Reinforcement learning and stochastic fictitious play are apparent rivals as models of human learning. They embody quite different assumptions about the processing of information and optimisation. This paper compares their properties and finds that they are far more similar than was thought. In...

Reinforcement learning and stochastic fictitious play are apparent rivals as models of human learning. The embody quite different assumptions about the processing of information and optimisation. This paper compares their properties and finds that they are far more similar than were thought. In...

We consider a matching model of the labour market where workers that differ in quality send signals to firms that are also vertically differentiated. Signals allow assortative matching in which the highest quality workers send the highest signals and are hired by the best firms. Matching is...

In many markets it is possible to find rival sellers charging different prices for the same good. Earlier research has attempted to explain this phenomenon by demonstrating the existence of dispersed price equilibria when consumers must make use of costly search to discover prices. We ask...

We investigate the stability of mixed strategy equilibria in 2 person (bimatrix) games under perturbed best response dynamics. A mixed equilibrium is asymptotically stable under all such dynamics if and only if the game is linearly equivalent to a zero sum game. In this case, the mixed...

This paper briefly and informally surveys different theoretical models of relative concerns and their relation to inequality. Models of inequity aversion in common use in experimental economics imply a negative relation between inequality and happiness. In contrast, empirical studies on...

Fictitious play and "gradient" learning are examined in the context of a large population where agents are repeatedly randomly matched. We show that the aggregation of this learning behaviour can be qualitatively different from learning at the level of the individual. This aggregate dynamic...