Chris J. Scogings and Ken A. Hawick
agent-based models, cellular automata, hybrid model, spatial locality, rule-set
Agent-based systems and cellular automata are two closely related model formulations that are heavily used in studying complex systems. They are both formulated as microscopically simple rule-based models that are applied to individual cells or agents in a collection, where the spatially localised neighbourhood of other cells or agents is used as input to update each one. We have experimented with a range of models including classic cellular automata, through more sophisticated multi-state automata, flocking models, and stochastic-agent models and animat agent-based predator-prey models. We discuss algorithmic commonalities and code implementation patterns that have emerged as common properties of these models and describe how we have experimented with optimal data structures to support spatially localised models of this class. We show how concurrency and model correctness issues are affected by different data structures in addition to their effect on model update computational efficiency.
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