发表机构
Doshisha University; RIKEN（立命馆大学; 理化学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过知识子网络模型揭示，集体知识整合的协同效应受知识重叠度与背景拓扑结构共同影响，内部协同在中等重叠时最优，外部协同依赖小世界拓扑。
AI 中文摘要
集体发现通常需要整合部分重叠的知识，然而，有益的重叠程度以及背景拓扑结构的作用仍不清楚。我们将个体知识建模为共同知识空间中的连通子网络，并区分内部协同与外部协同：内部协同指整合缩短了概念之间的路径，而外部协同指一个未知概念连接到共享区域及所有个体特有区域。内部协同在随机、无标度、模块化和小世界背景下均于中等重叠水平达到峰值，反映了互补性与共同基础之间的平衡。外部协同强烈依赖于拓扑结构，并且在小世界结构维持重叠的局部边界时最为显著。重连将协同节点的总供给与其在可访问边界处的集中度分离开来；适度的重连可以增加结构性机会，但同时将其分散到更多外部备选方案中，从而降低其被发现的可能性。群体规模对内部协同的影响取决于拓扑结构，而严格的高阶外部协同则因其全区域标准更具限制性而下降。因此，集体发现同时取决于知识重叠以及互补知识区域在周围知识空间中的组织方式。
英文摘要
Collective discovery often requires the integration of partially overlapping knowledge, yet the beneficial level of overlap and the role of background topology remain unclear. We model individual knowledge as connected subnetworks of a common knowledge space and distinguish internal synergy, in which integration shortens paths between concepts, from external synergy, in which an unknown concept connects to the shared and all individual-specific regions. Internal synergy peaked at intermediate overlap across random, scale-free, modular, and small-world backgrounds, reflecting a balance between complementarity and common ground. External synergy was strongly topology dependent and was most pronounced when small-world structure maintained overlapping local boundaries. Rewiring separated the total supply of synergistic nodes from their concentration at the accessible boundary; moderate rewiring could increase structural opportunities while dispersing them among more external alternatives and reducing their discovery. Group-size effects on internal synergy depended on topology, whereas strict higher-order external synergy declined as its all-region criterion became more restrictive. Collective discovery therefore depends jointly on knowledge overlap and on how complementary knowledge regions are organized within the surrounding knowledge space.
Comments24 pages, 16 figures
