摘要翻译：
系统动力学仿真(SDS)和基于Agent的仿真(ABS)的比较和结合研究较少。ABS是在许多抽象级别中使用的范例，包括SDS所涵盖的那些级别。我们相信，为这两种仿真方法之间的选择建立框架将有助于仿真研究。因此，我们的工作目标是为免疫系统相关问题的SDS和ABS方法之间的选择建立方向。以前，我们比较了ABS和SDS在环境中模拟药剂行为的使用，以及这些药剂之间的相互作用。我们得出结论，对于这些类型的代理，使用SDS是更好的，因为它占用更少的计算资源，并产生与ABS模型相同的结果。为了推进这一研究，我们的下一个研究问题是：如果我们引入这些代理之间的相互作用，SDS是否仍然是最合适的范式？为了回答免疫系统模拟问题的这个问题，我们将使用涉及肿瘤细胞和免疫效应细胞之间相互作用的模型作为案例研究。实验表明SDS和ABS在某些情况下不能互换使用，因此它们的比较并不直接。
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英文标题：
《Comparing System Dynamics and Agent-Based Simulation for Tumour Growth
  and its Interactions with Effector Cells》
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作者：
Grazziela P. Figueredo, Uwe Aickelin
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Cell Behavior        细胞行为
分类描述：Cell-cell signaling and interaction; morphogenesis and development; apoptosis; bacterial conjugation; viral-host interaction; immunology
细胞-细胞信号传导及相互作用；形态发生和发育；细胞凋亡；细菌接合；病毒-宿主相互作用；免疫学
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英文摘要：
  There is little research concerning comparisons and combination of System Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm used in many levels of abstraction, including those levels covered by SDS. We believe that the establishment of frameworks for the choice between these two simulation approaches would contribute to the simulation research. Hence, our work aims for the establishment of directions for the choice between SDS and ABS approaches for immune system-related problems. Previously, we compared the use of ABS and SDS for modelling agents' behaviour in an environment with nomovement or interactions between these agents. We concluded that for these types of agents it is preferable to use SDS, as it takes up less computational resources and produces the same results as those obtained by the ABS model. In order to move this research forward, our next research question is: if we introduce interactions between these agents will SDS still be the most appropriate paradigm to be used? To answer this question for immune system simulation problems, we will use, as case studies, models involving interactions between tumour cells and immune effector cells. Experiments show that there are cases where SDS and ABS can not be used interchangeably, and therefore, their comparison is not straightforward.
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PDF链接：
https://arxiv.org/pdf/1108.3235