  We study games in which the set of strategies is multi-dimensional, and new
agents might learn various strategic dimensions from different mentors. We
introduce a new family of dynamics, the recombinator dynamics, which is
characterised by a single parameter, the recombination rate r in [0,1]. The
case of r = 0 coincides with the standard replicator dynamics. The opposite
case of r = 1 corresponds to a setup in which each new agent learns each new
strategic dimension from a different mentor, and combines these dimensions into
her adopted strategy. We fully characterise stationary states and stable states
under these dynamics, and we show that they predict novel behaviour in various
applications.
