Learning by matching patterns

We analyze a model of repeated play between two boundedly rational agents. In each stage each player recalls the outcomes from the most recent few rounds, calculates the distribution of its opponent's past reactions to this outcome pattern, and then optimizes myopically against this distribution. If both players use the same pattern length then the limit points of pattern matching are in the convex hull of the limit points of fictitious play. Thus if fictitious play converges into the set of Nash equilibria then pattern matching converges into the convex hull of Nash equilibria. If the players use different pattern lengths, the more sophisticated player may, but does not generally, succeed in playing as if it could perfectly predict its opponent's play.