Optimal pricing policy of network goods

Makhdoumi, Ali; Malekian, Azarakhsh; Ozdaglar, Asuman

doi:10.1109/allerton.2017.8262811

Cited by 5 publications

(1 citation statement)

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“…For example, in the pioneering work [8], the authors investigated both the uniform pricing and the discriminatory pricing of the network operator in the presence of network effects. In [31], [32], the authors discussed the dynamic pricing strategy of divisible social goods with network effects. However, the authors investigated the user behaviors only in social networks without considering the congestion in wireless networks.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects

Xiong

Niyato

Wang

et al. 2020

IEEE Trans. Wireless Commun.

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Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods dynamically and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities.We analytically demonstrate that the proposed sequential dynamic pricing scheme can help the operator gain greater revenue and mobile users achieve higher total utilities than those of the baseline static pricing scheme. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator determines the pricing strategy at the beginning of each time period. Mobile users decide on their individual data demand in each time period simultaneously, considering the network effects in the social domain and the congestion effects in the network domain. We construct the social graph using Erdős-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

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Section: Related Workmentioning

confidence: 99%