Non-cooperative Case Research Articles

Strategic Interaction Between Government and Central Bank in Framework of Cooperative and Non-Cooperative Games

In this paper we analyzed the strategic interaction between government and central bank in Iranian economy. Using dynamic differential games and Nash equilibrium within cooperative and non-cooperative setting, we try to find the optimal values of debt, deficit and monetary base. The results of simulation show that in cooperative case the level of equilibrium debt is lower than the non-cooperative case and converge speed is higher in cooperative setting than non-cooperation setting. Also in cooperative case than non-cooperative case, less creation of money and less government deficit are needed for debt stabilization in long run. The results also show that in both cooperative and non-cooperative cases under uncertainty, more active policies are used to track debt to its equilibrium level. These active policies lead debt goes to smaller level.

Supply Chain Cooperation with Price-Sensitive Demand and Environmental Impacts

In this paper, we consider a two-echelon sustainable supply chain with price-sensitive demand. The government taxes the carbon footprint of each item caused by producing, transporting, and consuming the products. Both the supplier and retailer can exert efforts to reduce the carbon footprint. In a non-cooperative setting, the government only taxes the supplier, so that the retailer has no incentive to exert any effort to reduce the carbon footprint and the supplier merely decides on the selling price to maximize its own profit. We develop a centralized supply chain and show that there is an optimal solution to maximize the channel profit. Since the centralized policy may not be always not practical, we propose a tax-sharing contract, where both parties profit from the carbon footprint reduction. This problem is modeled as the Stackelberg game and Nash game. The results show that the leader has more power than the follower, which results in more profit. The Stackelberg game provides boundaries for both parties’ profits in the Nash game. Although the tax-sharing contract does not result in full cooperation, its efficiency is still much higher than that of the non-cooperative case. The results are illustrated with some numerical experiments.

Scenario-based dynamic negotiation for the coordination of multi-enterprise supply chains under uncertainty

A novel scenario-based dynamic negotiation approach is proposed for the coordination of decentralized supply chains under uncertainty. The relations between the involved organizations (client, provider and third parties) and their respective conflicting objectives are captured through a non-zero-sum and non-symmetric roles SBDN negotiation. The client (leader) designs coordination agreements considering the uncertain reaction of the provider (follower) resulting from the uncertain nature of the third parties, which is modeled as a probability of acceptance function. Different negotiation scenarios are studied: (i) cooperative, and (ii) non-cooperative and (iii) standalone cases. The use of the resulting models is illustrated through a case study with different vendors around a “leader” (client) in a decentralized scenario. Although the usual cooperation hypothesis will allow higher overall profit expectations, using the proposed approach it is possible to identify non-cooperative scenarios with high individual profit expectations which are more likely to be accepted by all individual partners.

On a Class of Hybrid Differential Games

This paper is intended to present a systematic application of the hybrid systems framework to differential games. A special class of bimodal linear-quadratic differential games is presented and illustrated with examples; two particular classes of switching rules, time-dependent and state-dependent switches are discussed. The main contribution of the paper consists in formulating necessary optimality conditions for determining optimal strategies in both cooperative and non-cooperative cases. A practically relevant hybrid differential game of pollution reduction is considered to illustrate the developed framework.

Relative dynamics estimation of non-cooperative spacecraft with unknown orbit elements and inertial tensor

The state estimation for relative motion with respect to non-cooperative spacecraft in rendezvous and docking (RVD) is a challenging problem. In this paper, a completely non-cooperative case is considered, which means that both orbit elements and inertial tensor of target spacecraft are unknown. By formulating the equations of relative translational dynamics in the orbital plane of chaser spacecraft, the issue of unknown orbit elements is solved. And for the problem for unknown inertial tensor, we propose a novel robust estimator named interaction cubature Kalman filter (InCKF) to handle it. The novel filter consists of multiple concurrent CKFs interlacing with a maximum a posteriori (MAP) estimator. The initial estimations provided by the multiple CKFs are used in a Bayesian framework to form description of posteriori probability about inertial tensor and the MAP estimator is applied to giving the optimal estimation. By exploiting special property of spherical-radial (SR) rule, a novel method with respect to approximating the likelihood probability of inertial tensor is presented. In addition, the issue about vision sensor’s location inconformity with center mass of chaser spacecraft is also considered. The performance of this filter is demonstrated by the estimation problem of RVD at the final phase. And the simulation results show that the performance of InCKF is better than that of extended Kalman filter (EKF) and the estimation accuracy of pose and attitude is relatively high even in the completely non-cooperative case.

Cooperative Energy-Efficient Content Dissemination Using Coalition Formation Game Over Device-to-Device Communications

This paper investigates the problem of cooperative energy-efficient content dissemination among a number of cellular user equipments (UEs), with the assumption that these UEs are seeking to receive the same content from a common wireless access point, such as an eNodeB. We formulate the problem as a nontransferable utility coalition formation game, in which a utility function is characterized by taking into consideration energy efficiency and mutual interference in the scenario of proximity device-to-device (D2D) communications. Then, we develop a distributed coalition formation algorithm. With the proposed algorithm, UEs that are in proximity of each other can cooperate and then self-organize into independent disjoint coalitions, and finally minimize overall network energy consumption using D2D communications. The simulation results show that using the proposed algorithm, significant energy savings can be achieved compared with the cellular multicasting case and the noncooperative case, which validates the high energy efficiency of the proposed game-theory-based algorithm in wireless content dissemination scenarios.

Cooperative Joint Synchronization and Localization in Wireless Sensor Networks

In this paper, cooperative sensor localization using asynchronous time-of-arrival measurements is investigated. It is well known that localization performance in wireless networks using time-based ranging or pseudo-ranging methods is greatly affected by the accuracy of the timing synchronization between the nodes involved in the estimation. Commonly, the original estimation problem is broken down into two subproblems, the synchronization problem and the localization problem, in what is known as a two-step approach. However, in this paper, the joint synchronization and localization problem is considered and examined for use in cooperative networks. It is discussed that the cooperation between the source nodes eliminates the need for high anchor node densities and improves localization performance significantly. Furthermore, the Cramer-Rao lower bounds (CRLB) and the maximum likelihood (ML) estimator are derived. It is shown that the ML estimator is highly nonlinear and nonconvex and must, therefore, be solved by using computationally complex algorithms. In order to reduce the complexity of the estimation, a novel semidefinite programming (SDP) relaxation method is developed by relaxing the original nonconvex ML problem, in such a way as to reformulate the estimation problem as a convex problem. The performance of the proposed SDP method is shown through computer simulations to nearly equal that of the ML estimator. The approach is also applied to the noncooperative case where it is found to be superior in performance than the previously proposed suboptimal estimators. Finally, complexity analyses are included for the estimators under consideration.

Spectrum Coordination in Energy-Efficient Cognitive Radio Networks

Device coordination in open spectrum systems is a challenging problem, particularly since users experience varying spectrum availability over time and in different locations. In this paper, we propose a game-theoretic approach that allows cognitive radio (CR) pairs, namely the primary user (PU) and the secondary user (SU), to update their transmission power and frequencies simultaneously. Specifically, we address a Stackelberg game model in which individual users attempt to hierarchically access to the wireless spectrum while maximizing their energy efficiency. A thorough analysis of the existence, uniqueness, and characterization of the Stackelberg equilibrium (SE) is conducted. In particular, we show that a spectrum coordination naturally occurs when both actors in the system decide sequentially about their power and their transmitting carriers. As a result, spectrum sensing in such a situation turns out to be a simple detection of the presence/absence of a transmission on each subband. We also show that when users experience very different channel gains on their two carriers, they may choose to transmit on the same carrier at the SE as this contributes enough energy efficiency to outweigh the interference degradation caused by the mutual transmission. Then, we provide an algorithmic analysis on how the PU and the SU can reach such a spectrum coordination using an appropriate learning process. We validate our results through extensive simulations and compare the proposed algorithm to some typical scenarios, including the non-cooperative case in the work of Meshkati et al. and the throughput-based-utility systems. Typically, it is shown that the proposed Stackelberg decision approach optimizes the energy efficiency while still maximizing the throughput at the equilibrium.

Decentralized Manufacturing Supply Chains Coordination under Uncertain Competitiveness

The coordination of decentralized multi-product manufacturing SCs is achieved through negotiations based on expected win-win principles in an uncertain competitive environment. Based on non-symmetrical roles of the different actors, the client (as leader) is supposed to propose coordination contracts according to its best expected conditions, taking into account the uncertain reaction of the provider (follower). This uncertain reaction is modeled as a probability of acceptance, computed according to the overall scenario conditions, which include the presence of 3rd parties. Different negotiation scenarios are analyzed considering cooperative and non-cooperative cases. The resulting MINLP tactical models are illustrated using a case study with different providers (follower SC) around a client (leader SC) interacting in a global decentralized scenario. The negotiations based on non-cooperative cases proves to identify the situation with higher independent profit expectations. Moreover, the proposed approach shows the importance of considering the uncertainty associated with the response of the follower to the leader's decisions, resulting from a wider knowledge of its options.

Coordinated Multipoint Joint Transmission in Heterogeneous Networks

Motivated by the ongoing discussion on coordinated multipoint in wireless cellular standard bodies, this paper considers the problem of base station cooperation in the downlink of heterogeneous cellular networks. The focus of this paper is the joint transmission scenario, where an ideal backhaul network allows a set of randomly located base stations, possibly belonging to different network tiers, to jointly transmit data, to mitigate intercell interference and hence improve coverage and spectral efficiency. Using tools from stochastic geometry, an integral expression for the network coverage probability is derived in the scenario where the typical user located at an arbitrary location, i.e., the general user, receives data from a pool of base stations that are selected based on their average received power levels. An expression for the coverage probability is also derived for the typical user located at the point equidistant from three base stations, which we refer to as the worst case user. In the special case where cooperation is limited to two base stations, numerical evaluations illustrate absolute gains in coverage probability of up to 17% for the general user and 24% for the worst case user compared with the noncooperative case. It is also shown that no diversity gain is achieved using noncoherent joint transmission, whereas full diversity gain can be achieved at the receiver if the transmitting base stations have channel state information.

Cooperative and non-cooperative Nash solution for linear supply function equilibrium game

In this paper, the competition among suppliers in an oligopolistic market is studied using supply function equilibrium model. Since the decision of each supplier affects other suppliers’ profit, we have a game problem rather than optimization. In the proposed supply function equilibrium game, both of the cooperative and non-cooperative behaviors of the players are studied. In non-cooperative case, the Nash equilibrium point of the game is obtained for n-player in constant and price sensitive demand cases. In cooperative game, the concept of Nash bargaining solution is utilized to study the collusive behavior of the suppliers. The convexity and closeness of the players’ payoff set in supply function equilibrium model is proved as the necessary and sufficient condition for existence and uniqueness of the solution and then, the mathematical formulation of Nash bargaining solution is derived. In addition, the cooperative and non-cooperative behavior of players is compared over a wide range of parameters in a case study.

A DIFFERENTIAL GAME OF POLLUTION CONTROL WITH OVERLAPPING GENERATIONS

We formulate an overlapping generations model on optimal emissions with continuous age structure. We compare the noncooperative solution to the cooperative one and obtain fundamental differences in the optimal strategies. Also including an altruistic motive does not avoid the problem of the myopic noncooperative solution. Finally we define a time-consistent tax scheme to obtain the cooperative solution in the noncooperative case.

Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a novel approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing work which assumes that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as a coalition formation game with overlapping coalitions. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed overlapping coalition formation game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions.

Stereovision-Based Estimation of Relative Dynamics Between Noncooperative Satellites: Theory and Experiments

Estimating the relative pose and motion of cooperative satellites using on-board sensors is a challenging problem. When the satellites are noncooperative, the problem becomes even more complicated, as there might be poor a priori information about the motion and structure of the target satellite. In this paper, the mentioned problem is solved by using only visual sensors, which measurements are processed through robust filtering algorithms. Using two cameras mounted on a chaser satellite, the relative state with respect to a target satellite, including the position, attitude, and rotational and translational velocities, is estimated. The new approach employs a stereoscopic vision system for tracking a set of feature points on the target spacecraft. The perspective projection of these points on the two cameras constitutes the observation model of an iterated extended Kalman filter (IEKF) estimation scheme. Using new theoretical results, the information contained in the visual data is quantified using the Fisher information matrix. It is shown that, even in the noncooperative case, there is information that can be extracted pertaining to the relative attitude and target structure. Finally, a method is proposed for rendering the relative motion filtering algorithm robust to uncertainties in the target's inertia tensor. This is accomplished by endowing the IEKF with a maximum a posteriori identification scheme for determining the most probable inertia tensor from several available hypotheses. The performance of the new filtering algorithm is validated by Monte-Carlo simulations. Also a preliminary experimental evaluation is provided.

Improving Macrocell-Small Cell Coexistence Through Adaptive Interference Draining

The deployment of underlay small base stations (SBSs) is expected to significantly boost the spectrum efficiency and the coverage of next-generation cellular networks. However, the coexistence of SBSs underlaid to a macro-cellular network faces important challenges, notably in terms of spectrum sharing and interference management. In this paper, we propose a novel game-theoretic model that enables the SBSs to optimize their transmission rates by making decisions on the resource occupation jointly in the frequency and spatial domains. This procedure, known as interference draining, is performed among cooperative SBSs and allows to drastically reduce the interference experienced by both macro- and small cell users. At the macrocell side, we consider a modified water-filling policy for the power allocation that allows each macrocell user (MUE) to focus the transmissions on the degrees of freedom over which the MUE experiences the best channel and interference conditions. This approach not only represents an effective way to decrease the received interference at the MUEs but also grants the SBS tier additional transmission opportunities and allows for a more agile interference management. Simulation results show that the proposed approach yields significant gains at both macrocell and small cell tiers, in terms of average achievable rate per user, reaching up to 37%, relative to the non-cooperative case, for a network with 150 MUEs and 200 SBSs.

Optimal Scheduling and Power Allocation in Cooperate-to-Join Cognitive Radio Networks

In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary system's performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

A macroeconomics perspective on international coordination in sales taxes

This article investigates how international coordination vis-à-vis sales tax policies affects the welfare of participating countries. A country's tax policies have asymmetric effects on the pricing behaviors of domestic and overseas producers. International cooperation endogenizes the externality that improves the purchasing power of foreign residents, but at the cost of its own residents’ work efforts. The first-best taxes are lower than in the noncooperative case. When world welfare is utilitarian, smaller economies may experience welfare losses from cooperation under the weak income effect of sales tax. We propose a coordinated tax rule that all countries agree to employ.

Opportunistic Spectrum Access for Cognitive Radio Networks withMultiple Secondary Users

We develop channel-aware opportunistic spectrum access strategies for cognitive radio networks consisting of multiple secondary users. In addition to balancing between the cost of foregoing weaker transmission opportunities in pursuit of channels that offer better data rates, we consider collisions between primary users (PUs) and secondary users (SUs), and among the SUs themselves. We derive strategies for both cooperative setting where SUs maximize their sum total of throughputs, as well as non-cooperative, game theoretic setting where each SU tries to maximize its own throughput. We show that the optimal schemes for both scenarios are pure threshold policies, where each SU decides to use or skip transmission opportunities by comparing the channel qualities to a fixed threshold. In the non-cooperative case, we establish the existence of Nash equilibrium and develop best response strategies that can converge to equilibria, with SUs relying only on their local observations. We study the tradeoff between maximal throughput in cooperative setting and fairness in the non-cooperative setting, and schemes based on utility functions and pricing that mitigate this tradeoff. Lastly, we analyze the issue of interference of SUs to the PUs. We show numerical results illustrating the schemes and analysis.

Interference Alignment for Cooperative Femtocell Networks: A Game-Theoretic Approach

The use of small cells serviced by low-power base stations such as femtocells is envisioned to improve the spectrum efficiency and the coverage of next-generation mobile wireless networks. However, one of the major challenges in femtocell deployments is managing interference. In this paper, we propose a novel cooperative solution that enables femtocells to improve their achievable data rates, by suppressing intratier interference using the concept of interference alignment (IA). We model this cooperative behavior among the femtocells as a coalitional game in partition form and we propose a distributed algorithm for the coalition formation. The proposed algorithm allows the femtocell base stations to independently decide on whether to cooperate or not, while maximizing a utility function capturing both the gains and costs from cooperation. Using the proposed algorithm, the femtocells can self-organize into a stable network partition composed of disjoint femtocell coalitions and which constitutes the recursive core of the game. Inside every coalition, cooperative femtocells use advanced IA techniques to improve their downlink transmission rate. Simulation results show that the proposed coalition formation algorithm yields significant gains, in terms of average payoff per femtocell, reaching up to 30 percent relative to the noncooperative case for a network of N=300 femtocells.

Cooperative advertising and pricing models in a dynamic marketing channel

This paper studies dynamic pricing and cooperative advertising strategies in a two-member channel of distribution. We propose differential game models that incorporate the carryover effects of advertising over time for both manufacturer and retailer. Two different models are studied. Firstly, we discussed a non-cooperative differential game which the manufacturer supports partially the cost of the promotion activities. Secondly, both channel members cooperatively determine their respective strategies. In these two cases, dynamic cooperative advertising and pricing strategies are characterized. Then numerical simulation method is applied to analyze the sensitivity of main model parameters and compare the main results of the two models. The simulation results show that the cooperative model achieves better coordination than the non-cooperative model with the following features: all channel members achieve higher advertising efforts and profit level in the cooperative case rather than in the non-cooperative case.