Algorithmic Price Discrimination

Algorithmic Price Discrimination
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算法价格歧视

DOI:
10.1137/1.9781611975994.149
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发表时间:
2020
期刊:
Proceedings of the Annual ACMSIAM Symposium on Discrete Algorithms
影响因子:
--
通讯作者:
Wang, Xianging
Wang, Xianging
中科院分区:
--
文献类型:
--
作者:
Cummings, Rachel;Devanur, Nikhil R.;Huang, Zhiyi;Wang, Xianging

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我们考虑[4]中研究的三度价格歧视问题的推广(Bergemann et al.,2015年),其中买方和卖方之间的中介可以设计细分市场,以最大限度地提高消费者剩余和卖方收入的任何线性组合。与文献[4]不同,我们假设中间人只知道买方价值的部分信息。我们考虑三种不同的信息模型,随着难度的增加。在第一个模型中,我们假设中介的信息允许他构建买方价值的概率分布。接下来,我们考虑样本复杂度模型,其中我们假设中介只看到来自此分布的样本。最后,我们考虑一个强盗在线学习模型,中介只能观察买家过去的购买决策,而不是她的确切价值。对于这些模型中的每一个,我们提出了算法来计算最优或接近最优的市场细分。
We consider a generalization of the third degree price discrimination problem studied in [4](Bergemann et al., 2015), where an intermediary between the buyer and the seller can design market segments to maximize any linear combination of consumer surplus and seller revenue. Unlike in [4], we assume that the intermediary only has partial information about the buyer's value. We consider three different models of information, with increasing order of difficulty. In the first model, we assume that the intermediary's information allows him to construct a probability distribution of the buyer's value. Next we consider the sample complexity model, where we assume that the intermediary only sees samples from this distribution. Finally, we consider a bandit online learning model, where the intermediary can only observe past purchasing decisions of the buyer, rather than her exact value. For each of these models, we present algorithms to compute optimal or near optimal market segmentation.
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