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arXiv Machine Learning

Honesty is the Best Policy ... or is it? Demand Management for Probabilistic Service Options

The paper proposes a framework for service providers to display probabilistic service level information at booking time, aiming to inform customers and redistribute demand. It models the challenge as a sequential decision process, introduces recursive definitions of truthful probabilities, and imposes transparency constraints to limit manipulation. Using reinforcement learning and a sampling‑based mixed‑integer program, the authors approximate truthful probabilities and optimize the communicated information, demonstrating a trade‑off between individual transparency and overall customer utility in out‑of‑home delivery scenarios.

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arXiv AI
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Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets

The paper presents a reinforcement learning framework for training autonomous large language model agents to act as sellers in multi-product bargaining markets. It models the problem as a Partially Observable Markov Decision Process and introduces a structured message protocol to map natural language into a regulated decision space. Using Reinforcement Learning from Verifiable Rewards, the trained agents match limited inventory to buyers more effectively, achieving seller surplus extraction and allocation quality comparable to or better than trillion‑parameter models, and generalize to unseen market conditions.

By Shuze Daniel Liu, Claire Chen, Jiuqi Wang, David Simchi-Levi, Thorsten Joachims
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arXiv AI
Sep 2

Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

The paper introduces a new online fair division framework where a learner must allocate indivisible items to agents in real time, balancing fairness and efficiency. Traditional methods rely on many copies of each item to estimate utilities, but this is unrealistic for platforms with many users and few interactions. By treating utility as an unknown function of item-agent features and framing the problem as a contextual bandit, the authors propose algorithms that achieve sublinear regret and demonstrate their effectiveness experimentally.

By Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low
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arXiv Machine Learning
Aug 18

Learning to Price with Persuasion

arXiv:2608. 16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design.

By Maria-Florina Balcan, Tejas Pagare, Karan Singh
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arXiv Machine Learning
Oct 12

Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models

The paper introduces the Constrained Triple-Head Price Optimization (C3PO) network, a transformer-based model designed for real‑time bi‑level decision making in discrete‑choice settings. C3PO integrates price imitation learning, multi‑task revenue prediction, and in‑context elasticity learning, while a lightweight differentiable constraint layer enforces business pricing rules. Experiments on simulated, synthetic, and real‑world datasets show that C3PO outperforms calibrated MNL baselines by up to 7.9 percentage points in expected revenue and reduces constraint violations by over 50%, with significant improvements in healthcare tender and airline ancillary pricing applications.

By Shivaram Subramanian, Zhengliang Xue, Markus Ettl, Yingdong Lu, Jayant Kalagnanam
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arXiv Machine Learning
Aug 31

Robust Assortment Optimization from Observational Data

The paper introduces a robust framework for assortment optimization that addresses distributional shifts in customer choice behavior. It demonstrates computational tractability when the nominal choice model is known and develops statistically optimal algorithms for the data‑driven setting, providing matching upper and lower bounds on sample complexity. The authors identify "robust item‑wise coverage" as the minimal data requirement for efficient robust learning, bridging robustness and statistical efficiency in assortment planning.

By Miao Lu, Yuxuan Han, Han Zhong, Zhengyuan Zhou, Jose Blanchet
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