Asymmetric reformulation of draw rules in chess and its implications for game theory: Repetition as loss for White

TL;DR

This paper proposes an asymmetric rule change in chess where threefold repetition results in a loss for White, aiming to reduce draws and balance first-move advantage, supported by a computational framework for validation.

Contribution

It introduces a novel asymmetric repetition rule in chess to address strategic artifacts and re-balance first-move advantage, with a framework for empirical testing.

Findings

Expected reduction in draw rates

Re-balancing of first-move advantage

Promotion of exploration in human and AI play

Abstract

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.