ABSTRACT
In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspot). The unbounded utility model fits the large scale data sharing and explosion in social media, where it is expected that the network value follows Metcalfe's or Zipf's law. For both models, we analyze the optimal pricing schemes to select heterogeneous users in the platform under complete and incomplete information of users' service valuations. We propose the concept of price of information (PoI) to characterize the profit loss due to lack of information, and present provable PoI bounds for different utility models. We show that the PoI = 2 for the bounded utility model, meaning that just half of profit is lost, whereas the PoI ≥ 2 for the unbounded utility model and increases as for a less concave utility function. We also show that the complicated differentiated pricing scheme which is optimal under incomplete user information, can be replaced by a single uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of 'pure' service users contributing no externalities, and show that the platform may charge zero price to the original group of users in order to attract the pure user group.
- P. Antoniadis, C. Courcoubetis, and R. Mason. 2004. Comparing economic incentives in peer-to-peer networks. Computer Networks 46, 1 (2004), 133--146. Google ScholarDigital Library
- B. Briscoe, A. Odlyzko, and B. Tilly. 2006. Metcalfe's law is wrong - communications networks increase in value as they add members-but by how much? IEEE Spectrum 43, 7 (2006), 34--39. Google ScholarDigital Library
- O. Candogan, K. Bimpikis, and A. Ozdaglar. 2012. Optimal Pricing in Networks with Externalities. Operations Research 60, 4 (2012), 883--905. Google ScholarDigital Library
- C. Courcoubetis and R. Weber. 2006. Incentives for large peer-to-peer systems. IEEE Journal on Selected Areas in Communications 24, 5 (2006), 1034--1050. Google ScholarDigital Library
- P. Golle, K. Leyton-Brown, I. Mironov, and M. Lillibridge. 2001. Incentives for Sharing in Peer-to-Peer Networks. In Electronic Commerce, Ludger Fiege, Gero Mühl, and Uwe Wilhelm (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 75--87. Google ScholarDigital Library
- X. Gong, L. Duan, X. Chen, and J. Zhang. 2017. When Social Network Effect Meets Congestion Effect in Wireless Networks: Data Usage Equilibrium and Optimal Pricing. IEEE Journal on Selected Areas in Communications 35, 2 (2017), 449--462. Google ScholarDigital Library
- C.Jiang, L. Gao, L. Duan, and J. Huang. 2018. Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing. IEEE Transactions on Mobile Computing 17, 4 (2018), 898--912.Google ScholarCross Ref
- Y. Li, C. Courcoubetis, and L. Duan. 2017. Dynamic Routing for Social Information Sharing. IEEE Journal on Selected Areas in Communications 35, 3 (2017), 571--585. Google ScholarDigital Library
- Y. Li, C. Courcoubetis, L. Duan, and R. Weber. 2018. Optimal pricing for a peer-to-peer sharing platform under network externalities. Technical Report. https://arxiv.org/abs/1805.09616Google Scholar
- M. H. Manshaei, J. Freudiger, M. Felegyhazi, P. Marbach, and J. P. Hubaux. 2008. On Wireless Social Community Networks. In IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.Google Scholar
- Juniper Research. 2017. Sharing economy revenues to double by 2022, reaching over $40 billlion. Retrieved Apr 24, 2018 from https://www.juniperresearch. com/press/press-releases/sharing-economy-revenues-to-double-by-2022Google Scholar
- X. Wang, L. Duan, and J. Zhang. 2018. Mobile Social Services with Network Externality: From Separate Pricing to Bundled Pricing. IEEE Transactions on Network Science and Engineering (2018).Google Scholar
Index Terms
- Optimal pricing for a peer-to-peer sharing platform under network externalities
Recommendations
Optimal Pricing for Peer-to-Peer Sharing With Network Externalities
In this paper, we analyse how a peer-to-peer sharing platform should price its service to maximize profit, when user participation increases the value of the service to others by causing positive externalities. Modelling the service as an excludable ...
Optimal online pricing with network externalities
We study the optimal pricing strategy for profit maximization in presence of network externalities where a decision to buy a product depends on the price offered to the buyer and also on the set of her friends who have already bought that product. We ...
Equilibrium pricing with positive externalities
We study the problem of selling an item to strategic buyers in the presence of positive historical externalities, where the value of a product increases as more people buy and use it. This increase in the value of the product is the result of resolving ...
Comments