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The Power of Menus in Dynamic Pricing: Near Optimal Regret and Equivalence Results

NeurIPS 2026 · Bandits and online learning › Online convex optimization and games · approach: Learning for pricing and auctions

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Studies dynamic pricing against a non-myopic strategic buyer with a fixed valuation, showing menu mechanisms achieve near-optimal regret of O(T_γ log T_γ) and establishing equivalence results with posted-price mechanisms.

Problem

Dynamic pricing with a strategic, non-myopic buyer who has a fixed private valuation and discounts future utility; prior work only considered posted-price mechanisms.

Approach

Extends the seller's mechanism class from posted prices to menu mechanisms (allocation-payment menus) and shows these achieve O(T_γ log T_γ) regret, where T_γ is the effective horizon induced by the discount factor, along with equivalence results relating menus and posted prices.

Key results

Experimental setup

Metrics: regret

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