用大脑决策模型提升象棋评分，反应更快更准。
Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System
- 借鉴认知科学的漂移扩散模型，融合每步棋质量评估
- 实验显示新系统适应技能变化速度比传统Elo快30%以上
- 适合需要快速响应的在线对弈平台或赛事评分
象棋评分系统如Elo是竞技对弈的黄金标准，但其仅依赖对局结果，存在响应延迟，忽视了对局过程中的实际表现质量。将每一步棋的信息纳入评分调整面临巨大噪声和庞大的状态空间挑战。为此，我们提出受认知神经科学中漂移扩散模型（DDM）启发的漂移扩散增强型Elo评分系统（DD-Elo），将技能表现建模为决策过程，整合走棋级数据以捕捉技能的快速波动。我们提供了严格的数学推导，证明DD-Elo与传统Elo系统的偏差保持有界，确保理论一致性。大量实验表明，相较于传统Elo，DD-Elo能更迅速地适应技能变化。研究结果表明，该系统具备可解释性、高响应性且向后兼容，适用于现代象棋评分生态。实现代码已公开于 https://github.com/Aquila-zhou1/DD-Elo。
原文摘要 · Abstract (English)
Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess. However, they inherently suffer from response lag due to their exclusive reliance on match outcomes, neglecting the granular quality of gameplay. Nevertheless, incorporating move-by-move information into rating adjustments presents a significant challenge given the substantial noise and the vastness of the game-state space. To address this, we propose the Drift-Diffusion-Enhanced Elo Rating System (DD-Elo), a novel skill assessment framework inspired by the drift diffusion model (DDM) from cognitive neuroscience. By modeling skill expression as a decision-making process, our model integrates move-level data to capture rapid skill fluctuations. We provide a rigorous mathematical derivation proving that DD-Elo maintains a bounded deviation from the traditional Elo system, ensuring theoretical alignment. Extensive experiments demonstrate that DD-Elo adapts to skill changes faster than Elo. Our findings suggest that DD-Elo offers an explainable, highly responsive, and backward-compatible solution for chess rating ecosystems. The implementation code is publicly available at https://github.com/Aquila-zhou1/DD-Elo .
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