Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
A player׳s knowledge of her own actions and the corresponding payoffs may enable her to infer or form beliefs about what the payoffs would have been if she had played differently. For quantitative learning models employed in studies of low information environments, players׳ ex-post inferences and beliefs have been largely ignored. For games with large strategy spaces, this omission can seriously weaken the predictive power of a learning model. We propose a novel method of using players׳ ex-post inferences and assessments to impute foregone payoffs for unplayed strategies in low-information environments. We then use the resulting learning model to explain the pricing and learning behavior observed in a Bertrand market experiment. Maximum likelihood estimation shows that the extended model organizes the data remarkably well at both the aggregate and individual levels.