On the Optimal Fixed-Up-To Pricing for Information Services

Fixed-up-to (FUT) pricing allows consumers to purchase a fixed usage amount of an information service for a certain fixed price chosen from a set of options. In this study, we derive an optimal analytical solution for FUT pricing without imposing the strong single-crossing assumption. Further, we illustrate the analytical solution by leveraging mixed integer nonlinear programming to derive an optimal FUT pricing scheme for information services and also investigate when and by how much FUT pricing improves upon commonly used “flat rate” pricing. Our numerical results show that FUT pricing improves the service provider’s profits while enhancing social welfare when consumers face different maximum consumption-level bounds. Notably, in terms of optimal pricing, our numerical results show that the consumers’ maximum consumption-level bounds are more important than their utility functions. Most importantly, our results show that FUT pricing performs better than flat rate pricing under conditions of incomplete information. Finally, we empirically show that it is not necessary to treat over-the-limit rates as a decision variable in terms of optimal FUT pricing since both FUT pricing and three-part tariffs are reasonable approximations of nonlinear pricing in terms of both firm profits and social welfare. We conclude with a discussion of theoretical and practical implications for the design of optimal FUT pricing in terms of enhancing firm profits, consumer surplus, and social welfare. Keywords:Pricing, Nonlinear Mixed Integer Programming, Information Services, Fixed-Up-To (FUT) Pricing

When Mobile Blockchain Meets Edge Computing

Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data, i.e., a block, to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.Comment: Accepted by IEEE Communications Magazin

Optimal pricing strategies for capacity leasing based on time and volume usage in telecommunication networks

In this study, we use a monopoly pricing model to examine the optimal pricing strategies for “pay-per-time”, “pay-per-volume” and “pay-per both time and volume” based leasing of data networks. Traditionally, network capacity distribution includes short/long term bandwidth and/or usage time leasing. Each consumer has a choice to select volume based, connection-time based or both volume and connection-time based pricing. When customers choose connection-time based pricing, their optimal behavior would be utilizing the bandwidth capacity fully, which can cause network to burst. Also, offering the pay-per-volume scheme to the consumer provides the advantage of leasing the excess capacity to other potential customers serving as network providers. However, volume-based strategies are decreasing the consumers’ interest and usage, because the optimal behaviors of the customers who choose the pay-per-volume pricing scheme generally encourages them to send only enough bytes for time-fixed tasks (for real time applications), causing quality of the task to decrease, which in turn creating an opportunity cost. Choosing pay-per time and volume hybridized pricing scheme allows customers to take advantages of both pricing strategies while decreasing (minimizing) the disadvantages of each, because consumers generally have both time-fixed and size-fixed task such as batch data transactions. However, such a complex pricing policy may confuse and frighten consumers. Therefore, in this study we examined the following two issues: (i) what (if any) are the benefits to the network provider of providing the time and volume hybridized pricing scheme? and (ii) would this offering schema make an impact on the market size? The main contribution of this study is to show that pay-per both time and volume pricing is a viable and often preferable alternative to the only time and/or only volume-based offerings for a large number of customers, and that judicious use of such pricing policy is profitable to the network provider

Time and volume based optimal pricing strategies for telecommunication networks

In the recent past, there have been several initiatives by major network providers such as Turk Telekom lead the industry towards network capacity distribution in Turkey. In this study, we use a monopoly pricing model to examine the optimal pricing strategies for “pay-per-volume” and “pay-per-time” based leasing of data networks. Traditionally, network capacity distribution includes short/long term bandwidth and/or usage time leasing. Each consumer has a choice to select volume based pricing or connection time based pricing. When customers choose connection time based pricing, their optimal behavior would be utilizing the bandwidth capacity fully therefore it can cause network to burst. Also, offering pay-per-volume scheme to the consumer provides the advantage of leasing the excess capacity for other potential customers for network provider. We examine the following issues in this study: (i) What are the extra benefits to the network provider for providing the volume based pricing scheme? and (ii) Does the amount of demand (number of customers enter the market) change? The contribution of this paper is to show that pay-per-volume is a viable alternative for a large number of customers, and that judicious pricing for pay-per-volume is profitable for the network provider

A Simple Model of Rail Infrastructure Capacity and Costs.

The recent White Paper on "New Opportunities for the Railways" (Cm 2012, 1992) proposes that British Rail's responsibilities for operation and infrastructure will be separated. A new track authority, Railtrack, will be established and will operate without subsidy, except for capital grants in cases where a satisfactory cost-benefit return is achieved. It is acknowledged that these new arrangements will lead to some difficulties in allocating and charging for infrastructure, especially where rail infrastructure is congested, and consultants have been hired by Government to examine this issue. The principles that Government has specified should underly the access and charging regime are that it should: (a)Promote efficient operation (b)Promote competition and innovation (c)Encourage efficient use of infrastructure and other resources (d)Not discriminate unfairly between competing operators and services (e)Provide the means for financing Railtrack's infrastructure. The relevant theory is embodied in the literature concerning peak load pricing and optimal investment for public enterprises as expounded in standard text books (Turvey, 1971, Rees, 1984, Brown and Sibley, 1986.) and put into practice in most areas of the transport sector (eg Hansson and Nilsson, 1989, for rail, Small and Winston, 1988, for road, Bishop and Thompson, 1992, for air). The aim of this paper is not to make a contribution to this theory but to use it in conjunction with simple models of rail's infrastructure requirements and costs to highlight the key problems in infrastructure allocation and charging. The structure of this paper is as follows. In section two we consider a hypothetical rail line and the likely costs of different service levels. In section three, we relax the assumption that all trains are operated at the same speed and re-examine the likely costs of different service levels. In section 4, we go on to examine the pricing implications of our findings. In a final section, the implications of this analysis for policy are assessed

Content-Specific Broadcast Cellular Networks based on User Demand Prediction: A Revenue Perspective

The Long Term Evolution (LTE) broadcast is a promising solution to cope with exponentially increasing user traffic by broadcasting common user requests over the same frequency channels. In this paper, we propose a novel network framework provisioning broadcast and unicast services simultaneously. For each serving file to users, a cellular base station determines either to broadcast or unicast the file based on user demand prediction examining the file's content specific characteristics such as: file size, delay tolerance, price sensitivity. In a network operator's revenue maximization perspective while not inflicting any user payoff degradation, we jointly optimize resource allocation, pricing, and file scheduling. In accordance with the state of the art LTE specifications, the proposed network demonstrates up to 32% increase in revenue for a single cell and more than a 7-fold increase for a 7 cell coordinated LTE broadcast network, compared to the conventional unicast cellular networks.Comment: 6 pages; This paper will appear in the Proc. of IEEE WCNC 201