We analyze the performance of the best-response dynamic across all
normal-form games using a random games approach. The playing sequence -- the
order in which players update their actions -- is essentially irrelevant in
determining whether the dynamic converges to a Nash equilibrium in certain
classes of games (e.g. in potential games) but, when evaluated across all
possible games, convergence to equilibrium depends on the playing sequence in
an extreme way. Our main asymptotic result shows that the best-response dynamic
converges to a pure Nash equilibrium in a vanishingly small fraction of all
(large) games when players take turns according to a fixed cyclic order. By
contrast, when the playing sequence is random, the dynamic converges to a pure
Nash equilibrium if one exists in almost all (large) games.Comment: JEL codes: C62, C72, C73, D83 Keywords: Best-response dynamics,
equilibrium convergence, random games, learning models in game