Department of Social Informatics, Kyoto University, 606-8501 Kyoto, Japan
Proc Biol Sci. 2018 Mar 14;285(1874). doi: 10.1098/rspb.2017.2360.
Progress in understanding and managing complex systems comprised of decision-making agents, such as cells, organisms, ecosystems or societies, is-like many scientific endeavours-limited by disciplinary boundaries. These boundaries, however, are moving and can actively be made porous or even disappear. To study this process, I advanced an original bibliometric approach based on network analysis to track and understand the development of the model-based science of agent-based complex systems (ACS). I analysed research citations between the two communities devoted to ACS research, namely agent-based (ABM) and individual-based modelling (IBM). Both terms refer to the same approach, yet the former is preferred in engineering and social sciences, while the latter prevails in natural sciences. This situation provided a unique case study for grasping how a new concept evolves distinctly across scientific domains and how to foster convergence into a universal scientific approach. The present analysis based on novel hetero-citation metrics revealed the historical development of ABM and IBM, confirmed their past disjointedness, and detected their progressive merger. The separation between these synonymous disciplines had silently opposed the free flow of knowledge among ACS practitioners and thereby hindered the transfer of methodological advances and the emergence of general systems theories. A surprisingly small number of key publications sparked the ongoing fusion between ABM and IBM research. Beside reviews raising awareness of broad-spectrum issues, generic protocols for model formulation and boundary-transcending inference strategies were critical means of science integration. Accessible broad-spectrum software similarly contributed to this change. From the modelling viewpoint, the discovery of the unification of ABM and IBM demonstrates that a wide variety of systems substantiate the premise of ACS research that microscale behaviours of agents and system-level dynamics are inseparably bound.
理解和管理由决策主体（如细胞、生物体、生态系统或社会）组成的复杂系统的进展，与许多科学努力一样，受到学科界限的限制。然而，这些界限正在移动，可以主动使其变得多孔甚至消失。为了研究这一过程，我提出了一种基于网络分析的原始文献计量方法，以跟踪和理解基于主体的复杂系统（ACS）的基于模型的科学的发展。我分析了致力于 ACS 研究的两个社区（基于主体的建模（ABM）和基于个体的建模（IBM））之间的研究引文。这两个术语都指的是相同的方法，然而前者在工程和社会科学中更受欢迎，而后者在自然科学中更流行。这种情况为把握一个新概念如何在不同的科学领域中明显发展以及如何促进融合成一种通用的科学方法提供了一个独特的案例研究。本分析基于新颖的异被引指标，揭示了 ABM 和 IBM 的历史发展，证实了它们过去的不连续性，并检测到它们的渐进融合。这些同义词学科之间的分离默默地反对了 ACS 从业者之间知识的自由流动，从而阻碍了方法学进展的转移和一般系统理论的出现。少数关键出版物引发了 ABM 和 IBM 研究的持续融合。除了提高对广谱问题的认识的综述外，通用模型制定协议和跨越边界的推断策略是科学整合的关键手段。可访问的广谱软件同样促成了这一变化。从建模的角度来看，发现 ABM 和 IBM 的统一证明了各种各样的系统都证实了 ACS 研究的前提，即主体的微观行为和系统级动态是不可分割地联系在一起的。
