# Learning Prices for Repeated Auctions with Strategic Buyers

arXiv: https://arxiv.org/abs/1311.6838 · HTML: https://mlsanity.com/paper/1311.6838/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2013.

## Built on by (most cited first, 30 of 37)

- [Bilateral Trade: A Regret Minimization Perspective](https://mlsanity.com/paper/2109.12974/) (2021)
- [Learning in Stackelberg Games with Non-myopic Agents](https://mlsanity.com/paper/2208.09407/) (2022)
- [Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents](https://mlsanity.com/paper/2306.02704/) (2023)
- [A Regret Analysis of Bilateral Trade](https://mlsanity.com/paper/2102.08754/) (2021)
- [Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions](https://mlsanity.com/paper/2110.03906/) (2021)
- [Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model](https://mlsanity.com/paper/2303.08613/) (2023)
- [Multi-armed Bandit Algorithm against Strategic Replication](https://mlsanity.com/paper/2110.12160/) (2021)
- [Regret Analysis of Repeated Delegated Choice](https://mlsanity.com/paper/2310.04884/) (2023)
- [Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions](https://mlsanity.com/paper/2408.08272/) (2024)
- [A Parametric Contextual Online Learning Theory of Brokerage](https://mlsanity.com/paper/2407.01566/) (2024)
- [Learning to Manipulate a Commitment Optimizer](https://mlsanity.com/paper/2302.11829/) (2023)
- [On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms](https://mlsanity.com/paper/2307.07675/) (2023)
- [Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification](https://mlsanity.com/paper/2312.07338/) (2023)
- [Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting](https://mlsanity.com/paper/2501.16018/) (2025)
- [From No-Regret to Strategically Robust Learning in Repeated Auctions](https://mlsanity.com/paper/2601.03853/) (2026)
- [Dynamic Reserve Price Design with Distributed Solving Algorithm](https://mlsanity.com/paper/2206.10295/) (2022)
- [A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design](https://mlsanity.com/paper/2210.10278/) (2022)
- [Corruption-Robust Lipschitz Contextual Search](https://mlsanity.com/paper/2307.13903/) (2023)
- [Learning Local Stackelberg Equilibria from Repeated Interactions with a Learning Agent](https://mlsanity.com/paper/2510.22471/) (2025)
- [Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition](https://mlsanity.com/paper/2106.04392/) (2021)
- [Learning Revenue Maximization using Posted Prices for Stochastic Strategic Patient Buyers](https://mlsanity.com/paper/2202.06143/) (2022)
- [Differentially Private Online Item Pricing](https://mlsanity.com/paper/2305.11362/) (2023)
- [Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter](https://mlsanity.com/paper/2309.09443/) (2023)
- [GMTR: Graph Matching Transformers](https://mlsanity.com/paper/2311.08141/) (2023)
- [DarkShot: Lighting Dark Images with Low-Compute and High-Quality](https://mlsanity.com/paper/2312.16805/) (2023)
- [TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing](https://mlsanity.com/paper/2401.05431/) (2024)
- [TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks](https://mlsanity.com/paper/2401.05432/) (2024)
- [T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image](https://mlsanity.com/paper/2403.13663/) (2024)
- [Decentralized Online Learning in General-Sum Stackelberg Games](https://mlsanity.com/paper/2405.03158/) (2024)
- [Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning](https://mlsanity.com/paper/2407.04898/) (2024)

## Abstract (arXiv)

Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret --- the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus --- i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.