High-fidelity models are increasingly used to predict, and guide decision making. Prior work has emphasized the importance of replication in ensuring reliable modeling, and has yielded important replication strategies. However, this work is based on relatively simple theory generating models, and its lessons might not translate to high-fidelity models used for decision support. Using NetLogo we replicate a recently published high-fidelity model examining the effects of a HIV biomedical intervention. We use a modular approach to build our model from the ground up, and provide examples of the replication process investigating the replication of two sub-modules as well as the overall simulation experiment. For the first module, we achieved numerical identity during replication, whereas we obtained distributional equivalence in replicating the second module. We achieved relational equivalence among the overall model behaviors, with a 0.98 correlation across the two implementations for our outcome measure even without strictly following the original model in the formation of the sexual network. Our results show that replication of high-fidelity models is feasible when following a set of systematic strategies that leverage the modularity, and highlight the role of replication standards, modular testing, and functional code in facilitating such strategies.

中文翻译：

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在高保真模型复制过程中利用模块化：复制基于代理的艾滋病毒预防模型的经验教训。

高保真模型越来越多地用于预测和指导决策。先前的工作强调了复制在确保可靠建模方面的重要性，并产生了重要的复制策略。然而，这项工作基于相对简单的理论生成模型，其经验教训可能无法转化为用于决策支持的高保真模型。使用 NetLogo，我们复制了最近发布的高保真模型，用于检查 HIV 生物医学干预的效果。我们使用模块化方法从头开始构建模型，并提供复制过程的示例，研究两个子模块的复制以及整体模拟实验。对于第一个模块，我们在复制过程中实现了数字同一性，而我们在复制第二个模块时获得了分布等价性。我们在整体模型行为之间实现了关系等价，即使在性网络的形成中没有严格遵循原始模型，我们的结果测量的两种实现之间的相关性也为 0.98。我们的结果表明，当遵循一组利用模块化的系统策略时，高保真模型的复制是可行的，并强调了复制标准、模块化测试和功能代码在促进此类策略中的作用。