论文标题
使用随机战略患者买家的发布价格学习收入最大化
Learning Revenue Maximization using Posted Prices for Stochastic Strategic Patient Buyers
论文作者
论文摘要
我们认为一个卖方面临的买家，他们有能力推迟他们的决定，我们称之为耐心。每种买家的类型都由价值和耐心组成，并采样了。从分布。卖方使用张贴的价格，希望从销售给买方来最大化她的收入。在本文中，我们将此环境正式化，并描述了由此产生的Stackelberg平衡，卖方首先致力于她的策略，然后买家最能做出回应。在此之后，我们展示了如何计算最佳纯和混合策略。然后，我们考虑一个学习环境，卖方无法通过购买者的类型访问分销。我们的主要结果是以下。我们通过计算此设置的脂肪震动维度来得出一个用于学习近似最佳纯策略的样本复杂性。此外，我们为近似最佳混合策略提供了一般样本复杂性。我们还考虑在线环境，并在最佳纯策略和最佳混合策略方面获得了消失的遗憾。
We consider a seller faced with buyers which have the ability to delay their decision, which we call patience. Each buyer's type is composed of value and patience, and it is sampled i.i.d. from a distribution. The seller, using posted prices, would like to maximize her revenue from selling to the buyer. In this paper, we formalize this setting and characterize the resulting Stackelberg equilibrium, where the seller first commits to her strategy, and then the buyers best respond. Following this, we show how to compute both the optimal pure and mixed strategies. We then consider a learning setting, where the seller does not have access to the distribution over buyer's types. Our main results are the following. We derive a sample complexity bound for the learning of an approximate optimal pure strategy, by computing the fat-shattering dimension of this setting. Moreover, we provide a general sample complexity bound for the approximate optimal mixed strategy. We also consider an online setting and derive a vanishing regret bound with respect to both the optimal pure strategy and the optimal mixed strategy.
