Strategic decision-making requires balancing immediate opportunities against long-term objectives: a tension fundamental to competitive environments. We investigate this trade-off in chess by analyzing the dynamics of human and AI gameplay through a network-based metric that quantifies piece-to-piece interactions. Our analysis reveals that elite AI players sustain substantially higher levels of strategic tension for longer durations than top human grandmasters. We find that cumulative tension scales with algorithmic complexity in AI systems and increases linearly with skill level (Elo rating) in human play. Longer time controls are associated with higher tension in human games, reflecting the additional strategic complexity players can manage with more thinking time. The temporal profiles reveal contrasting approaches: highly competitive AI systems tolerate densely interconnected positions that balance offensive and defensive tactics over extended periods, while human players systematically limit tension and game complexity. These differences have broader implications for understanding how artificial and biological systems navigate complex strategic environments and for the deployment of AI in high-stakes competitive scenarios.

翻译：战略决策需要在即时机会与长期目标之间取得平衡：这是竞争环境中的基本张力。我们通过基于网络的度量方法量化棋子间相互作用，分析人类与AI对局的动态，以此研究国际象棋中的这种权衡。分析表明，顶尖AI棋手比顶级人类特级大师能维持显著更高的战略张力水平且持续时间更长。我们发现累积张力随AI系统算法复杂度呈比例增长，而在人类对局中随技能水平（Elo等级分）线性增加。更长的时间控制与人类对局中更高的张力相关，反映出玩家在更多思考时间下能处理的额外战略复杂性。时间分布图揭示了对比鲜明的方法：高度竞争的AI系统能容忍密集互连的局面，在较长时间内平衡进攻与防守战术；而人类棋手则系统性地限制张力和对局复杂度。这些差异对于理解人工与生物系统如何驾驭复杂战略环境，以及AI在高风险竞争场景中的部署具有更广泛的启示意义。