Top-percentile traffic routing problem by dynamic programming

Multi-homing is a technology used by Internet Service Provider (ISP) to connect to the Internet via different network providers. To make full use of the underlying networks with minimum cost, an optimal routing strategy is required by ISPs. This study investigates the optimal routing strategy in case where network providers charge ISPs according to top-percentile pricing. We call this problem the Top-percentile Traffic Routing Problem (TpTRP). The TpTRP is a multistage stochastic optimisation problem in which routing decision should be made before knowing the amount of traffic that is to be routed in the following time period. The stochastic nature of the problem forms the critical difficulty of this study. In this paper several approaches are investigated in modelling and solving the problem. We begin by modelling the TpTRP as a multi-stage stochastic programming problem, which is hard to solve due to the integer variables introduced by top-percentile pricing. Several simplifications of the original TpTRP are then explored in the second part of this work. Some of these allow analytical solutions which lead to bounds on the achievable optimal solution. We also establish bounds by investigation several "naive" routing policies. In the end, we explore the solution of the TpTRP as a stochastic dynamic programming problem by a discretization of the state space. This SDP model gives us achievable routing policies on medium size instances of TpTRP, which of course improve the naive routing policies. With a classification of the SDP decision table, a crude routing policy for realistic size instances can be developed from the smaller size SDP model. © 2011 Springer Science+Business Media, LLC

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

Modelling and analysis of Internet pricing and revenue distribution.

Cheung, Yang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 85-89).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 2 --- Related Works --- p.4Chapter 2.1 --- Pricing Mechanisms --- p.4Chapter 2.1.1 --- Current Situation --- p.4Chapter 2.1.2 --- Proposed Pricing Mechanisms --- p.6Chapter 2.1.3 --- Congestion Pricing --- p.9Chapter 2.1.4 --- Bandwidth Allocation Mechanism --- p.10Chapter 2.2 --- Revenue Distribution Mechanisms --- p.12Chapter 2.2.1 --- Current Situation --- p.12Chapter 2.2.2 --- Novel Revenue Distribution Mechanisms --- p.13Chapter 3 --- Problems in Revenue Collecting Stage --- p.16Chapter 3.1 --- Introduction --- p.17Chapter 3.1.1 --- Desirable Characteristics of Internet Pricing Mechanism --- p.19Chapter 3.1.2 --- Existing Solution --- p.21Chapter 3.1.3 --- Applying Insurance into Internet Pricing --- p.22Chapter 3.2 --- The Internet Pricing Model --- p.25Chapter 3.2.1 --- System Model --- p.25Chapter 3.2.2 --- Decisions Time Scales --- p.27Chapter 3.2.3 --- Micro Time Scale Pricing --- p.28Chapter 3.2.4 --- Macro Time Scale Pricing --- p.29Chapter 3.3 --- Actuarially Fair Coinsurance Function --- p.30Chapter 3.3.1 --- The Actuarially Fair Coinsurance Function --- p.32Chapter 3.3.2 --- Properties of the Actuarially Fair Coinsurance Function --- p.34Chapter 3.3.3 --- How Much Insurance Should a User Buy? --- p.35Chapter 3.3.4 --- Numerical Examples --- p.37Chapter 3.4 --- Premium Coinsurance Function --- p.40Chapter 3.4.1 --- Problems of Allowing Pull Insurance --- p.41Chapter 3.4.2 --- The Premium Coinsurance Function --- p.43Chapter 3.4.3 --- Properties of the premium coinsurance function --- p.44Chapter 3.4.4 --- Numerical Example --- p.46Chapter 4 --- Problems in Revenue Distributing Stage --- p.48Chapter 4.1 --- Introduction --- p.50Chapter 4.2 --- System Models --- p.52Chapter 4.2.1 --- Topology Model --- p.52Chapter 4.2.2 --- Traffic Model --- p.54Chapter 4.3 --- Settlement Model and Definition of Fair Price --- p.55Chapter 4.3.1 --- Bilateral Settlement --- p.55Chapter 4.3.2 --- Shapley Settlement --- p.58Chapter 4.4 --- Fair Price Achieving the Shapley Value: The Symmetric Case --- p.61Chapter 4.5 --- Properties of the Fair Prices in the Symmetric Case --- p.65Chapter 4.5.1 --- Sensitivity to traffic pattern α --- p.65Chapter 4.5.2 --- Sensitivity to network topology parame- ters p and d --- p.67Chapter 4.6 --- Fair Price Achieving the Shapley Value: The Asym- metric Case --- p.70Chapter 4.7 --- Distributed and Local Approximation of the Fair Price --- p.71Chapter 5 --- Conclusions --- p.74Chapter A --- Mathematical Proofs --- p.77Chapter A.l --- Mathematical Proof for Chapter 3 --- p.77Chapter A.1.1 --- Proof of Theorem 3.3.2 --- p.77Chapter A.1.2 --- Proof of Proposition 3.3.5 --- p.77Chapter A.1.3 --- Proof of Proposition 3.3.6 --- p.78Chapter A.1.4 --- Proof of Proposition 3.3.7 --- p.78Chapter A.1.5 --- Proof of Proposition 3.4.1 --- p.79Chapter A.1.6 --- Proof of Proposition 3.4.3 --- p.79Chapter A.1.7 --- Proof of Proposition 3.4.5 --- p.80Chapter A.2 --- Mathematical Proof for Chapter 4 --- p.81Chapter A.2.1 --- Proof of Theorem 4.4.2 --- p.81Chapter A.2.2 --- Proof of Theorem (4.6.1) --- p.83Chapter A.2.3 --- Terms Description of Equation (4.1) --- p.84Bibliography --- p.8

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

Life Cycle and Cohort Productivity in Economic Research: The Case of Germany

We examine the research productivity of German academic economists over their life cycles. It turns out that the career-patterns of research productivity as measured by journal publications are characterized by marked cohort effects. Moreover, the life-cycles of younger German economists are hump-shaped and closely resemble the life cycles identified for U.S. economists, whereas the life-cycles of older German economists are much flatter. Finally, we find that not only productivity, but also research quality follows distinct life cycles. Our study employs econometric techniques that are likely to produce estimates that are more trustworthy than previous estimates.research productivity, life cycles, cohort effects

Regulation and welfare efficiency: evidence from China

Along with rapid economic growth, China has started to face the challenge of environment degradation. And, the integration of the Chinese economy into global markets has put pressure on the government to address the issue of intellectual property infringement. The government has begun to enforce copyright protection and address environmental issues by imposing regulatory policies on related markets. In this dissertation, I focus on two policies: the anti-piracy campaign enforcing music copyright protection in 2015 and the vehicle license lottery policy started in 2011 in Beijing. I empirically assess the impact of those regulations on market competition, allocative efficiency and consumer welfare. Copyright enforcement in China since 2015 has heightened competition among music streaming services for obtaining exclusive licenses. The competition is driven by the existence of multi-homing and switching costs for consumers in choosing among services. I specify and estimate a structural model that allows consumers to tradeoff between multi-homing and switching. I use estimates to simulate market outcomes had a compulsory licensing provision been enforced. I find that with compulsory licensing, the market will evolve to a ``tipping'' equilibrium in which all users choose to exclusively subscribe to a same service that is of better quality. Although providing more music content, smaller services would lose significant market shares. This is because multi-homing users of smaller services would switch away from their services when the music content were less differentiated from others. The result suggests that a compulsory provision does not benefit the smaller services and may lead to a higher market concentration. In the next chapter, I study a vehicle license lottery in Beijing. Although the static inefficiency from misallocation under a lottery is well-known, I introduce the concept of a dynamic inefficiency due to agents’ suboptimal timing of entering the lottery. Using a structural empirical model, I find that households on average participate in the lottery system at least four years earlier than they would in a counterfactual environment with no quantity constraint. Dynamic inefficiency accounts for the majority of the welfare loss from using the lottery policy. In the last chapter, I formalize the concept of dynamic inefficiency via a simple theoretical model. I show that, with reasonable assumptions, an equilibrium with dynamic misallocation always exits. Consumers with lower willingness to pay for the resource will enter the lottery early in order to increase the chance of winning, although they may receive a negative utility if they win the lottery before their valuation for the resource increases

Essays on Two-sided Platforms: Market Entry Strategy and Dynamic Pricing

This dissertation consists of two chapters: In the first chapter, we build a theoretical framework to study the dynamic entry interactions between two platforms with homogeneous products into city-based markets. This research is applicable for studying the entry strategies between, for example, Uber and Lyft; Groupon and Living Social, and other business models with the attributes of switching cost, network effect, and segregated markets. We address three questions in this paper: 1) What determines the expansion path of city-based platforms?; 2) What factors are affecting the market concentration structures; and 3) Under what conditions can a second mover become the market leader (with more than 50% of the market share)? We find that a significant degree of the network effect and large switching cost will build a natural barrier for the late entrant; Transaction-efficient markets with larger transaction volume are less likely to be concentrated than transaction-inefficient markets. We take consideration of entry cost and initial fund in our dynamic settings, and find that the uncertainty in market return will make the platforms\u27 expansion path and the final outcome less predictable. However, on average, the capability of capturing the largest market first is crucial for both players; if a platform loses the opportunity of being the first to capture the largest market, it may have to raise a considerable amount of money to overcome its disadvantages in the following competitions. In the second chapter, we empirically investigate the effect of the dynamic pricing system on ride-sharing platform drivers\u27 labor supply. Rather than working-hour and wage-rate relation explored by previous and current literature, we examine the instantaneous response of drivers to price surges. Using data from New York City, we estimate the structural model through a constrained non-parametric instrumental variable (NPIV) approach. We find that the emergence of a price surge is a strong incentive for drivers, and the dynamic pricing scheme of ride-sharing platforms effectively solves the geographical disparity problem of uncoordinated taxi systems. Consequently, the overall accessibility and quantity of pickup service in the entire city will increase. In the absence of dynamic pricing, we show in a counterfactual analysis that platform drivers will be clumped in the Manhattan area and airports, a dilemma shared by the taxi drivers. The counterfactual context implies that 27 % of the total supply will be lost, including a significantly large 59% reduction in the non-Manhattan area

Essays on digital markets

This thesis consists of five essays that relate to digital platforms/markets. The first essay provides a literature survey on digital platform competition and relevant competition policy. The second and the third essays study the indirect effect of software/application quality on hardware/tablet demand in the asymmetric competition between the closed platform (Apple iOS) and the open platform (Google Android), and analyze the effects of possible policies by platforms/regulators. While the second essay develops a theoretical model to set up the predictions, the third essay tests these predictions empirically using product-level data on tablet PCs and applications in five European countries. In a similar context of a closed platform versus an open platform, the fourth essay investigates the trade-off between quality and variety that platforms face when choosing the software quality standard. Finally, the fifth essay examines the merger strategies and evaluates the ex-post effects of mergers and acquisitions on innovation measured by patents in the cloud computing market