Communicating Sequential Agents: An Analysis of Concurrent Agent Scheduling



McCabe, Stefan D

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Concurrent scheduling of agents presents a challenge for researchers who wish to develop scalable agent-based models (ABMs) without sacrificing intelligibility or fine control over model elements. In this thesis, I advance our understanding of the requirements and challenges of concurrent scheduling by investigating the problem outside of existing ABM modeling frameworks. I examine the possibility space of agent activation regimes, considering as axes: parallelization, selection order, updating regime, endogenous or exogenous access to model state, uniformity of activation, and reproducibility. This analysis informs a systematic review of ABMs on a popular repository of ABM source code to determine how researchers are currently addressing agent activation issues. The review suggests that there is currently widespread homogeneity of modeling practices regarding agent activation. I also expand an existing ABM of economic exchange to demonstrate the effects of varying activation regime on model results and model runtime, extending the analysis to a parallel computing context. This work also extends previous work on agent activation by applying the examination on a more complex model. Varying the activation regime produces significant differences in behavior and model outcomes in this more complex model. This research contributes to the existing literature on the implementation of agent-based models and may be of use for further advances in ABM library development. The results of the case study may also be of interest to researchers of the foundations of economic theory.



Computational social science, Agent-based modeling, Agent activation regimes, Parallel computing