Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10437781 | Journal of Economic Behavior & Organization | 2005 | 17 Pages |
Abstract
In matrix games with fully mixed solutions, simultaneous gradient ascent by both players does not converge, a fact known as the Crawford puzzle. We suggest the lagging anchor learning model, which we prove to give convergence, as a solution to this puzzle. Our learning model can be viewed as a reinforcement learning process where the players perform relatively little computation. We compare our learning model with other published solutions to the puzzle. We also prove a generalization of the Crawford puzzle by identifying a broad class of learning rules that cannot produce exponential stability of solution points.
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Authors
F.A. Dahl,