# Econometrics for Learning Agents

arXiv: https://arxiv.org/abs/1505.00720 · HTML: https://mlsanity.com/paper/1505.00720/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2015.

## Built on by (most cited first, 23 of 23)

- [Contracting with a Learning Agent](https://mlsanity.com/paper/2401.16198/) (2024)
- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions](https://mlsanity.com/paper/2110.03906/) (2021)
- [Strategizing against No-Regret Learners in First-Price Auctions](https://mlsanity.com/paper/2402.08637/) (2024)
- [Maximizing utility in multi-agent environments by anticipating the behavior of other learners](https://mlsanity.com/paper/2407.04889/) (2024)
- [Generalized Principal-Agent Problem with a Learning Agent](https://mlsanity.com/paper/2402.09721/) (2024)
- [Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games](https://mlsanity.com/paper/2102.12415/) (2021)
- [AI-driven Prices for Externalities and Sustainability in Production Markets](https://mlsanity.com/paper/2106.06060/) (2021)
- [Games with Payments between Learning Agents](https://mlsanity.com/paper/2405.20880/) (2024)
- [Efficient Inverse Multiagent Learning](https://mlsanity.com/paper/2502.14160/) (2025)
- [Online Learning with Bounded Recall](https://mlsanity.com/paper/2205.14519/) (2022)
- [Are Equivariant Equilibrium Approximators Beneficial?](https://mlsanity.com/paper/2301.11481/) (2023)
- [Computational Intractability of Strategizing against Online Learners](https://mlsanity.com/paper/2503.04202/) (2025)
- [From No-Regret to Strategically Robust Learning in Repeated Auctions](https://mlsanity.com/paper/2601.03853/) (2026)
- [Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty](https://mlsanity.com/paper/2302.09700/) (2023)
- [Randomized Truthful Auctions with Learning Agents](https://mlsanity.com/paper/2411.09517/) (2024)
- [Breaking Algorithmic Collusion in Human-AI Ecosystems](https://mlsanity.com/paper/2511.21935/) (2025)
- [Solipsistic Superintelligence is Unlikely to be Cooperative](https://mlsanity.com/paper/2606.03237/) (2026)
- [From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions](https://mlsanity.com/paper/2112.15151/) (2021)
- [Advancing Ad Auction Realism: Practical Insights & Modeling Implications](https://mlsanity.com/paper/2307.11732/) (2023)
- [Barriers to Welfare Maximization with No-Regret Learning](https://mlsanity.com/paper/2411.01720/) (2024)
- [Improved Multi-Dimensional Forecasting for Swap Regret](https://mlsanity.com/paper/2606.29533/) (2026)

## Abstract (arXiv)

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.