The effect of (non-)competing brokers on the quality and price of differentiated internet services

Price war, as an important factor in undercutting competitors and attracting customers, has spurred considerable work that analyzes such conflict situation. However, in most of these studies, quality of service (QoS), as an important decision-making criterion, has been neglected. Furthermore, with the rise of service-oriented architectures, where players may offer different levels of QoS for different prices, more studies are needed to examine the interaction among players within the service hierarchy. In this paper, we present a new approach to modeling price competition in (virtualized) service-oriented architectures, where there are multiple service levels. In our model, brokers, as intermediaries between end-users and service providers, offer different QoS by adapting the service that they obtain from lower-level providers so as to match the demands of their clients to the services of providers. To maximize profit, players, i.e. providers and brokers, at each level compete in a Bertrand game while they offer different QoS. To maintain an oligopoly market, we then describe underlying dynamics which lead to a Bertrand game with price constraints at the providers’ level. We also study cooperation among a subset of brokers. Numerical simulations demonstrate the behavior of brokers and providers and the effect of price competition on their market shares.Accepted manuscrip

Privatization in oligopoly : the impact of the shadow cost of public funds

The aim of this paper is to investigate the welfare eect of privatization in oligopoly when the government takes into account the distortionary eect of rising funds by taxation (shadow cost of public funds). We analyze the impact of the change in ownership not only on the objective function of the rms, but also on the timing of competition by endogenizing the determination of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games. We show that, absent effciency gains, privatization never increases welfare. Moreover, even when large effciency gains are realized, an ineffcient public rm may be preferred

Mixed duopoly, privatization and the shadow costs of public funds

The purpose of this article is to investigate how the introduction of the shadow cost of public funds in the utilitarian measure of the economywide welfare affects the behavior of a welfare maximizer public firm in a mixed duopoly. We prove that when firms play simultaneously, the mixed-Nash equilibrium can dominate any Cournot equilibria implemented after a privatization, with or without efficiency gains. This can be true both in terms of welfare and of public firm's profit. When we consider endogenous timing, we show that either mixed- Nash, private leadership or both Stackelberg equilibria can result as subgameperfect Nash equilibria (SPNE). As a consequence, the sustainability of sequential equilibria enlarges the subspace of parameters such that the market performance with an inefficient public firm is better than the one implemented after a full-efficient privatization. Absent efficiency gains, privatization always lowers welfare

Privatization in oligopoly : the impact of the shadow cost of public funds

The aim of this paper is to investigate the welfare effect of privatization in oligopoly when the government takes into account the distortionary effect of raising funds by taxation (shadow cost of public funds). We analyze the impact of the change in ownership not only on the objective function of the firms, but also on the timing of competition by endogenizing the determination of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games. We show that, absent efficiency gains, privatization never increases welfare. Moreover, even when large efficiency gains are realized, an inefficient public firm may be preferred

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Multi-attribute demand characterization and layered service pricing

As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare

Method and Approach Mapping of Fair and Balanced Risk and Value-added Distribution in Supply Chains: A Review and Future Agenda

This paper proposes a fair and balanced risk and value-added distribution as a novel approach for collaborative supply chain. The objective of this article is to analyze the existing methods and approaches for risk management, value-adding, risk and revenue sharing to develop a new framework for balancing risk and value-adding in collaborative supply chains. The authors reviewed and synthesized 162 scientific articles which were published between 2001 and 2017 and. The reviewed articles were categorized into supply chain management and performance, risk management, value-added, fair risk and value-added distribution and supply chain negotiation. The potentials identified for future research were the importance of decision-making and sustainability for effectiveness of supply chain risk management. Most previous authors have applied an approach of revenue and risk-- sharing with both decentralized and centralized supply chains to achieve the fair risk and value-added distribution. The dominant methods we found in literature were game theory and complex mathematical formulation. Most literature focused on operation research techniques. We identified a lack of discussion of the intelligent system approach and a potential for future exploration. This paper guide future research and application agenda of fair risk and value-added distribution in supply chain collaboration. We developed a new framework for a fair and balanced risk and value-added distribution model. For a future agenda, we point towards the development of a systematic intelligent system applying soft-computing techniques and knowledge transfer for maintaining sustainable supply chains.Keywords Supply chain collaboration, Fair risk and value-added distribution, Revenue sharing, Risk management, Risk sharin

Equilibrium Modeling and Policy Analysis of a Biofuel Supply Chain with a Hydroelectric Reservoir

This research proposesd a game theoretic model of a biofuel supply chain (BSC) where a utility company supplies reservoir water to two farmers, located in downstream and upstream of a hydropower dam. The decision-making process of the model is formulated as a three-stage Stackelberg game. We analyze the equilibrium of the decentralized systems and the effect of the government subsidy on energy crop (switchgrass) production for cellulosic biofuel industries, with two forms of subsidy: (1) discriminated subsidies and (2) equalized subsidies. The results show that both forms of subsidy improve social welfare in the BSC unless the amount of subsidy exceeds certain limits, in which case there are negative margins for the farmers, and disappearance or monopoly of the markets. Increasing the subsidy to the upstream farmer is more efficient in improving social welfare than equalizing the subsidies to the two farmers. Increasing the subsidy to the downstream farmer shows the least efficiency in improving social welfare

QoS based Route Management in Cognitive Radio Networks

Cognitive radio networks are smart networks that automatically sense the channel and adjust the network parameters accordingly. Cognitive radio is an emerging technology that enables the dynamic deployment of highly adaptive radios that are built upon software defined radio technology. The radio technology allows the unlicensed operation to be in the licensed band. The cognitive radio network paradigm therefore raises many technical challenges such as the power efficiency, spectrum management, spectrum detection, environment awareness, the path selection as well as the path robustness, and security issues. Traditionally, in the routing approaches in the wired network, each node allows a maximum load through the selected route while traditionally in the routing approaches in wireless network, each node broadcasts its request with the identification of the required destination. However, the existing routing approaches in cognitive radio networks (CRN) follow the traditional approaches in wireless network especially those applied for ad hoc networks. In addition, these traditional approaches do not take into account spectrum trading as well as spectrum competition among licensed users (PUs). In this thesis, a novel QoS based route management approach is proposed by introducing two different models; the first model is without game theory and the second model is with game theory. The proposed QoS routing algorithm contains the following elements: (i) a profile for each user, which contains different parameters such as the unlicensed user (secondary user, SU) identification, number of neighbors, channel identification, neighbor identification, probabilities of idle slots and the licensed user (primary user, PU) presence. In addition, the radio functionality feature for CRN nodes gives the capability to sense the channels and therefore each node shares its profile with the sensed PU, which then exchanges its profile with other PUs, (ii) spectrum trading, a PU calculates its price based on the SU requirements, (iii) spectrum competition, a new coefficient α is defined that controls the price because of competition among PUs and depends on many factors such as the number of primary users, available channels, and duration of the usage, (iv) a new function called QoS function is defined to provide different levels of quality of service to SUs, and (v) the game theory concept adds many features such as the flexibility, the dynamicity in finding solutions to the model and the dynamic behaviors of users. Based on the previous elements, all possible paths are managed and categorized based on the level of QoS requested by SUs and the price offered by the PU. The simulation results show that the aggregate throughput and the average delay of the routes determined by the proposed QoS routing algorithm are superior to existing wireless routing algorithms. Moreover, network dynamics is examined under different levels of QoS

Reverse logistics applied to E-commerce: A Systematic Literature Review on Methods, Applications, and Trends for a Virtual Sustainable Market / Logística reversa aplicada ao comércio eletrônico: uma revisão sistemática da literatura sobre métodos, aplicações e tendências para um mercado virtual sustentável

The digital transformation of society, strengthened by the social isolation resulting from the COVID-19 pandemic, boosted sales and returns of products in e-commerce. In this sense, reverse logistics in e-commerce (RLec) has become essential to meet environmental legislation and consumer expectations, which evaluate exchange policies on new purchases. In this sense, this article presents a systematic review of the literature and content analysis, from 2009 to 2019, to identify methods of decision making and applications in RLec. Thus, 261 publications were selected, of which 92 met the search criteria related to reverse logistics and only 7 applied to e-commerce. In view of this, the main applications involved network design (26%), remanufacturing (21%) and outsourcing (16%), aiming at reducing costs and identifying barriers in reverse operations. Finally, artificial intelligence for decision making was identified as a competitive differential in reducing the complexity and subjectivity of LRec problems