Mean-Field Analysis of Dynamic Rating and Matchmaking

This paper provides a mathematical framework for understanding and controlling rating systems in large-scale competitive platforms. It uses mean-field analysis to model the dynamics of skills and ratings, offering insights into the limitations of rating accuracy (the "Red Queen" effect), the invariance of information content under signal-matched scaling, and the separation of optimal platform policy into filtering and matchmaking components. The work is significant for its application of control theory to online platforms.

• •Skill drift limits the long-run accuracy of rating systems.
• •Information content of interactions is invariant under signal-matched scaling.
• •Optimal platform policy separates into filtering and matchmaking components.