Combinator dynamics: Multidimensional evolutionary learning

In complex strategic settings with multiple dimensions, players often simplify decisions by breaking them down into separate dimensions. In contrast to standard replicator dynamics, which assume perfect multidimensional coordination, our combinator dynamics model captures cases in which players revise actions dimension by dimension, imitate successful traits in each dimension, and then combine the selected traits into a new action. We analyze key properties of these dynamics—monotonicity, stationarity, and stability. We then extend the model to heterogeneous settings, where some players imitate entire multidimensional actions while others combine traits imitated separately in each dimension. We apply the framework to organizational decision‐making, where decentralized adoption of practices can generate coordination failures, persistent performance differences, and a trade‐off between specialized and versatile organizational traits.