game theory

Evolution of Two-Sided Markets in Game Theoretic Pricing

Mohammed Azam R and B. Lalitha

Computer Science and Engineering Department, College of Engineering

Jawaharlal Nehru Technological University, Anantapur, India [email protected] and [email protected]

Abstract: The emergence of Smart Phones and 3G networks made the mobile phones popular for watching videos provided by service providers by taking subscription. However there is a problem in this subscription model. Due to innovative phone to phone communication technologies the subscribers are able to redistribute the content to nonsubscribers causing potential damage to revenues of the service providers. To solve this problem Lin and Liu proposed a game theoretic pricing approach to attract non-subscribers to become subscribers thus preventing the illegal redistribution of videos. They provided an analysis for equilibrium which ensures that the service provider is profited and improves QoS for subscribers further. However, they do not consider analysis of two sided markets that emerge due to the interactions between parties though one or more platforms. This paper focuses on building a prototype application which demonstrates the proof of concept of game theoretic pricing besides exploring the emergence of two sided markets and analyzing their mutual performance in presence of market dynamics. The empirical results revealed that the proposed mechanisms can help analyze two sided markets. Keywords: Game theoretic pricing, Two sided markets, Video streaming.

1. INTRODUCTION

With the advent of innovative multiple technologies, video streaming is made possible. Mobile phones also have become famous for downloading videos as they are supported by 3G or 4G networks. This has become good business opportunity to network service providers. However, they need to have efficient solutions for providing multimedia data streaming. Mobile devices such as smart phones, PDAs and laptops are being used to interactions among them with cooperative content caching for performance are found in the literature which includes secure transactions [9], [10], [11]. Mobile phones usage has become common. People of all walks of life need mobile device as of now. It has become a necessity than a luxury [12], [13]. Due to the high popularity and innovative applications in mobile devices the subscribers to a video service are capable of redistributing the video content illegally. It does mean that some users do no pay money to subscribe but uses the videos through subscribers. This causes the service providers to forgo profits substantially. This behavior among the non-subscriber is more as they need to pay fewer amounts to

subscribers for illegal sharing of content when compared to the legal subscription. Tracking such illegal behavior among the subscribers is also difficult due the mobility nature of mobiles and the short duration redistribution activities.

Instead of tracking that way, the mobile service

provider thinks it as a game and uses game theory. As per the game theory, the parties behave to protect their own interests. For instance service providers think about setting a game theoretic pricing that will attract the non-subscribers to become subscribers as they price lets them to do so. That optimal pricing which can influence the non-subscribers is known as equilibrium state which bring about more customers to service providers while eliminating the non-subscribers watching videos illegally. When number of subscribers is more, the service provider gets more profits and thinks about improving quality service and reducing the price further. This kind of game theory has become popular recently [14], [15]. Lin and Liu [16] proposed a game theoretic pricing scheme according to which service provider and subscribers are players in the game. The analysis is made for optimal pricing for service provider to gain more profits in the streaming market. The game theoretic pricing helps the mobile users who do not subscribe to understand that the pricing is low and they are influenced to become subscribers. In the process the present non-subscribers who gain illegal access to videos become subscribers gradually and finally there will be equilibrium state in which the service providers do not see any misbehaving non-subscribers. Such equilibrium state is also known as ESS (Evolutionary Stable Strategy).

The remainder of the paper is organized as

follows. Section II provides details of game theoretic model proposed by Lin and Liu [16]. Section III presents the proposed two sided market evaluation. Section IV provides prototype implementation details. Section V presents experimental results while section VI concludes the paper.

2. GAME THEORITIC PRICING MODEL

The game theoretical pricing model proposed by Lin and Liu [16] is described here. Consider a scenario where a mobile service provider provides video streaming

Journal of Innovation in Computer Science and Engineering

Vol.3(1), Jul -Dec 2013 @ ISSN 2278 - 0947 20

__________________________________________________________________________________________________

__________________________________________________________________________________________________

services to its subscribers. The paid subscribers are supposed to get quality of services from the service provider. However, due to the availability of data sharing technologies, the subscribers are able to redistribute video content to non-subscribers for monetary benefits. To present this a game theoretic pricing model has been proposed. The model video – stream redistribution network is as shown in fig. 1.

Figure. 1 –Video stream redistribution network

As can be seen in fig. 1, the subscribers are able to watch videos streamed from the service providers. This is as per the business rules. However, the subscribers are sharing the video content to non-subscribers for monetary benefits. To avoid this problem and ensure maximum profits to service provider, game theoretic pricing scheme was proposed. This scheme brings computes optimal pricing to attract non-subscribers to subscribe to the service providers. The scheme brings about the ideal equilibrium in which the service providers will be able to gain maximum profits by reducing the non-subscribers. More details on this can be found in [16]. In this paper we built a prototype application that demonstrates the proof of concept of [16] besides implementing an algorithm to evaluate the emergence of two seeded markets in the context of game theoretical pricing.

3. PROPOSED TWO SIDED MARKET Two Sided Markets

Two sided markets come into existence when two parties see gains with mutual interaction through a mediator or platform. There are three types of agents involved in two sided markets. The first type are known as service providers, the second type subscribers and the third type intermediaries that support existence of the network among the first and two parties. The users or parties in the two sided markets are named blue user and green user for convenience. Cross-side network effects

When new service providers (blue user) enter into market the same side benefits reduce while the cross side

benefits increase. In this case, on the cross side subscriber can be assumed. Same-side network effect

When the number of subscribers enters into the market, it adds value to the network. However, there might be negative effect on the same side while the other side gets attractive benefits. Market State and Evaluation

In two sided markets interactions are between the two types of users. As per the terminology pertaining to two sided markets there is market state associated. The market state at any given point of time “t” is calculated as follows.

M (t) = (B1(t), …, Bk(t), G1(t), …, Gk(t) The evaluation of two sided markets is done as follows. M(t + 1) = F(M(t)) Where F is a stochastic function.

For two sided market evaluation between the

service providers and service consumers described in the scenario of game theoretic pricing, the algorithm proposed is as given in listing 1.

1. If(new service provider)

• Calculate probability of same side and cross side effects

2. Else if(remove service provider) • Calculate probability of same side

and cross side effects

3. Else if(new subscriber) • Calculate probability of same side


4. Else if(remove subscriber) • Calculate probability of same side


5. Update the effects database

Listing 1- Algorithm for two sided market evaluation

As seen in listing 1, computation of same side

and cross side markets is made to evaluate the benefits. Generally when new service providers are added, the same side effect will not be encouraging as the service providers have to share the market available. When a service provider is removed from the context, then the existing service providers will get changes to increase their market share thus adding to the same side benefits. In the same fashion, when the number of subscribers are increased it


Vol.3(1), Jul -Dec 2013 @ ISSN 2278 - 0947 21

__________________________________________________________________________________________________

__________________________________________________________________________________________________

obviously increases benefits to cross side while the same side may or may not get added benefits. When number of subscribers is removed from the network, the same side benefits may increase in terms of bandwidth while the negative side of this is that the subscriber may not be able to improve service quality as his revenues go down.

4. IMPLEMENTATION

A custom simulator application is built in Java platform that includes mobile clients. The environment used is a PC which runs Windows 7 with 4 GB RAM, Core 2 Dual processor. NetBeans is used as an IDE to build the application. MY SQL is used as backend. The application facilitates interaction between two types of users namely service providers and service consumers.

5. EXPERIMENTAL RESULTS We have made many experiments with the

prototype application. The experiments are related to average utility of subscribers, utility of the secondary buyers, optimal video stream pricing, and so on. We also made experiments on two sided market evaluation. The results of optimal service pricing using game theory and also the results of two sided market evaluation are presented in a series of graphs. Results of Game Theoretic Pricing

Figure 2 – Number of users vs. utilities and optimal price

As can be seen in fig. 2, when number of

subscribers is increased, the optimal video stream price increases (price decreases), utility of the secondary buyer decreases, and average utility of the subscribers remain same almost.

Figure 3 – Optimal streaming price vs. network delay

As can be seen in fig. 3 the horizontal axis

represents network delay while the vertical axis represents optimal streaming price for the service provider. When the network delay increases, the optimal streaming price for the service provider decreases.

Figure 4 – Number of secondary buyers vs. utility

As can be seen in fig. 4, it is evident that when

number of secondary users increases, the utility of secondary buyers decrease, the optimal content price increases, and the utility of subscribers also increase.


Vol.3(1), Jul -Dec 2013 @ ISSN 2278 - 0947 22

__________________________________________________________________________________________________

__________________________________________________________________________________________________

Figure 5.Convergence of the iterated algorithm

As seen in fig. 5 the horizontal axis represents number of iterations while the vertical axis represents averaged utility of the secondary buyers. As number of iterations increase, the average utility of the secondary buyers also increase.

Results Two Sided Market Evaluation The results of two sided market evaluation from the standpoint of subscriber are presented in fig. 6 while the same from the standpoint of service provider is presented in fig. 7. The experiments made on two sided market evaluation as per the algorithm presented in listing 1 are reflected in fig. 6 and fig. 7. The results show same side and other side effects. The analysis is made to find the effects in the network with respect to same side and the cross side.

Figure 6: Two sided market evaluation from subscriber

As seen in fig. 6, when subscriber count increases same side benefits decrease while the cross side benefits increase. When the number of subscribers is removed from the market, the same side benefits are increased while the cross side benefits are decreased.

Figure 7: Two sided market evaluation from subscriber

As seen in fig. 7, when service provider count increases same side benefits decrease while the cross side benefits increase. When the number of service providers is removed from the market, the same side benefits are increased while the cross side benefits are decreased.

6. CONCLUSION

In this paper we implement the game theoretic pricing model that can influence non-subscribers illegally accessing video content through subscribers. The service providers of video content get benefited from the optimal game theoretic pricing to increase number of subscribers drastically. We built a prototype application which implements game theoretic pricing scheme proposed by Lin and Liu [16] besides making a provision in the application to explore the emergence of two sided markets and their mutual benefits in the presence of market dynamics. We evaluate two sided markets that arise between service providers and service consumers. The empirical results revealed that the proposed application is effective in optimizing the pricing and also analyze how the two sided markets are influenced in presence of each other.


Vol.3(1), Jul -Dec 2013 @ ISSN 2278 - 0947 23

__________________________________________________________________________________________________

__________________________________________________________________________________________________

REFERENCES 1. D. F. S. Santos and A. Perkusich, “Granola: A location and

bandwidth aware protocol for mobile video on-demand systems,” in Proc. Int.Conf. SoftCom, Sep. 2008, pp. 309–313.

2. S. Sudin, A. Tretiakov, R. H. R. M. Ali, and M. E. Rusli, “Attacks on mobile networks: An overview of new security challenge,” in Proc. Int.Conf. Electron. Design, Dec. 2008, pp. 1–6.

3. H. Lee, Y. Lee, J. Lee, D. Lee, and H. Shin, “Design of a mobile video streaming system using adaptive spatial resolution control,” IEEETrans. Consum. Electron., vol. 55, no. 3, pp. 1682–1689, Aug. 2009.

4. T. Schierl, T. Stockhammer, and T.Wiegand, “Mobile video transmission using scalable video coding,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 17, no. 9, pp. 1204–1217, Sep. 2007.

5. W. A. Vorbau, A. S. Mitchell, and K. O’Hara, ““My iPod is my pacifier”: An investigation on the everyday practices of mobile video consumption,” in Proc. IEEEWorkshop HotMobile,Mar. 2007, pp. 29–33.

6. B. Girod and N. Farber, “Feedback-based error control for mobile video transmission,” Proc. IEEE, vol. 87, no. 10, pp. 1707–1723, Feb. 1999.

7. S. Jumisko-Pyykko and J. Hakkinen, “Evaluation of subjective video quality of mobile devices,” in Proc. 13th Annu. ACM Int. Conf. Multimedia, 2005, p. 538.

8. M. Ries, O. Nemethova, and M. Rupp, “Video quality estimation for mobile H.264/AVC video streaming,” J. Commun., vol. 3, no. 1, pp. 41–50, Jan. 2008.

9. M. Taghizadeh and S. Biswas, “Minimizing content provisioning cost in heterogeneous social wireless networks,” in Proc. 3rd Int. Conf.COMSNETS, 2011, pp. 1–10.

10. N. Dimokas, D. Katsaros, and Y. Manolopoulos, “Cooperative caching in wireless multimedia sensor networks,” Mobile Netw. Appl., vol. 13, no. 3/4, pp. 337–356, Aug. 2008.

11. S. Banerjee and S. Karforma, “A prototype design for DRM based credit card transaction in e-commerce,” Ubiquity, vol. 9, no. 18, pp. 1–9, 2008.

12. International Telecommunication Union [Online]. Available: http://www.itu.int/itu-d/ict/statistics/ict/ graphs/mobile.jpg

13. K. O’Hara, A. S. Mitchell, and A. Vorbau, “Consuming video on mobile devices,” in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2007, p. 866.

14. I. Ahmad and J. Luo, “On using game theory for perceptually tuned rate control algorithm for video coding,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 16, no. 2, pp. 202–208, 2006.

15. H. Zhao, W. S. Lin, and K. J. R. Liu, “Behavior modeling and forensics for multimedia social networks: A case study in multimedia fingerprinting,” IEEE Signal Process. Mag., vol. 26, no. 1, pp. 118–139, Jan. 2009.

16. W. Sabrina Lin and K. J. Ray Liu.”Game-Theoretic Pricing for Video Streaming in Mobile Networks”. IEEE Transactions on Image Processing, vol. 21, no. 5, May 2012. pp2667-2680.

AUTHORS BIOGHRAPHY Mr. Mohammed Azam R, is a M.Tech in CSE. He has been

actively involved in coordinating and organizing plenty of national events such as seminars and workshops. He has coordinated and participated in numerous events such as Quizzes, Debates. He has presented papers at technical fest. Worked as Organizing Committee Member for College day

and Technical sessions. He has keen interest in paper writing and presentations on emerging technologies.


Vol.3(1), Jul -Dec 2013 @ ISSN 2278 - 0947 24

__________________________________________________________________________________________________

__________________________________________________________________________________________________

game theory

Documents