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The compensation system called komi has been used in scoring games such as Go. In Go, White (the second player) is at a disadvantage because Black gets to move first, giving that player an advantage; indeed, the winning percentage for Black is higher. The perceived value of komi has been re-evaluated over the years to maintain fairness. However, this implies that this static komi is not a sufficiently sophisticated solution. We leveraged existing komi methods in Go to study the evolution of fairness in board games and to generalize the concept of fairness in other contexts. This work revisits the notion of fairness and proposes the concept of dynamic komi Scrabble. We introduce two approaches, static and dynamic komi, in Scrabble to mitigate the advantage of initiative (AoI) issue and to improve fairness. We found that implementing the dynamic komi made the game attractive and provided direct real-time feedback, which is useful for the training of novice players and maintaining fairness for skilled players. A possible interpretation of physics-in-mind is also discussed for enhancing game refinement theory concerning fairness in games.

What Constitutes Fairness in Games? A Case Study with Scrabble