模糊环境下社交网络的最优定价

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Soft Computing Pub Date : 2024-08-20 DOI:10.1007/s00500-024-09657-4
Zhuqing Liu, Yaodong Ni
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引用次数: 0

摘要

在本文中,我们研究了模糊环境下社交网络中垄断者的最优定价策略决策,在这种环境下,消费者会受到非负网络效应的影响,而这种网络效应会受到其邻居消费水平的影响,其受影响的程度被视为一个模糊变量。为了得出均衡解,我们建立了消费者社交网络决策过程的两阶段博弈模型。利用反向归纳法,我们首先得到预期消费均衡,然后通过垄断者利润最大化计算出唯一定价均衡的矩阵表达式。此外,我们还引入了模糊博纳西奇中心性(Fuzzy Bonacich Centrality),并在模糊网络中找出了垄断者向每个消费者收取的价格的组成部分,这就指出了垄断者了解消费者网络结构的重要性。通过数值研究,我们发现在模糊社会网络中,网络效应对定价策略的决定起着至关重要的作用,但模糊性会削弱这种影响。对于存在模糊性的社交网络,垄断者应选择歧视性定价策略,以获得最大利益。我们的模型结果可以为垄断者的定价决策提供有价值的管理启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Optimal pricing in social networks under fuzzy environment

In this paper, we study optimal pricing strategy decisions of the monopolist in a social network under a fuzzy environment, in which consumers experience a nonnegative network effect that is influenced by their neighbors’ consumption level, the extent to which they are affected is considered as a fuzzy variable. To derive the equilibrium solution, we establish a two-stage game model for decision processes in consumer social networks. Utilizing the backward induction, we first get the expected consumption equilibrium, then figure out the matrix expression of unique pricing equilibrium by maximizing the monopolist’s profit. In addition, we introduce Fuzzy Bonacich Centrality, and find out components of the price each consumer charged by the monopolist in a fuzzy network, this points out the importance of the monopolist knowing consumer network structure. By conducting numerical studies, we find that the network effect plays an essential role in deciding pricing strategies in fuzzy social networks, but fuzziness would weaken this impact. For social networks with fuzziness existing, the monopolist should choose discriminatory pricing strategy to benefit most. The results of our model can provide valuable managerial insights when helping the monopolist make pricing decisions.

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来源期刊
Soft Computing
Soft Computing 工程技术-计算机:跨学科应用
CiteScore
8.10
自引率
9.80%
发文量
927
审稿时长
7.3 months
期刊介绍: Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.
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