Recontracting and stochastic stability in cooperative games

An evolutionary style model of recontracting is given which guarantees convergence to core allocations of an underlying cooperative game. Unlike its predecessors in the evolution/learning literature, this is achieved without assumptions of convexity of the characteristic function or a reliance on random errors. The stochastic stability properties of the model are then examined and it is shown that stochastically stable states solve a simple and intuitive minimization problem which reduces to maximizing a Rawlsian SWF for a common class of utility functions. In contrast to previous analyses, the stochastically stable state is unique for a broad class of utility functions.