Abstract
We introduce a class of reinforcement models where, at each time step , one first chooses a random subset of colours (independent of the past) from colours of balls, and then chooses a colour from this subset with probability proportional to the number of balls of colour in the urn raised to the power . We consider stability of equilibria for such models and establish the existence of phase transitions in a number of examples, including when the colours are the edges of a graph, a context which is a toy model for the formation and reinforcement of neural connections.
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