Agent-based modelling is a process of representing and simulating the intentions, behaviours and actions of complex systems with the goal of understanding specific phenomenon related to the communications within complex systems that produces emergent behaviour and self-organisation, or for predicting spatial or behavioural patterns of individuals or groups of interacting entities. Agent-based modelling, also termed multiagent systems, or in ecological simulation, individual-based model spans simple to highly complex systems, their interactions can be difficult to implement and optimise programmatically particularly when there could be hundreds of thousands of agents within a community that have multiple levels of communication. The resolution and the scale of simulation is an especially important component that could determine the accuracy of the models. This article focuses on the model resolution of complex systems, facilitated by an object-oriented communications framework, a foundation for the simulation of the fine resolution of the dynamics, behaviour, preferences, interaction and n-tiered trophic networks, including the simulated environments they inhabit. It dissects individual agents with a view to modelling and simulating fine behaviours amongst a population of agent-types in n-tiered networks, scalable to hundreds of thousands of species using mathematically defined behaviour, efficient algorithms and adaptive data structures as support for the simulations.
Ch’ng, E. (2013) Model Resolution in Complex Systems Simulation: Agent Preferences, Behaviour, Dynamics and N-Tiered Networks, Special Issue on Agent-based Modeling and Simulation of Complex Adaptive Communication Networks and Environments (CACOONS), Simulation: Transactions of the Society for Modeling and Simulation International [link]