Model resolution in complex systems simulation: Agent preferences, behavior, dynamics and n-tiered networks

Agent-based modeling is a process of representing and simulating the intentions, behaviors and actions of complex systems with the goal of understanding specific phenomena related to the communications within complex systems that produce emergent behavior and self-organization, or for predicting spatial or behavioral patterns of individuals or groups of interacting entities. Agent-based modeling, also termed multi-agent systems, or in ecological simulation, individual-based models, spans simple to highly complex systems; their interactions can be difficult to implement and optimize 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, behavior, preferences, interaction and n-tiered trophic networks, including the simulated environments they inhabit. It dissects individual agents with a view to modeling and simulating fine behaviors amongst a population of agent types in n-tiered networks, scalable to hundreds of thousands of species using mathematically defined behavior, efficient algorithms and adaptive data structures as support for the simulations.