Towards Model-Free Learning in Dynamic Population Games: An Application to Karma Economies
Dynamic Population Games (DPGs) provide a tractable framework for modeling strategic interactions in large populations of self-interested agents, and have been successfully applied to the design of Karma economies, a class of fair non-monetary resource allocation mechanisms. Despite their appealing theoretical properties, existing computational tools for DPGs assume full knowledge of the game model and operate in a centralized fashion, limiting their applicability in realistic settings where agents have access only to their own private experience. This paper takes a step towards addressing this gap by studying model-free equilibrium learning in Karma DPGs. First, we analyze the setting in...
| Authors | Matteo Cederle, Saverio Bolognani, Gian Antonio Susto |
|---|---|
| Primary category | cs.GT |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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