NP-Hardness and Approximation Algorithms for Iterative Pricing on Social Networks with Externalities
| Autor: | Wensong Lin, Chenli Shen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of Foundations of Computer Science. 32:957-979 |
| ISSN: | 1793-6373 0129-0541 |
| DOI: | 10.1142/s0129054121500313 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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