NP-Hardness and Approximation Algorithms for Iterative Pricing on Social Networks with Externalities

Chenli Shen, Wensong Lin
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引用次数: 1

Abstract

We study how a monopolist seller should price an indivisible product iteratively to the consumers who are connected by a known link-weighted directed social network. For two consumers [Formula: see text] and [Formula: see text], there is an arc directed from [Formula: see text] to [Formula: see text] if and only if [Formula: see text] is a fashion leader of [Formula: see text]. Assuming complete information about the network, the seller offers consumers a sequence of prices over time and the goal is to obtain the maximum revenue. We assume that the consumers buy the product as soon as the seller posts a price not greater than their valuations of the product. The product’s value for a consumer is determined by three factors: a fixed consumer specified intrinsic value and a variable positive (resp. negative) externality that is exerted from the consumer’s out(resp. in)-neighbours. The setting of positive externality is that the influence of fashion leaders on a consumer is the total weight of links from herself to her fashion leaders who have owned the product, and more fashion leaders of a consumer owning the product will increase the influence (external value) on the consumer. And the setting of negative externalities is that the product’s value of showing off for a consumer is the total weight of links from her followers who do not own the product to herself, and more followers of a consumer owning the product will decrease this external value for the consumer. We confirm that finding an optimal iterative pricing is NP-hard even for acyclic networks with maximum total degree [Formula: see text] and with all intrinsic values zero. We design a greedy algorithm which achieves [Formula: see text]-approximation for networks with all intrinsic values zero and show that the approximation ratio [Formula: see text] is tight. Complementary to the hardness result, we design a [Formula: see text]-approximation algorithm for Barabási–Albert networks.
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具有外部性的社会网络迭代定价的np -硬度和近似算法
我们研究了一个垄断的销售者如何对一个不可分割的产品进行迭代定价,这些消费者通过一个已知的链接加权有向社会网络连接。对于两个消费者[Formula: see text]和[Formula: see text]来说,当且仅当[Formula: see text]是[Formula: see text]的时尚领导者时,存在一条从[Formula: see text]到[Formula: see text]的弧线。假设关于网络的信息是完整的,卖方向消费者提供一段时间内的一系列价格,目标是获得最大的收益。我们假设,一旦卖家公布的价格不高于消费者对该产品的估价,消费者就会购买该产品。产品对消费者的价值是由三个因素决定的:一个固定的消费者指定的内在价值和一个可变的正价值。负的)外部性是由消费者的外部施加的。邻居。正外部性的设定是,时尚领袖对消费者的影响是她与拥有该产品的时尚领袖之间的链接的总权重,拥有该产品的消费者的时尚领袖越多,对消费者的影响(外部价值)就会增加。负外部性的设定是,产品对消费者的炫耀价值是她的追随者不拥有该产品到她自己的链接的总权重,拥有该产品的消费者的追随者越多,消费者的这种外部价值就会减少。我们证实,即使对于具有最大总度(公式:见文本)且所有内在值为零的无环网络,找到最优迭代定价也是np困难的。我们设计了一种贪心算法,实现了对所有内在值为零的网络的[公式:见文]逼近,并证明了逼近比[公式:见文]是紧的。与硬度结果相补充,我们设计了一个[公式:见文本]- Barabási-Albert网络的近似算法。
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