研究如何为有独立价值的单需求买家学习最优定价，给出接近最优的查询复杂度。
Learning Optimal Posted Prices for a Unit-Demand Buyer
- 通过样本和价格查询两种方式获取买家价值信息
- 首次给出单位需求定价问题的近似最优样本与查询复杂度
- 适合对机制设计与在线学习感兴趣的读者
我们研究在独立物品价值下，为单需求买家学习最优商品定价的问题，学习者可通过查询访问买家的价值分布。考虑文献中两种常见查询模型：样本访问模型（可获取每个物品价值的样本），以及价格查询模型（可设定物品价格，并获得该物品采样价值是否高于所设价格的二元信号）。本文给出了单位需求定价问题的近似最优样本复杂度与价格查询复杂度。
原文摘要 · Abstract (English)
We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the sample access model where the learner can obtain a sample of each item value, and the pricing query model where the learner can set a price for an item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work, we give nearly tight sample complexity and pricing query complexity of the unit-demand pricing problem.
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