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AbstractThis paper proposes a quantal response learning model to explain sellers' pricing and learning behaviour observed in a laboratory Bertrand market experiment. In the model, sellers hold beliefs about their opponents' strategies and play quantal best responses to these beliefs. After each round, sellers update their beliefs based on the information learned from previous play. The results indicate that when sellers have full past price information, the model explains the price distributions within periods and the dynamics across periods. The fit is particularly good if one allows for sellers being risk averse. In contrast, quantal response equilibrium does not work well.