What preferences can - and cannot - predict in multi-agent online learning
We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game -- its preference graph -- determine the outcomes of no-regret learning dynamics -- such as follow-the-regularized-leader (FTRL). In one direction, we show that the skeleton of every dynamically stable set (i.e. the set of pure profiles it contains) must also be preferentially stable, that is, it must be closed under profitable deviations. We then ask the converse question: when do preferences determine the long-run behavior of the players' learning dynamics? We begin by showing that preferenc...
| Authors | Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos |
|---|---|
| Primary category | cs.GT |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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