We study dynamic pricing where a seller repeatedly interacts with a strategic, non-myopic buyer who has a fixed private valuation and discounts future utility. Prior work focused exclusively on posted-price mechanisms, where the seller gives a take-it-or-leave-it offer. For our first result, under geometric discounting $γ_t = γ^{t-1}$, we show that menu mechanisms consisting of allocation-payment contracts achieve $O(T_γ)$ regret, where $T_γ$ is the buyer's effective discounted time horizon. We also establish a $Ω(T_γ)$ lower bound, demonstrating the bound is tight. Considering the geometric discounting buyer with a constant discount factor, our bound is $O(1)$, while prior bounds using posted-price mechanisms incur an unavoidable $Ω(\log\log T)$ factor in regret. Our second contribution is more conceptual in nature. The problem of dynamic pricing sits at the intersection of two paradigms: learning with strategic agents in computer science / machine learning and revelation-principle-based mechanism design in economics, yet their relationship has remained unclear. We establish an equivalence between these two classes of mechanisms in our single-buyer setting: any adaptive indirect menu mechanism can be converted into an incentive-compatible direct mechanism with no less revenue, and conversely, any such direct mechanism can be implemented by an adaptive indirect menu mechanism with no less revenue. Consequently, the two classes achieve identical optimal regret. The adaptive, data-driven algorithms of online learning and explicit type elicitation from direct revelation type mechanisms are two languages towards solving the same problem.

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