Bargaining Game Approach Research Articles

Dynamic bargaining game DEA carbon emissions abatement allocation and the Nash equilibrium

Allocating carbon emissions abatement (CEA) is crucial for reducing carbon footprint of organizations (or decision-making units, DMUs) and mitigating global warming. However, discrepancies often arise among DMUs regarding CEA allocation. This study introduces a dynamic bargaining game approach to address the issue under the nonparametric production frontier analysis framework. Our approach employs an iterative process, allowing each DMU to propose its individual preferred CEA allocation proposal in each iteration. Subsequently, dynamic negotiations among the DMUs occur, leading to the eventual consensus on the CEA allocation result. Our theoretical analysis demonstrates that, at the end of the dynamic bargaining game, all DMUs will converge on the same CEA allocation result, termed as the consensus CEA allocation result. This agreement is reached despite each DMU customizing its individual CEA allocation proposal to suit its own interests. Moreover, we prove that this consensus CEA allocation result represents a Nash equilibrium solution, thereby ensuring its stability and acceptance among all DMUs. Finally, we provide a simple numerical example and a case study of CEA allocation across 27 European Union countries to illustrate the usefulness of our approach and compare it with prior CEA allocation approaches.

Power Allocation for Cooperative Localization in Decentralized Distributed Radar Networks: A Nash Bargaining Game Approach

Power Allocation for Cooperative Localization in Decentralized Distributed Radar Networks: A Nash Bargaining Game Approach

Distributed cooperation optimization of multi-microgrids under grid tariff uncertainty: A nash bargaining game approach with cheating behaviors

Multi-microgrid system (MMGs) has drawn extensive attention recently because of its high energy efficiency. However, MMGs’ operational efficiency can be affected by market price fluctuations and intermittent renewable energy. This paper proposes an energy-sharing model based on the Nash bargaining game between multi-microgrids. The proposed model provides a robust energy trading schedule to deal with uncertainties brought by grid tariffs and renewable energy. To ensure the model is tractable, the original game problem is equivalently converted into a system benefit maximization subproblem and an additional profit distribution subproblem to get optimal energy sharing power and prices. In addition, microgrid has the motivation to cheat for maximizing its benefits which may lead to the breakdown of cooperation. Furthermore, cheating behaviors in energy transaction are analyzed; the energy sharing scheme based on cheating equilibrium is derived by proposing an intermediary transaction mode. Finally, the alternating direction method of multipliers (ADMM) is used to protect the players’ privacies in a distributed way. Simulation results show that the proposed model can realize stable cooperation, effectively reduce operating costs and immunize against multiple uncertainties and cheating behaviors.

MBP: Multi-channel broadcast proxy re-encryption for cloud-based IoT devices

The broadcast proxy re-encryption methods extend traditional proxy re-encryption mechanisms, where a single user shares the cloud data with multiple receivers. When the sender has different data to share with different sets of users, the existing broadcast proxy re-encryption schemes allow him/her to calculate distinct re-encryption keys for different groups in terms of additional computation time and overhead. To overcome these issues, we propose a scheme, named MBP, in IoT application scenario that allows the sender to calculate a single re-encryption key for all the groups of users, i.e., IoT devices. We use the multi-channel broadcast encryption concept in the broadcast proxy re-encryption method, so as single re-encryption key calculation and single re-encryption are done for different groups of IoT devices. It reduces the size of the security elements. However, it increases the computation time of the receiver IoT devices at the time of decryption of both the ciphertexts. To address this issue, we use the Rubinstein-Ståhl bargaining game approach. MBP is secure under a selective group chosen-ciphertext attack using the random oracle model. The implementation of MBP shows that it reduces the communication overhead from the data owner to the cloud server and from the cloud server to the receiver than existing algorithms.

Cooperative Cruise Control for Intelligent Connected Vehicles: A Bargaining Game Approach

Intelligent transportation systems (ITSs) are at the forefront of advancements in transportation, offering enhanced efficiency, safety, and environmental friendliness. To enable ITSs, autonomous systems play a pivotal role, contributing to the development of autonomous driving, data-driven modeling, and multiagent control strategies to establish sustainable and coordinated traffic management. The integration of networked and automated vehicles has garnered significant attention as a potential solution for alleviating traffic congestion and improving fuel economy, achieved through global route optimization and cooperative driving. This study focuses on a predictive control perspective to address the cooperative cruise control problem. Online decision making is employed during the driving process, utilizing information gathered from the network. By employing bargaining games to establish an operating agreement among vehicles, we formalize a synchronization approach based on predictive control theory. Ultimately, these findings are put to the test in an emulation environment within a hardware-in-the-loop system. The results revealed that the proposed cruise control successfully achieved convergence toward the desired reference signal. These results demonstrate the effectiveness of our approach in achieving synchronized platoon behavior and correct bargaining outcomes. These findings underscore the effectiveness and potential of DMPC with bargaining games in coordinating and optimizing vehicular networks. This paves the way for future research and development in this promising area.

Multiobjective Coordinated Operation for Multienergy Hubs: A Bargaining Game Approach

The energy hub (EH) is an essential part of the energy conversion supply process, which integrates multienergy forms to improve energy efficiency effectively. A multiobjective optimization model, including exergy loss and operation cost, is established to balance the efficiency of energy utilization and cost. Due to EH and users being connected by a thermal network, a thermal network model that considers the time delay and loss is established. An interaction framework based on the bargaining game theory is proposed to realize the coordination between EHs and heat station (HS). Furthermore, models, such as thermal exergy, thermal network, and bargaining game goal, are linearized. It dramatically reduces the computational cost and can easily be solved by Cplex or Gurobi. Finally, numerical studies with three EHs and one HS demonstrate the effectiveness of the proposed method. In the case study, the overall cost of EH and HS can be reduced by 2.60% through the interaction of electric power, thermal power, and exergy loss.

Efficiency evaluation of sustainability indicators in a two-stage network structure: a Nash bargaining game approach

Efficiency evaluation of sustainability indicators in a two-stage network structure: a Nash bargaining game approach

A New Two-Stage Bargaining Game Approach for Intra- and Inter-WBAN Management

The Internet of Medical Things (IoMT) is an amalgamation of smart devices to operate the wireless body area network (WBAN) by using networking technologies. To reduce the burden on WBANs, they link to the mobile edge computing (MEC), on which captured medical data can be stored and analyzed. In this paper, we design a new control scheme to effectively share the limited computation and communication resources in the MEC-assisted WBAN (M-W) platform. Based on the bargaining game theory, our proposed scheme explores the mutual benefits of intra- and inter-WBAN interactions. To dynamically adapt the current system conditions, we shape each WBAN’s aspirations to reach a reciprocal consensus for different application services. Utilizing two control factors, we provide a unifying framework for the study of intra- and inter-WBAN bargaining problems to share the limited system resource. Based on the feasibility and real-time effectiveness, the main novelty of the proposed scheme is the ability to achieve a relevant tradeoff between efficiency and fairness through the interactive bargaining process. At last, the experimental results show that the proposed scheme achieves substantial performance improvements to the comparison schemes.

Efficiency decomposition in a three-stage network structure: Cooperative DEA, Nash bargaining game models and conic relaxations

In this paper, for evaluating the efficiency in a three-stage DEA structure we use the additive and the multiplicative cooperative models that comply with the cooperation paradigm in the organizations, where for improving efficiency of system, stages cooperate together. Since the overall efficiency from the cooperative models may not be unique and consequently the stages’ efficiencies, then we combine them with the Nash bargaining game approach that besides maximizing efficiency scores for stages and the whole system, provides a unique and fair efficiency decomposition. Second order programming relaxation of the proposed nonlinear models are given in contrast to the parametric linear models in the literature. Finally, the effectiveness of the proposed models are illustrated with two numerical examples.

4G/5G coexistent dynamic spectrum sharing scheme based on dual bargaining game approach

In this paper, we propose a novel dynamic spectrum sharing (DSS) scheme while satisfying diversified service requirements in the 4G/5G coexisting network platform. To design our scheme, we adopt the basic idea of the cooperative game theory and different allocation rules. In a cooperative game manner, our proposed scheme is formulated as a two-level dual bargaining model. This hierarchical approach can effectively share the limited wireless spectrum resource to significantly reduce the computational complexity and control overheads. To give mutual advantages for network agents, we leverage the full synergy of 4G/5G coexisting infrastructure while maximize the system efficiency. Finally, simulation results show the advantages of the proposed approach by comparing with the existing state-of-the-art spectrum sharing protocols. Specifically, the throughput, payoff and service provision are improved by about 5%, 10% and 10%, respectively, under dynamically changing network system environments. It is worth noting that our dual bargaining based strategic control mechanism facilitates the coordination and cooperation among network entities to effectively allocate the limited spectrum resource. This property can improve the performance measures in the 4G/5G coexisting platform.

Allocating a fixed cost across decision-making units with undesirable outputs: A bargaining game approach

Allocating a fixed cost among a set of peer decision-making units (DMUs) is one of the most important applications of data envelopment analysis. However, almost all existing studies have addressed the fixed cost allocation (FCA) problem within a traditional framework while ignoring the existence of undesirable outputs. Undesirable outputs are neither scarce in various production activities in real world applications nor trivial in efficiency evaluation and subsequent decision making. Motivated by this observation, this article attempts to explicitly extend the traditional FCA problem to situations in which DMUs are necessarily involved with undesirable outputs. To this end, we first investigate the efficiency evaluation of DMUs considering undesirable outputs based on the joint weak disposability assumption. Then, flexible FCA schemes are considered to revisit the efficiency evaluation process. The results show that feasible allocation schemes exist such that all DMUs can be simultaneously efficient. Furthermore, we define the comprehensive satisfaction degree and develop a satisfaction degree bargaining game approach to determine a unique FCA scheme. Finally, the proposed approach is tested with an empirical study of banking activities based on real conditions.

Ramp metering control under stochastic capacity in a connected environment: A dynamic bargaining game theory approach

This paper presents a dynamic predictive and cooperative ramp metering approach that considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is used to estimate traffic state parameters based on speed, location, and travel time information from connected vehicles. Traffic state predictions are obtained on a lane by lane basis using an adaptive Kalman filter (AKF) that fuses fixed detector measurements with the model; the AKF then produces multiple step ahead predictions. The ramp metering problem in this paper is modeled as a stochastic distributed model predictive control (SDMPC) approach. The SDMPC problem is solved based on a bargaining game approach where each controller, a player in the game, receives traffic state and control decision information from other controllers to solve the local optimization problem based on expected local costs and constraints. The performance of the proposed model is evaluated for three aspects of efficiency: short-term and long-term equity and effectiveness compared to multiple control scenarios. The outcomes indicate that the proposed cooperative model with stochastic capacity considerations outperforms the deterministic capacity-based models in regard to effectiveness and equity properties. However, the centralized approach performs slightly better in respect to system-wide efficiency.

Cooperative Inter-ISP Traffic Control Scheme Based on Bargaining Game Approach

New applications in 5G networks are becoming very popular, and internet service providers (ISPs) may take the opportunity to provide various services to their clients. Today, one of the challenging problems facing ISPs is how the profitability can be increased while maintaining an excellent network performance. In this work, we seek to understand the fundamental issues on interactions among ISPs, and design a novel inter-ISP traffic control scheme to take full advantage of network resource sharing. Our approach explores the impact of ISP peering relationship and bandwidth allocation to maximize the total system revenue. Specifically, we identify the special properties of ISPs' interactions, and employ the concept of Yu bargaining solutions to address the ISP traffic control problem. This bargaining approach minimizes the Euclidean distance between the ISP's utopia payoff and the feasible solution set to provide a fair-efficient solution. The main novelty of our proposed scheme adaptively handling contradictory requirements while maximizing the network performance. To verify the benefits of our idea, we show the correctness of our scheme through carrying out extensive simulation experiments. Finally, several research challenges are discussed and open issues are also outlined.

Bargaining Game Based Time Scheduling Scheme for Ambient Backscatter Communications

Backscatter communications have been acknowledged as an essential key technology in the Internet of Things (IoT) applications. Considering the fact that it needs the coordination from network agents, cooperative bargaining theory is an effective method to strike an appropriate system performance. In this paper, we investigate time scheduling algorithms for a backscatter-aided radio-frequency (RF) powered cognitive radio (CR) network, where multiple secondary transmitters can switch among the backscatter, the energy harvest, and active data transmission modes. Our objective is to maximize the RF-CR system performance while exploring the mutual benefits to leverage a reciprocal consensus between different control issues. According to the ideas of two different bargaining solutions - modified Nash bargaining solution and equitable Nash bargaining solution, we design a new dual bargaining game model to effectively share the limited time resources. The main novelty of our proposed approach is its adaptability, flexibility and responsiveness to current RF-CR system conditions. At last, numerical simulations are carried out to evaluate the performance of the proposed scheme, and we demonstrate the benefits of our dual bargaining game approach.

Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach

The increasing concerns of energy utilization and climate change have promoted the permeation of various smart energy subsystems on the distribution level, such as integrated energy systems (IESs) and electric vehicle charging stations (EVCSs). These subsystems typically act separately during operation and their transaction values have not yet been well investigated. In this paper, we propose an energy trading model based on the Nash bargaining game to study cooperative benefits between an IES and several EVCSs. The proposed model not only considers individual interests, but also enables the players to fairly benefit from cooperation. In particular, the uncertainties of the market prices, renewable energies and integrated demand response are considered. To ensure that the entire game is computationally tractable, the original problem is decomposed into a major energy trading problem and an additional payment bargaining problem. Furthermore, a distributed algorithm based on modified Benders decomposition is used to overcoming the players’ privacies. The results show the considerable benefits where the costs of the IES may be reduced by 3.89% and the profits associated with the EVCSs may be increased by at least 7.8%. The proposed algorithm is proven to be able to find the optimal global solutions efficiently and accurately.

Cooperative Inter-Domain Cache Sharing for Information-Centric Networking via a Bargaining Game Approach

Caching popular content locally has been shown to be an efficient way for internet service providers (ISPs) to reduce transit fees and improve the quality of their services. Recently, with the development of information-centric networking, content-peering, which allows peering ISPs to access each other's cache, has attracted interest. However, due to the economic structure and policy routing of the internet, content peering is only applicable between settlement-free peering ISPs, which constraints the benefit of inter-domain cache sharing. In this study, we consider extending the inter-domain cache sharing to ISPs with different tiers; this is done to take full advantage of the caching resources. Specifically, we identify the special properties of the inter-domain cache-sharing market with ISPs of different tiers, analyze the ISPs’ interactions, and employ Nash bargaining solutions to address the resource allocation issues in the market. In addition, we present decentralized algorithms to realize the proposed mechanism for large-scale networks. We show the correctness of the proposed method through rigorous analysis, and we verify the benefits to ISPs by carrying out extensive experiments.

Coordinated production target setting for production–pollutant control systems: A DEA two-stage bargaining game approach

Setting a production target for a two-stage production–pollutant control system can provide guidance for the system to achieve efficient performance in its future production. The existing studies have succeeded in targeting but have missed the consideration of coordination between the adjustments for the two stages’ input and output metrics. In this article, we propose an approach to address this problem using a non-parametric method: data envelopment analysis. First, we use the leader–follower analysis from game theory to propose slacks-based measure models for production target setting in situations where a dominance relationship exists between the two stages of the systems. Based on the results generated by the leader–follower models, we define each stage’s dissatisfaction degree with the set targets. Further, considering the coordination between the adjustments of the two stages’ inputs and outputs, we incorporate the bargaining game theory to propose a model which can generate the set of coordinated adjustments that is a bargaining solution taking account of both the dissatisfaction degrees of the two stages. The properties of the solution generated by the bargaining model feature fairness, which makes the target setting result coordinated between each system’s two stages. Finally, the proposed approaches are applied and tested in an empirical study of 30 provincial regions in China.

DEA-based fixed cost allocation in two-stage systems: Leader-follower and satisfaction degree bargaining game approaches

This paper proposes new fixed cost allocation approaches for allocating a fixed cost among decision-making units (DMUs) with two-stage structures under the framework of data envelopment analysis (DEA). Firstly, we give the set of possible fixed cost allocations, prove that all DMUs can be overall efficient when evaluated by a common set of weights after fixed cost allocation. Secondly, from a centralized point of view, we consider the competition between the DMUs’ two stages in fixed cost allocation and regard these two kinds of stages as two unions. Then, we incorporate leader-follower models to propose a fixed cost allocation approach to handle the situation in which the two unions make decisions sequentially. Based on the result of these models, a concept of satisfaction degree of each union on a fixed cost allocation is presented. A satisfaction degree bargaining game model is then proposed to obtain a fixed cost allocation which is a bargaining equilibrium of the two unions. We show that the proposed approaches always obtain a fixed cost allocation that is proportionally invariant. Additionally, the satisfaction degree bargaining game approach automatically guarantees the uniqueness of the fixed cost allocation. These properties make the fixed cost allocation generated by our approaches more stable and more acceptable. Finally, a numerical example and an application of fixed cost allocation among bank branches are given to illustrate the proposed approach and to compare it with a benchmark approach among the current studies, respectively.

Two-Phase Cooperative Bargaining Game Approach for Shard-Based Blockchain Consensus Scheme

In the last few years, blockchain technologies have come to the forefront of the research and industrial communities as they bring potential benefits for many industries. Even though the blockchain is a safe, reliable, and innovative mechanism, current blockchain solutions have fatal drawbacks of non-supervision and huge computational overhead. Therefore, they cannot be directly applied for real world operations. To resolve this problem, a novel model is presented in this study where the key idea is to split the transactions among multiple shards while processing them in parallel. To achieve the maximum system efficiency through the shard mechanism, we focus on the cooperative game theory. Based on the egalitarian bargaining solution, total transactions per each time period are divided for each shard. According to the proportional bargaining solution, assigned transactions in each individual shard are validated by blockchain nodes in a distributed manner. The main advantage of our two-phase bargaining game model is to provide an axiom-based strategic solution for the shard-based consensus problem while dynamically responding to the current blockchain network conditions. The numerical simulation results show that the effectiveness and efficiency of our game based approach by comparing the existing state-of-the-art blockchain control schemes. In the conclusion, we present our conclusions and provide important future research directions based on the combination of blockchain with other technologies.

Improving physical layer security and efficiency in D2D underlay communication

Device-to-device (D2D) communication is a core technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem of D2D underlay communication in a base station cell area with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D User Equipment and Cellular User Equipment are taken into consideration under the control of Efficiency Functions. The optimization problems are formulated as a game model series of utility functions built on the unit price of jamming power and the amount of jamming service. We extracting the system into a price bargain game with a buyer and a seller both desiring maximum profits, a bargaining game approach is adopted to solve this problem by reaching an agreement of unit price. The step number of bargain process is also a restriction under consideration. For the non-steps model, an Evaluation Function and a Comprehensive Utility Function are demonstrated to analyze the bargain process. For steps-contained model, the step number of iteration is involved and an attenuation function is introduced to modify the bargaining game. The algorithms of two models are proposed to derive the equilibrium point for reaching an agreement. Finally, extensive simulations are illustrated for verifying proposed theory.