Adapting Rules of Official International Mahjong for Online Players

2026-01-13

AI Summary

Traditional international mahjong rules were designed for offline, multi-round matches with fixed opponents, making them ill-suited to ensure fairness in online single-game random matchmaking. This study introduces, for the first time, an AI-driven, data-based approach to evaluate rule balance by leveraging world-champion-level reinforcement learning agents in large-scale self-play simulations. Statistical analysis of these games reveals significant first-player advantage and flaws in the sub-goal scoring system. To address these issues, the work proposes an intra-game compensation mechanism to replace the traditional multi-round dealer rotation and redesigns the hand-scoring framework. The revised rule set has been deployed in live online play, demonstrably enhancing fairness and player experience in single-game settings.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As one of the worldwide spread traditional game, Official International Mahjong can be played and promoted online through remote devices instead of requiring face-to-face interaction. However, online players have fragmented playtime and unfixed combination of opponents in contrary to offline players who have fixed opponents for multiple rounds of play. Therefore, the rules designed for offline players need to be modified to ensure the fairness of online single-round play. Specifically, We employ a world champion AI to engage in self-play competitions and conduct statistical data analysis. Our study reveals the first-mover advantage and issues in the subgoal scoring settings. Based on our findings, we propose rule adaptations to make the game more suitable for the online environment, such as introducing compensatory points for the first-mover advantage and refining the scores of subgoals for different tile patterns. Compared with the traditional method of rotating positions over multiple rounds to balance first-mover advantage, our compensatory points mechanism in each round is more convenient for online players. Furthermore, we implement the revised Mahjong game online, which is open for online players. This work is an initial attempt to use data from AI systems to evaluate Official Internatinoal Mahjong's game balance and develop a revised version of the traditional game better adapted for online players.

Problem

• Online Mahjong
• Game Balance
• First-Mover Advantage
• Rule Adaptation
• Fairness

Innovation

• AI self-play
• game balance
• rule adaptation
• online mahjong
• first-mover advantage