An Experimental Evaluation of Regret-Based Econometrics
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Abstract (arXiv)
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 classic equilibrium-based assumptions. En route we studied to what extent humans actually minimize regret in our ad auction, and found a significant difference between the "high types" (players with a high valuation) who indeed rationally minimized regret and the "low types" who significantly overbid. We suggest that correcting for these biases and adjusting the regret-based econometric method may improve the accuracy of estimated values.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 May 2016.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 2.
4 papers.
- 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluationscited by 120
- 2021Auctions Between Regret-Minimizing Agentscited by 22
- 2024Games with Payments between Learning Agentscited by 3
- 2021From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactionscited by 0
- 2024Games with Payments between Learning Agents31 May 2024
- 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations19 Feb 2024
- 2021From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions30 Dec 2021
- 2021Auctions Between Regret-Minimizing Agents22 Oct 2021