Online Learning for Equilibrium Pricing in Markets under Incomplete Information

• URL: http://arxiv.org/abs/2303.11522v3
• Date: Mon, 16 Jun 2025 01:05:19 GMT
• Title: Online Learning for Equilibrium Pricing in Markets under Incomplete Information
• Abstract summary: We consider the problem of learning equilibrium prices over a horizon of $T$ periods in the incomplete information setting.<n>We develop algorithms that simultaneously achieve a regret of $O(1)$ when the customer demand is constant over time, and $O(sqrtT)$ when the demand varies over time.
• Score: 8.145115843220308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The computation of equilibrium prices at which the supply of goods matches their demand typically relies on complete information on agents' private attributes, e.g., suppliers' cost functions, which are often unavailable in practice. Motivated by this practical consideration, we consider the problem of learning equilibrium prices over a horizon of $T$ periods in the incomplete information setting wherein a market operator seeks to satisfy the customer demand for a commodity by purchasing it from competing suppliers with cost functions unknown to the operator. We first consider the setting when suppliers' cost functions are fixed and develop algorithms that, on three pertinent regret metrics, simultaneously achieve a regret of $O(1)$ when the customer demand is constant over time, and $O(\sqrt{T})$ when the demand varies over time. In the setting when the suppliers' cost functions vary over time, we demonstrate that, in general, no online algorithm can achieve sublinear regret on all three metrics. Thus, we consider an augmented setting wherein the operator has access to hints/contexts that reflect the variation in the cost functions and propose an algorithm with sublinear regret in this augmented setting. Finally, we present numerical experiments that validate our results and discuss various model extensions.

Related papers

• Learning to Price and Stock Under Contextual and Censored Demand [5.326846590465264]
  We present a framework where we model demand as a linear combination of basis functions with unknown coefficients.<n>We propose an efficient algorithm to achieve regret bound $mathcalO(KsqrtTlog T)$ under concave revenue conditions and $mathcalO(K2/3T2/3(log T)1/2)$ for the general case.
  arXiv  Detail & Related papers  (2026-09-05T13:21:46Z)
• Nonparametric Contextual Pricing and Inventory Learning under Censored Demand [5.6700444577605085]
  In online retailing, when a product sells out, a retailer often sees only the units sold, not how many customers would have bought it had inventory been available.<n>We study an online selling problem in which, in each round, the seller observes a market context and then makes pricing and stocking decisions based on censored sales data from previous rounds.<n>We propose a Mean-Calibrated Kernel UCB (MCK-UCB) algorithm that turns each incomplete sales record into a reliable guide for both inventory and price decisions.
  arXiv  Detail & Related papers  (2026-08-31T15:15:48Z)
• Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation [64.51088225051959]
  Heterogeneous AI systems can improve quality and efficiency by routing queries to the specialist who can answer most effectively at the lowest cost.<n>We formalize this tradeoff as an instance of Pandora's Box, the classical problem of optimal search with costly inspection.<n>Under a Gaussian signal model, the resulting policies have closed-form value-of-information expressions that determine, for each specialist and input, whether refining the value estimate is worth its cost.
  arXiv  Detail & Related papers  (2026-08-20T17:54:37Z)
• A Sustainable AI Economy Needs Data Deals That Work for Generators [56.949279542190084]
  We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality.<n>We analyze 73 public data deals and show that the majority of value accrues to aggregators.<n>We propose an Equitable Data-Value Exchange Framework to enable a minimal market that benefits all participants.
  arXiv  Detail & Related papers  (2026-01-15T01:05:48Z)
• Learning Steerable Clarification Policies with Collaborative Self-play [67.67872810596839]
  To handle ambiguous queries, AI assistants need a policy for managing their uncertainty.<n>We propose to train steerable policies for managing this uncertainty using self-play.<n>We show this leads to a steerable policy that changes its behavior predictably conditioned on the provided costs.
  arXiv  Detail & Related papers  (2025-12-03T18:49:54Z)
• e1: Learning Adaptive Control of Reasoning Effort [88.51897900019485]
  Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning.<n>Users may prefer to allocate different amounts of reasoning effort depending on how they value output quality versus latency and cost.<n>We propose Adaptive Effort Control, a self-adaptive reinforcement learning method that trains models to use a user-specified fraction of tokens.
  arXiv  Detail & Related papers  (2025-10-30T23:12:21Z)
• Bayesian Optimization for Dynamic Pricing and Learning [0.306238659426286]
  Dynamic pricing is the practice of adjusting the selling price of a product to maximize a firm's revenue by responding to market demand.<n>Traditional approaches often assume a specific parametric form for the demand function, enabling the use of reinforcement learning (RL) to identify near-optimal pricing strategies.<n>We propose a nonparametric approach to dynamic pricing that avoids restrictive modeling assumptions.
  arXiv  Detail & Related papers  (2025-10-14T12:28:06Z)
• Cost-aware Stopping for Bayesian Optimization [53.34052774820105]
  We propose a cost-aware stopping rule for Bayesian optimization that adapts to varying evaluation costs and is free of tuning.<n>We prove a theoretical guarantee bounding the expected cumulative evaluation cost incurred by our stopping rule when paired with state-of-the-art acquisition functions.
  arXiv  Detail & Related papers  (2025-07-16T17:54:14Z)
• Optimal Nonlinear Online Learning under Sequential Price Competition via s-Concavity [24.586053819490985]
  We consider price competition among multiple sellers over a selling horizon of $T$ periods. Sellers simultaneously offer their prices and observe their respective demand that is unobservable to competitors. We show that when all sellers employ our policy, their prices converge at a rate of $O(T-1/7)$ to the Nash equilibrium prices that sellers would reach if they were fully informed.
  arXiv  Detail & Related papers  (2025-03-20T22:51:03Z)
• Dynamic Pricing with Adversarially-Censored Demands [25.566323930646178]
  We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,ldots, T$ is and dependent on the price. A perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. We show that our algorithm achieves $tildeO(sqrtT)$ optimal regret even with adversarial inventory series.
  arXiv  Detail & Related papers  (2025-02-10T05:37:39Z)
• Leveraging Microservices Architecture for Dynamic Pricing in the Travel Industry: Algorithms, Scalability, and Impact on Revenue and Customer Satisfaction [1.03590082373586]
  This research investigates the implementation of a real-time,-oriented dynamic pricing system for the travel sector. The system is designed to address factors such as demand, competitor pricing, and other external circumstances in real-time. Both controlled simulation and real-life application showed a respectable gain of 22% in revenue generation and a 17% improvement in pricing response time.
  arXiv  Detail & Related papers  (2024-11-03T17:24:02Z)
• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We propose an immersion-aware model trading framework that facilitates data provision for services while ensuring privacy through federated learning (FL) We design an incentive mechanism to incentivize metaverse users (MUs) to contribute high-value models under resource constraints. We develop a fully distributed dynamic reward algorithm based on deep reinforcement learning, without accessing any private information about MUs and other MSPs.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
  We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
  arXiv  Detail & Related papers  (2024-03-21T14:48:37Z)
• Online Learning under Budget and ROI Constraints via Weak Adaptivity [57.097119428915796]
  Existing primal-dual algorithms for constrained online learning problems rely on two fundamental assumptions. We show how such assumptions can be circumvented by endowing standard primal-dual templates with weakly adaptive regret minimizers. We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions.
  arXiv  Detail & Related papers  (2023-02-02T16:30:33Z)
• Dynamic Pricing with Volume Discounts in Online Settings [102.00782184214326]
  This paper focuses on pricing in e-commerce when objective function is profit and only transaction data are available. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. We design a two-phase online learning algorithm, namely-B- capable of exploiting the data in an online fashion.
  arXiv  Detail & Related papers  (2022-11-17T16:01:06Z)
• Learning to Price Supply Chain Contracts against a Learning Retailer [3.7814216736076434]
  We study the supply chain contract design problem faced by a data-driven supplier. Both the supplier and the retailer are uncertain about the market demand. We show that our pricing policies lead to sublinear regret bounds in all these cases.
  arXiv  Detail & Related papers  (2022-11-02T04:00:47Z)
• A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design [158.0041488194202]
  We study reserve price optimization in multi-phase second price auctions. From the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders. Third, the seller's per-step revenue is unknown, nonlinear, and cannot even be directly observed from the environment.
  arXiv  Detail & Related papers  (2022-10-19T03:49:05Z)
• KnowGraph-PM: a Knowledge Graph based Pricing Model for Semiconductors Supply Chains [0.0]
  KnowGraph-PM is a knowledge graph-based dynamic pricing model. Price change potentially generates conflicts with customers. We demonstrate that semantic data integration enables customer-tailored revenue management.
  arXiv  Detail & Related papers  (2022-05-13T10:34:57Z)
• Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements [3.2732273647357437]
  We study an online variant of Fisher markets, wherein users with privately known utility and budget parameters arrive sequentially. In this setting, we first study the limitations of static pricing algorithms, which set uniform prices for all users. We design adaptive posted-pricing algorithms, one with knowledge of the distribution of users' budget and utility parameters and another that adjusts prices solely based on past observations of user consumption.
  arXiv  Detail & Related papers  (2022-04-27T05:03:45Z)
• Convex Loss Functions for Contextual Pricing with Observational Posted-Price Data [2.538209532048867]
  We study an off-policy contextual pricing problem where the seller has access to samples of prices which customers were previously offered. This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed. In our setting, the observed data is influenced by the historic pricing policy, and we do not know how customers would have responded to alternative prices.
  arXiv  Detail & Related papers  (2022-02-16T22:35:39Z)
• Loss Functions for Discrete Contextual Pricing with Observational Data [8.661128420558349]
  We study a pricing setting where each customer is offered a contextualized price based on customer and/or product features. We observe whether each customer purchased a product at the price prescribed rather than the customer's true valuation.
  arXiv  Detail & Related papers  (2021-11-18T20:12:57Z)
• Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima [114.31577038081026]
  We propose an efficient method that tackles the designer's and agents' problems simultaneously in a single loop. Although the designer does not solve the equilibrium problem repeatedly, it can anticipate the overall influence of the incentives on the agents. We prove that the algorithm converges to the global optima at a sublinear rate for a broad class of games.
  arXiv  Detail & Related papers  (2021-10-04T06:53:59Z)
• VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback [104.06766271716774]
  We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism. Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
  arXiv  Detail & Related papers  (2020-04-19T18:00:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.