摘要翻译：
近年来，许多学者称赞机器学习(ML)技术在基于Agent的仿真模型(ABM)中的应用似乎是无穷无尽的可能性。为了更全面地了解这些可能性，我们进行了系统的文献综述(SLR)，并根据理论推导的分类方案对ML在ABM中的应用进行了分类。我们这样做是为了研究到目前为止，机器学习是如何准确地应用于基于Agent的模型中的，并批判性地讨论这两种有前途的方法的结合。我们发现，确实，有一个广泛的可能的应用ML支持和补充ABMs在许多不同的方式，已经应用于许多不同的学科。我们看到，到目前为止，最大似然模型主要用于两种广泛的ABM情况：第一，具有经验学习的自适应智能体的建模，第二，对给定ABM产生的结果进行分析。虽然这些是最常见的，但也存在许多更有趣的应用。在这种情况下，研究人员应该更深入地分析哪些ML技术何时以及如何支持ABM，例如，通过对不同的用例进行更深入的分析和比较。然而，由于ML在ABM中的应用是有一定代价的，研究人员不应该仅仅为了做而将ML用于ABMs。
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英文标题：
《Is the Juice Worth the Squeeze? Machine Learning (ML) In and For
  Agent-Based Modelling (ABM)》
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作者：
Johannes Dahlke, Kristina Bogner, Matthias Mueller, Thomas Berger,
  Andreas Pyka and Bernd Ebersberger
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  In recent years, many scholars praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models (ABM). To get a more comprehensive understanding of these possibilities, we conduct a systematic literature review (SLR) and classify the literature on the application of ML in and for ABM according to a theoretically derived classification scheme. We do so to investigate how exactly machine learning has been utilized in and for agent-based models so far and to critically discuss the combination of these two promising methods. We find that, indeed, there is a broad range of possible applications of ML to support and complement ABMs in many different ways, already applied in many different disciplines. We see that, so far, ML is mainly used in ABM for two broad cases: First, the modelling of adaptive agents equipped with experience learning and, second, the analysis of outcomes produced by a given ABM. While these are the most frequent, there also exist a variety of many more interesting applications. This being the case, researchers should dive deeper into the analysis of when and how which kinds of ML techniques can support ABM, e.g. by conducting a more in-depth analysis and comparison of different use cases. Nonetheless, as the application of ML in and for ABM comes at certain costs, researchers should not use ML for ABMs just for the sake of doing it.
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PDF链接：
https://arxiv.org/pdf/2003.11985