论文标题

对手对评分系统的冷漠:索纳斯的理论案例

Opponent Indifference in Rating Systems: A Theoretical Case for Sonas

论文作者

Bodwin, Greg, Zhang, Forest

论文摘要

在竞争性游戏中,通常将每个玩家分配一个实际数字等级表示他们的技能水平。评级系统是每次赢得每次损失或下降的过程,每次输球时都会调整播放器评分。许多配对系统使玩家可以控制对手的评分;例如,玩家可能能够选择性地与对手公开可见的对手进行比赛,或者在开始之前但瞥见对手的评分之前就流产一场比赛。很自然地问一个人是否可以设计一个不会激励评分最大化的球员进行战略性采取行动的评级系统,并寻求与一个评分的对手的比赛。我们显示以下内容: - 不幸的是,这种“对手无差异”属性的完整版本太强了,无法可行。尽管对某些评级系统满足,但这些系统缺乏某些理想的表达性能,这表明它们不适合捕获大多数感兴趣的游戏。 - 但是,自然的放松是一种自然的放松,大致需要与选择玩家“相当均匀匹配”的任何两个对手之间的冷漠。我们证明,这种轻松的对手冷漠变体可行,我们称之为$ p $对手的漠不关心。实际上,某种强大版本的$ p $对手的冷漠恰恰是评级系统索纳斯(Sonas)的特征,该评分系统最初是因为其在高级国际象棋比赛的结果上的经验预测准确性而提出的。

In competitive games, it is common to assign each player a real number rating signifying their skill level. A rating system is a procedure by which player ratings are adjusted upwards each time they win, or downwards each time they lose. Many matchmaking systems give players some control over their opponent's rating; for example, a player might be able to selectively initiate matches against opponents whose ratings are publicly visible, or abort a match without penalty before it begins but after glimpsing their opponent's rating. It is natural to ask whether one can design a rating system that does not incentivize a rating-maximizing player to act strategically, seeking matches against opponents of one rating over another. We show the following: - The full version of this "opponent indifference" property is unfortunately too strong to be feasible. Although it is satisfied by some rating systems, these systems lack certain desirable expressiveness properties, suggesting that they are not suitable to capture most games of interest. - However, there is a natural relaxation, roughly requiring indifference between any two opponents who are "reasonably evenly matched" with the choosing player. We prove that this relaxed variant of opponent indifference, which we call $P$ opponent indifference, is viable. In fact, a certain strong version of $P$ opponent indifference precisely characterizes the rating system Sonas, which was originally proposed for its empirical predictive accuracy on the outcomes of high-level chess matches.

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