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Econometrics for Learning Agents

Published: 15 June 2015 Publication History

Abstract

The main goal of this paper is to develop a theory of inference of player valuations from observed data in the generalized second price auction without relying on the Nash equilibrium assumption. Existing work in Economics on inferring agent values from data relies on the assumption that all participant strategies are best responses of the observed play of other players, i.e. they constitute a Nash equilibrium. In this paper, we show how to perform inference relying on a weaker assumption instead: assuming that players are using some form of no-regret learning. Learning outcomes emerged in recent years as an attractive alternative to Nash equilibrium in analyzing game outcomes, modeling players who haven't reached a stable equilibrium, but rather use algorithmic learning, aiming to learn the best way to play from previous observations. In this paper we show how to infer values of players who use algorithmic learning strategies. Such inference is an important first step before we move to testing any learning theoretic behavioral model on auction data. We apply our techniques to a dataset from Microsoft's sponsored search ad auction system.

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Cited By

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  • (2024)Online learning with bounded recallProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693854(43791-43803)Online publication date: 21-Jul-2024
  • (2024)Impact of decentralized learning on player utilities in stackelberg gamesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692518(11253-11310)Online publication date: 21-Jul-2024
  • (2024)Strategizing against No-Regret Learners in First-Price AuctionsProceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673613(894-921)Online publication date: 8-Jul-2024
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Published In

cover image ACM Conferences
EC '15: Proceedings of the Sixteenth ACM Conference on Economics and Computation
June 2015
852 pages
ISBN:9781450334105
DOI:10.1145/2764468
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 15 June 2015

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Author Tags

  1. econometrics
  2. no-regret learning
  3. sponsored search
  4. value inference

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  • Research-article

Funding Sources

  • NSF
  • ONR

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EC '15
Sponsor:
EC '15: ACM Conference on Economics and Computation
June 15 - 19, 2015
Oregon, Portland, USA

Acceptance Rates

EC '15 Paper Acceptance Rate 72 of 220 submissions, 33%;
Overall Acceptance Rate 664 of 2,389 submissions, 28%

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EC '25
The 25th ACM Conference on Economics and Computation
July 7 - 11, 2025
Stanford , CA , USA

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Cited By

View all
  • (2024)Online learning with bounded recallProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693854(43791-43803)Online publication date: 21-Jul-2024
  • (2024)Impact of decentralized learning on player utilities in stackelberg gamesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692518(11253-11310)Online publication date: 21-Jul-2024
  • (2024)Strategizing against No-Regret Learners in First-Price AuctionsProceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673613(894-921)Online publication date: 8-Jul-2024
  • (2024)Efficient Prior-Free Mechanisms for No-Regret AgentsProceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673460(511-541)Online publication date: 8-Jul-2024
  • (2024)Regulation of Algorithmic CollusionProceedings of the Symposium on Computer Science and Law10.1145/3614407.3643706(98-108)Online publication date: 12-Mar-2024
  • (2024)Efficiency of the Generalized Second-Price Auction for Value MaximizersProceedings of the ACM Web Conference 202410.1145/3589334.3645360(46-56)Online publication date: 13-May-2024
  • (2024)Multi-objective Mechanism Design via AI-Driven ApproachesAI-Driven Mechanism Design10.1007/978-981-97-9286-3_4(95-127)Online publication date: 30-Sep-2024
  • (2024)Dynamic Mechanism Design via AI-Driven ApproachesAI-Driven Mechanism Design10.1007/978-981-97-9286-3_3(53-93)Online publication date: 30-Sep-2024
  • (2023)Are equivariant equilibrium approximators beneficial?Proceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618759(8747-8778)Online publication date: 23-Jul-2023
  • (2023)Non-strategic Econometrics (for Initial Play)Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598694(634-642)Online publication date: 30-May-2023
  • Show More Cited By

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