Strategic Buying Agents

• URL: http://arxiv.org/abs/2607.04708v1
• Date: Mon, 06 Jul 2026 06:24:17 GMT
• Title: Strategic Buying Agents
• Abstract summary: Agentic AI is shifting online shopping from search toward delegated purchasing.<n>We study the design of such strategic buying agents, which must decide when to purchase within a finite shopping window.<n>We formulate this problem across three information regimes: stationary, Bayesian, and robust.
• Score: 5.678371127103685
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy on a consumer's behalf. We study the design of such strategic buying agents, which must decide when to purchase within a finite shopping window, translating price observations, the remaining time horizon, and beliefs about future price changes into a purchase policy. We formulate this problem across three information regimes: stationary, Bayesian, and robust, and treat the resulting optimal policies as a policy menu for implementation. In the stationary regime, price adjustments follow a Poisson arrival process with a known post-adjustment price distribution; the optimal policy is a dynamic purchase-threshold rule, with the threshold governed by an ordinary differential equation. In the Bayesian regime, the adjustment intensity is known, but the price-adjustment distribution is uncertain; the optimal rule remains threshold-based, now depending on posterior beliefs, and we bound the value of knowing the true distribution. In the robust regime, the agent has only price bounds and seeks worst-case protection; randomized threshold policies achieve optimal competitive-ratio and minimax-regret guarantees. We evaluate the proposed policies on Amazon price histories from Keepa (367 items, 48,933 timestamped observations) and examine their integration into language-model buying agents. The stationary and Bayesian policies perform competitively on mean normalized consumer surplus despite their stylized assumptions, while the robust policy performs best at the distribution's 10th percentile. Results suggest language models are better suited to selecting among regimes and calibration samples than to making buy-or-wait decisions directly.

Related papers

• Risk-Controlled Post-Processing of Decision Policies [38.38357747696667]
  We study risk-controlled post-processing: given a deterministic baseline policy, choose a new policy that maximizes agreement with the baseline subject to a chance constraint on a user-specified loss.<n>We show that the optimal policy has a threshold structure: it follows the baseline except on contexts where switching to the fallback policy yields a large reduction in conditional violation risk.<n>Experiments on a COVID-19 radiograph diagnosis task, an LLM routing problem, and a synthetic multiclass decision task show that targeted post-processing can meet or nearly meet risk budgets while preserving substantially more agreement with the baseline than score-blind random mixing
  arXiv  Detail & Related papers  (2026-05-07T16:03:24Z)
• Deadline-Aware Online Scheduling for LLM Fine-Tuning with Spot Market Predictions [11.849924812127371]
  We show the power of prediction in enabling cost-efficient scheduling and its sensitivity to estimation errors.<n>We propose an online allocation algorithm with prediction based on the committed horizon control approach.<n>An online policy selection algorithm is developed that learns the best policy from a pool constructed by varying the parameters of both algorithms.
  arXiv  Detail & Related papers  (2025-12-24T05:47:27Z)
• Best-Effort Policies for Robust Markov Decision Processes [69.60742680559788]
  We study the common generalization of Markov decision processes (MDPs) with sets of transition probabilities, known as robust MDPs (RMDPs)<n>We call such a policy an optimal robust best-effort (ORBE) policy.<n>We prove that ORBE policies always exist, characterize their structure, and present an algorithm to compute them with a small overhead compared to standard robust value iteration.
  arXiv  Detail & Related papers  (2025-08-11T09:18:34Z)
• Learning Deterministic Policies with Policy Gradients in Constrained Markov Decision Processes [59.27926064817273]
  We introduce an exploration-agnostic algorithm, called C-PG, which enjoys global last-iterate convergence guarantees under domination assumptions.<n>We empirically validate both the action-based (C-PGAE) and parameter-based (C-PGPE) variants of C-PG on constrained control tasks.
  arXiv  Detail & Related papers  (2025-06-06T10:29:05Z)
• Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand [7.289672463326423]
  We study the offline sequential feature-based pricing and inventory control problem.<n>Our goal is to leverage the offline dataset to estimate the optimal pricing and inventory control policy.
  arXiv  Detail & Related papers  (2025-04-14T02:57:51Z)
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution [60.6953713877886]
  Policy Convolution family of estimators uses latent structure within actions to strategically convolve the logging and target policies. Experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC.
  arXiv  Detail & Related papers  (2023-10-24T01:00:01Z)
• Contextual Dynamic Pricing with Strategic Buyers [93.97401997137564]
  We study the contextual dynamic pricing problem with strategic buyers. Seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. We propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue.
  arXiv  Detail & Related papers  (2023-07-08T23:06:42Z)
• Autoregressive Bandits [58.46584210388307]
  We propose a novel online learning setting, Autoregressive Bandits, in which the observed reward is governed by an autoregressive process of order $k$. We show that, under mild assumptions on the reward process, the optimal policy can be conveniently computed. We then devise a new optimistic regret minimization algorithm, namely, AutoRegressive Upper Confidence Bound (AR-UCB), that suffers sublinear regret of order $widetildemathcalO left( frac(k+1)3/2sqrtnT (1-G
  arXiv  Detail & Related papers  (2022-12-12T21:37:36Z)
• A Reinforcement Learning Approach to the Stochastic Cutting Stock Problem [0.0]
  We propose a formulation of the cutting stock problem as a discounted infinite-horizon decision process. An optimal solution corresponds to a policy that associates each state with a decision and minimizes the expected total cost.
  arXiv  Detail & Related papers  (2021-09-20T14:47:54Z)
• Offline Policy Selection under Uncertainty [113.57441913299868]
  We consider offline policy selection as learning preferences over a set of policy prospects given a fixed experience dataset. Access to the full distribution over one's belief of the policy value enables more flexible selection algorithms under a wider range of downstream evaluation metrics. We show how BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric.
  arXiv  Detail & Related papers  (2020-12-12T23:09:21Z)

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.