The recent advancement of agent-based modeling is characterized by higher demands on the parameterization, evaluation and documentation of these computationally expensivemodels. Accordingly, there is also a growing request for “easy to go” applications just mimicking the input-output behavior of such models. Metamodels are being increasingly used for these tasks. In this paper, we provide an overview of common metamodel types and the purposes of their usage in an agent-based modeling context. To guide modelers in the selection and application ofmetamodels for their own needs, we further assessed their implementation effort and performance. We performed a literature research in January 2019 using four di erent databases. Five di erent terms paraphrasing metamodels (approximation, emulator, meta-model, metamodel and surrogate) were used to capture the whole range of relevant literature in all disciplines. All metamodel applications found were then categorized into specific metamodel types and rated by di erent junior and senior researches from varying disciplines (including forest sciences, landscape ecology, or economics) regarding the implementation e ort and performance. Specifically, we captured the metamodel performance according to (i) the consideration of uncertainties, (ii) the suitability assessment provided by the authors for the particular purpose, and (iii) the number of valuation criteria provided for suitability assessment. We selected 40 distinct metamodel applications from studies published in peer-reviewed journals from 2005 to 2019. These were used for the sensitivity analysis, calibration and upscaling of agent-basedmodels, aswell tomimic their prediction for di erent scenarios. This review provides information about themost applicablemetamodel types for each purpose and forms a first guidance for the implementation and validation of metamodels for agent-basedmodels.

中文翻译：

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用于评估、校准和应用基于代理的模型的元模型：回顾

基于代理的建模的最新进展的特点是对这些计算昂贵的模型的参数化、评估和文档提出了更高的要求。因此，对模拟此类模型的输入输出行为的“易于使用”的应用程序的需求也在不断增长。元模型越来越多地用于这些任务。在本文中，我们概述了常见的元模型类型及其在基于代理的建模环境中的使用目的。为了指导建模者根据自己的需要选择和应用元模型，我们进一步评估了他们的实施工作和绩效。我们于 2019 年 1 月使用四个不同的数据库进行了一项文献研究。解释元模型的五个不同术语（近似、仿真器、元模型、元模型和代理）被用来捕捉所有学科的所有相关文献。然后将发现的所有元模型应用程序分类为特定的元模型类型，并由来自不同学科（包括森林科学、景观生态学或经济学）的不同初级和高级研究人员对实施工作和性能进行评分。具体来说，我们根据 (i) 考虑不确定性，(ii) 作者为特定目的提供的适用性评估，以及 (iii) 为适用性评估提供的估值标准的数量来捕获元模型性能。我们从 2005 年至 2019 年发表在同行评审期刊上的研究中选择了 40 个不同的元模型应用程序。这些应用程序用于基于代理的模型的敏感性分析、校准和升级，以及模仿他们对不同场景的预测。这篇综述提供了关于每种目的最适用的元模型类型的信息，并为基于代理的模型的元模型的实施和验证提供了第一个指南。