  Motivated by the idea that lack of experience is a source of errors but that
experience should reduce them, we model agents' behavior using a stochastic
choice model, leaving endogenous the accuracy of their choice. In some games,
increased accuracy is conducive to unstable best-response dynamics. We define
the barrier to learning as the minimum level of noise which keeps the
best-response dynamic stable. Using logit Quantal Response, this defines a
limitQR Equilibrium. We apply the concept to centipede, travelers' dilemma, and
11-20 money-request games and to first-price and all-pay auctions, and discuss
the role of strategy restrictions in reducing or amplifying barriers to
learning.
