  In repeated games, strategies are often evaluated by their ability to
guarantee the performance of the single best action that is selected in
hindsight, a property referred to as \emph{Hannan consistency}, or
\emph{no-regret}. However, the effectiveness of the single best action as a
yardstick to evaluate strategies is limited, as any static action may perform
poorly in common dynamic settings. Our work therefore turns to a more ambitious
notion of \emph{dynamic benchmark consistency}, which guarantees the
performance of the best \emph{dynamic} sequence of actions, selected in
hindsight subject to a constraint on the allowable number of action changes.
Our main result establishes that for any joint empirical distribution of play
that may arise when all players deploy no-regret strategies, there exist
dynamic benchmark consistent strategies such that if all players deploy these
strategies the same empirical distribution emerges when the horizon is large
enough. This result demonstrates that although dynamic benchmark consistent
strategies have a different algorithmic structure and provide significantly
enhanced individual assurances, they lead to the same equilibrium set as
no-regret strategies. Moreover, the proof of our main result uncovers the
capacity of independent algorithms with strong individual guarantees to foster
a strong form of coordination.
