An Experimental Evaluation of Regret-Based Econometrics
Using data obtained in a controlled ad-auction experiment that we ran, we evaluate the regret-based approach to econometrics that was recently suggested by Nekipelov, Syrgkanis, and Tardos (EC 2015). We found that despite the weak regret-based assumptions, the results were (at least) as accurate as those obtained using classical equilibrium-based assumptions. En route we studied to what extent did humans actually minimize regret in our ad-auction, and found a significant difference between the "high types" who indeed rationally minimized regret and the "low types" that significantly over-bid. We suggest that correcting for these biases may improve the accuracy of estimated values.
Gali Noti is a PhD student at the School of Computer Science and at the Center for the Study of Rationality, at the Hebrew University of Jerusalem, under the supervision of Prof. Noam Nisan. Gali's research interests are in the intersection of computer science, economics and psychology. In particular, she is interested in gaining insights from human decision making behavior for enhancing mechanism design and systems, using the framework of (algorithmic-) game theory.