Socio-ecological systems include diverse resources of complexity involving complicated
interactions, feedback, dynamic behavior patterns, heterogeneity in agents' characteristics …

The growing number of end-of-life (EOL) wind blades could further strain US landfills or be a
valuable composite materials source, depending on stakeholders' behaviors. Technical …

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 …

As the need for a just and equitable energy transition accelerates, disruptive clean energy
technologies are becoming more visible to the public. Clean energy technologies, such as …

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 …

Traditional agent-based modelling is mostly rule-based. For many systems, this approach is
extremely successful, since the rules are well understood. However, for a large class of …