arXiv cs.AI· Adamo Cerioli, Vito D. P. Servedio, Gabriele Cerioli, Antonio Villani, Carlo Metta·· 12 小时前AI 评分34
AI 教人类应对围棋中的复杂性：AlphaGo 之后职业棋手战略张力显著上升
AI Teaches Humans to Handle Complexity in the Game of Go
AI 导读
研究以围棋为测试平台，用空间依赖矩阵的谱半径量化“战略张力”，发现人类从新手到大师张力系统性衰减。但分析 1980–2026 年职业对局发现，2016 年 AlphaGo 突破后精英人类对局的持续张力明显上升，现代职业棋手在落子距离与提子的二维投影上接近神经网络引擎。作者据此提出战略张力是决策范式的宏观序参量，而非单纯算力指标，并视 AI 为扩展人类启发式的认知脚手架。
正文
Abstract:Strategic decision-making in complex environments requires balancing local stability against global coordination. Using the game of Go as a bounded empirical testbed, we investigate how biological and artificial agents navigate this trade-off by formalizing a network-derived metric of strategic tension defined via the spectral radius of spatial dependency matrices. Across human skill progression, tension systematically decays from novices to masters as uncoordinated tactical skirmishes give way to disciplined territorial stabilization. However, a longitudinal analysis of professional play spanning 1980--2026 reveals a striking historical paradox: following the superhuman breakthrough of AlphaGo in 2016, elite human matches display a marked surge in sustained tension. A two-dimensional projection based on move distance and captures places modern professionals close to neural engines, separating their tension from the local contact fighting of novices and from random noise. Consequently, strategic tension acts as a macroscopic order parameter for decision paradigms rather than raw computational strength. Beyond the game of Go, these findings establish a constructive paradigm for human-AI interaction, indicating how artificial systems can act as cognitive scaffolding that expands biological heuristics and elevates human capacity to navigate higher-order complexity across professional training and decision-making domains.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33323 [cs.AI] |
| (or arXiv:2610.33323v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33323 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Adamo Cerioli [view email]
[v1]
Fri, 23 Oct 2026 21:52:10 UTC (1,287 KB)
来源：arXiv cs.AI · arxiv.org