Repetition-based draw rules in deterministic games like chess ensure termination but introduce strategic artifacts, allowing players to enforce draws independent of positional value. We propose an asymmetric modification: threefold repetition results in a loss for White if it is responsible for initiating it. This rule directly targets the persistent first-move advantage and removes low-effort draw strategies available to White. The new rule is expected to reduce draw rates, re-balance first-move advantage, and promote exploration in both human and artificial play. We outline a computational framework with existing and newly designed neural-network chess engines for the empirical validation of the proposal and analyze it from the perspectives of game theory and graph dynamics.

翻译：基于重复判定的和棋规则在象棋等确定性游戏中确保了终局性，但引入了策略性伪像，允许棋手无视棋局实际价值而强制达成和棋。我们提出一种非对称修改方案：若白方主动引发三次重复局面，则判定白方负。该规则直接针对持续存在的先手优势，消除白方可利用的低成本和棋策略。预期新规则将降低和棋率、重新平衡先手优势，并促进人类与人工智能棋手在博弈中的探索行为。我们基于现有及新设计的神经网络象棋引擎构建了计算框架用于验证该方案，并从博弈论与图动力学视角展开分析。