Asymmetric reformulation of draw rules in chess and its implications for game theory: Repetition as loss for White
This addresses strategic artifacts in chess for players and AI systems, though it is incremental as it modifies existing rules.
The paper tackles the problem of draw rules in chess by proposing an asymmetric modification where threefold repetition results in a loss for White if responsible, aiming to reduce draw rates and re-balance first-move advantage.
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.