Dynamic Pricing

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Dynamic pricing studies how to choose prices over time when demand is uncertain or changing, and when decisions affect inventory, customer choices, or competitors. Common approaches include online bandits such as UCB, contextual and nonparametric demand learning, forecasting followed by constrained optimization, and reinforcement learning for competitive markets. Regret analysis provides guarantees for settings such as censored demand, limited inventory, and nonstationary markets, while deployed systems also combine pricing with assortment, advertising, or resource allocation. A central tension is how to learn prices that are profitable and feasible without causing unfair outcomes or encouraging supra-competitive behavior.

Latest papers

50 papers6 papers in the last four weeks, up 20% on the four weeks before. 0.0% of all new papers.

When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State
May 18, 2026Peiying Zhu, Sidi ChangPartially Observable RL Dynamic Pricing
Blossom AI San Francisco, USA · Blossom AI Labs Tokyo, Japan
Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices
May 15, 2026Jackie Baek, Vivek F. Farias, Farrell WuPredict-Then-Optimize Learning to Optimize
Stern School of Business, New York University · Massachusetts Institute of Technology
Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
May 14, 2026Yingying Fan, Yuxuan Han, Jinchi Lv +2Bandit Convex Optimization Dynamic Pricing
Data Sciences and Operations Department, University of Southern California, Los Angeles, California 90089, USA · Stern School of Business, New York University, New York, NY 10003, USA
Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time
May 8, 2026Kalana Kalupahana, Francesco Emanuele Stradi, Matteo Castiglioni +1Corruption Robustness Dynamic Pricing
Politecnico di Milano
Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models
May 7, 2026Shivaram Subramanian, Zhengliang Xue, Markus Ettl +2Large Language Model-Guided Optimization Discrete Choice Modeling
IBM T.J. Watson Research Center Yorktown Heights, New York, 10598, U.S.A.
Market-Alignment Risk in Pricing Agents: Trace Diagnostics and Trace-Prior RL under Hidden Competitor State
May 7, 2026Peiying Zhu, Sidi ChangReward Hacking Reinforcement Learning
Blossom AI · Blossom AI Labs
Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand
May 7, 2026Jianyu Xu, Yu-Xiang WangContextual Bandits UCB Algorithms
Carnegie Mellon University Pittsburgh, PA 15213
Anchor-and-Resume Concession Under Dynamic Pricing for LLM-Augmented Freight Negotiation
Apr 22, 2026Hoang Nguyen, Lu Wang, Marta Gaia BrasAutomated Negotiation Dynamic Pricing
Georgia Institute of Technology · Atlanta, Georgia, USA · Transportation Insight / NTG
A Model and Estimation of the Bitcoin Transaction Fee
Apr 19, 2026Daniel Aronoff, Kristian Praizner, Armin SabouriDynamic Pricing
MIT Department of Economics · MIT EECS · MIT Media Lab Digital Currency Initiative
A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
Apr 15, 2026Lev Razumovskiy, Nikolay KareninDynamic Pricing Reinforcement Learning
JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing
Apr 7, 2026Linghui Meng, Chun Gan, Shengsheng Niu +8Automated Bidding Real-Time Bidding
JD.com China · The Chinese University of Hong Kong China
The Value of Information in Resource-Constrained Pricing
Mar 26, 2026Ruicheng Ao, Jiashuo Jiang, David Simchi-LeviDynamic Pricing Decision-Making under Uncertainty
Institute for Data, Systems, and Society, Massachusetts Institute of Technology · Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology · Department of Civil and Environmental Engineering and Operations Research Center, MIT
Agentic Service Markets Across the Computing Continuum: A Polymatroidal Architecture
Mar 5, 2026Lauri Lovén, Alaa Saleh, Reza Farahani +5Mechanism Design Combinatorial Optimization
Future Computing Group, University of Oulu, Finland · Distributed Systems Group, TU Wien, Vienna, Austria · Department of Mechatronics, University of Prishtina, Kosova +4
Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systems
Mar 5, 2026Emil Kragh Toft, Carolin Schmidt, Daniele Gammelli +1Dynamic Pricing Multi-Agent Reinforcement Learning
Technical University of Denmark, Denmark · Technical University of Munich, Germany · Stanford University, USA
Multi-Agent Reinforcement Learning for Dynamic Pricing: Balancing Profitability,Stability and Fairness
Feb 28, 2026Krishna Kumar Neelakanta Pillai Santha Kumari AmmaDynamic Pricing Reinforcement Learning
Poisson-MNL Bandit: Nearly Optimal Dynamic Joint Assortment and Pricing with Decision-Dependent Customer Arrivals
Feb 18, 2026Junhui Cai, Ran Chen, Qitao Huang +2Assortment Optimization Dynamic Pricing
Department of Information Technology, Analytics, and Operations, University of Notre Dame · Department of Statistics and Data Science, Washington University in St. Louis · Department of Mathematics, Tsinghua University +2
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
Dec 28, 2025Rui Ai, David Simchi-Levi, Feng ZhuCausal Effect Estimation Instrumental Variable Estimation
MIT
LLP: LLM-Based Product Pricing in E-commerce
Oct 10, 2025Hairu Wang, Sheng You, Qiheng Zhang +5E-Commerce LLM Post-Training
Xianyu of Alibaba, Hangzhou, China · University of Science and Technology of China, Suzhou, China
Online Linear Programming with Batching
Aug 1, 2024Haoran Xu, Peter W. Glynn, Yinyu YeSequential Decision Making Regret Minimization
Department of Management Science and Engineering, Stanford University
Algorithmic Collusion by Large Language Models
Mar 31, 2024Sara Fish, Yannai A. Gonczarowski, Ran I. ShorrerPrompt Sensitivity Algorithmic Collusion
†School of Engineering and Applied Sciences, Harvard University · ‡Department of Economics and Department of Computer Science, Harvard University · §Department of Economics, Penn State University

May 18, 2026J/K move · Enter open · S save

When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State

Peiying Zhu, Sidi Chang

Blossom AI San Francisco, USA · Blossom AI Labs Tokyo, Japan

Partially Observable RL Dynamic Pricing Multi-Agent Reinforcement Learning

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Outcome-only evaluation can certify economically unsafe agents: a policy can hit a business KPI while violating deployable behavioral discipline. In hotel pricing with hidden competitor state, a learner can achieve plausible revenue per available room while failing to preserve the rate discipline of a rule-based revenue-management competitor. We introduce discipline stability, a trace-based evaluation paradigm: define the benchmark behavior, restrict observations to the deployment regime, induce trace diagnostics from failure, separate mechanisms with ablations, and test transfer and deployment. Across a two-hotel benchmark and a compact hidden-budget bidding task, reward-only PPO variants miss trace alignment; revealing hidden state reduces label uncertainty; deterministic copy collapses uncertainty; and trace-prior or corrected history policies better preserve price or bid distributions. Pure behavior cloning is nearly enough for symmetric imitation, while Trace-Prior RL adds bounded adaptation under capacity asymmetry. The contribution is an evaluation and benchmark paradigm, not a new optimizer or a universal claim about MARL