Asymmetric reformulation of draw rules in chess and its implications for game theory: Repetition as loss for White
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917383945322496 |
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| author | Qi, Chong |
| author_facet | Qi, Chong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03683 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Asymmetric reformulation of draw rules in chess and its implications for game theory: Repetition as loss for White Qi, Chong Computer Science and Game Theory Physics and Society 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. |
| title | Asymmetric reformulation of draw rules in chess and its implications for game theory: Repetition as loss for White |
| topic | Computer Science and Game Theory Physics and Society |
| url | https://arxiv.org/abs/2604.03683 |