Professor of Behavioral Economics

Bidding challenges learning theories since experiences for the same bid vary stochastically: the same choice can result in a gain or a loss. In such an environment the question arises how the nearly universally documented phenomenon of loss aversion affects the adaptive dynamics. We analyze the impact of loss aversion in a simple auction for different learning theories. Our experimental results suggest that a version of reinforcement learning which accounts for loss aversion fares as well as more sophisticated alternatives.