Non-cooperative Game Theory Research Articles

Correction to “Noncooperative Game Theory To Ensure the Marketability of Organic Fertilizers within a Sustainable Circular Economy”

ADVERTISEMENT RETURN TO ISSUEPREVCorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to “Noncooperative Game Theory To Ensure the Marketability of Organic Fertilizers within a Sustainable Circular Economy”Selene Cobo*Selene CoboMore by Selene Cobohttp://orcid.org/0000-0002-2879-6261, Fengqi YouFengqi YouMore by Fengqi Youhttp://orcid.org/0000-0001-9609-4299, Antonio Dominguez-RamosAntonio Dominguez-RamosMore by Antonio Dominguez-Ramos, and Angel IrabienAngel IrabienMore by Angel Irabienhttp://orcid.org/0000-0002-2411-4163Cite this: ACS Sustainable Chem. Eng. 2020, 8, 23, 8880Publication Date (Web):June 4, 2020Publication History Received26 May 2020Published online4 June 2020Published inissue 15 June 2020https://doi.org/10.1021/acssuschemeng.0c03927Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views529Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (464 KB) Get e-Alerts Get e-Alerts

A multicast routing restoration algorithm based on the n-person non-cooperative game in multi-domain optical networks

Aiming at the restoration problems for multicast service in multi-domain optical networks, a multicast routing restoration algorithm in multi-domain optical networks based on the n-person non-cooperative game is proposed by adopting the Breadth First Search (BFS) strategy and n-person non-cooperative game theory. This algorithm optimizes the searching sequence of BFS algorithm and reduces the searching scope by the competitive selection relation of the game theory. The algorithm analysis and experimental results show that the algorithm has low time complexity, reduces the computational time of the restoration path, and reduces the blocking rate of the multicast restoration service.

Neighbor Cell List Optimization based on Game Theory and Location Information for the Handover Process in Dense Fcns.

The integration of cellular networks allows mobile users to eliminate poor indoor coverage and call dropping probability. Femto stations (FS’s) appeared to be one of the innovative solutions that enhanced network coverage and the Quality of Service (QoS) when servicing indoor users. The cellular network Operator can potentially benefit by employing FS inside buildings and shares the allocated spectrum among different network entities. The seamless handover (HO) process between network entities is a major challenge of the Femto cellular networks (FCNs). Furthermore, the minimum and appropriate neighbor Femto list (NFL) is the main aim to guarantee the complete execution of the HO process. In this paper, an algorithm is proposed for power control in dense Femto Stations environment as long as possible through the Nash non-cooperative game theory. additionally, it provides location information mechanism to ensure a seamless transition between different network entities based on detected frequency from neighbor FS’s, signal to interference noise ratio (SINR), as well as the location information of FS in the coverage area. Simulation results show that the proposed algorithm reduces the HO failure probability through improving the NFL by deducting 40% of the amount of FSs in the NFL. As compared to the traditional scheme based on RSSI and frequency allocation, with increasing the number of FSs, there is around 40- 50% reduction in the probability that the target FS is not included in the NFL which improves the network performance and lowers HO failure probability.

Bi-objective optimization problems with two decision makers: refining Pareto-optimal front for equilibrium solution

The Pareto-optimality concept in multi-objective optimization theory is different from the Nash equilibrium concept in noncooperative game theory. When the objective holders are independent decision makers, i.e., human entities or organizations, any solution on the Pareto-optimal front is not necessarily an equilibrium point, hence not a valid solution. The solution has to be a Pareto-optimal-equilibrium (POE) point. In this paper, we convert a bi-objective optimization problem into a two-player game problem by introducing “induced games,” and we propose a new refinement method to find a POE point. We prove that at least one such POE point exists for a class of linear bi-objective optimization problems, and we develop an algorithm to find it. We discuss that the innovative approach considered in this paper is of real future interest to some industrial and social applications. One such example is also presented.

자가 구성 네트워크에서 셀주밍과 비협력 게임 이론을 이용한 단말기의 최저 SINR 개선을 위한 셀 간 간섭 조정

자가 구성 네트워크에서 셀주밍과 비협력 게임 이론을 이용한 단말기의 최저 SINR 개선을 위한 셀 간 간섭 조정

Optimal Cooperative Path Planning Considering Driving Intention for Shared Control

<div class="section abstract"><div class="htmlview paragraph">This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach. Finally, the proposed framework is tested with different driver’s driving intentions to avoid obstacles on a straight road and a curvy road. As a result, the planned path could continuously adapt to the driving intention and various road shapes, and the path conflicts between the human driver and the controller is also decreased by the proposed cooperative path planning method in the game-based shared control framework.</div></div>

Can the Threat of Economic Sanctions Ensure the Sustainability of International Fisheries? An Experiment of a Dynamic Non-cooperative CPR Game with Uncertain Tipping Point

Complex dynamic systems such as common-pool resource systems can undergo a critical shift at a given threshold, the so-called tipping point, which potentially requires substantial changes from the management system. We present in this research a framed laboratory experiment design to examine how the threat of economic sanctions influences the strategic management of a common-pool resource. We use the context of the East Atlantic bluefin tuna international fishery as it has been the archetype of an overfished and mismanaged fishery until a dramatic reinforcement of its regulations followed the threat of a trade ban. We consider endogenous threats and examine their effects on cooperation through harvest decisions taken in the context of non-cooperative game theory in which cooperation could be sustained using a trigger strategy. Our experiment results show that the threat of economic sanctions fosters more cooperative behaviors, less over-exploitation, and a more precautionary management of resources, reducing the economic rent dissipation. This result is exacerbated when the location of the tipping point that triggers the economic sanction is uncertain. In order to avoid free-riding behaviors and foster the emergence of a self-enforcing agreement, we suggest to introduce economic sanctions, such as trade restrictions, associated with uncertain biological limit reference points.

Assessment of transmission outage Contingencies’ effects on bidding strategies of electricity suppliers

This paper presents a solution concept for quantifying effect of transmission outage contingencies on wholesale electricity markets. For this purpose, an equilibrium-based model is employed to investigate the optimal bidding strategies of contingency-constrained generation companies (GENCOs). All GENCOs submit their bids to the independent system operator (ISO) in economic model of supply function equilibrium (SFE). Economic behaviour of a GENCO is formulated as a bi-level programming (BLP) problem. In the upper level, the profit of intended GENCO is maximized and in the lower level, the ISO clears the market via solving a bid-based preventive contingency constrained economic dispatch (PCCED). A constraint-reduced Primal-Dual Interior Point Method (PDIPM) is proposed to determine the optimal solution of PCCED. To ease the computation burden, a novel Contingency Filtering (CF) technique is devised to filter out non-critical contingencies. It is shown that non-critical contingencies can be identified by solving series of Linear Programming (LP) problems. Also, the non-cooperative Game Theory (GT) is utilized to determine the equilibrium of oligopolistic competition of contingency-constrained GENCOs. Finally, effect of contingency constraints on the optimal bidding strategies of GENCOs is investigated through simulation studies.

Balancing management of strategic aggregators using non-cooperative game theory

The aggregators are intermediary players at the distribution system level. They manage the financial transactions of resources such as micro-turbines (MTs), photo-voltaic (PV) production systems, flexible demands (FDs), and must-run demands (MDs) in the energy market. Based on the regulation of many transmission system operators (TSOs) in European countries, an aggregator is obliged to be assigned to a balancing group (BG) represented by a balancing group manager (BGM). The TSO measures the energy imbalances at the BG level. Meanwhile, the BGM is responsible for the management of financial transactions between the aggregators and the TSO. The strategic aggregators can establish a common BG instead of individual BGs. Then, they gain more payoffs since their energy imbalances are compensated more efficiently. However, the BGM should have the complete knowledge of the aggregators’ confidential information, which includes the evaluation of aggregators concerning the cost/utility functions of associated resources. The aggregators have no incentive to disclose this valuable information. To solve this challenge, an iterative distributed algorithm based on non-cooperative game theory is proposed. In the numerical results, the performance of proposed common BG has been evaluated thanks to the comparison with a benchmark case where the aggregators establish their own individual BGs.

The Portfolio Optimization Performance during Malaysia’s 2018 General Election by Using Noncooperative and Cooperative Game Theory Approach

Game theory approach is used in this study that involves two types of games which are noncooperative and cooperative. Noncooperative game is used to get the equilibrium solutions from each payoff matrix. From the solutions, the values then be used as characteristic functions of Shapley value solution concept in cooperative game. In this paper, the sectors are divided into three groups where each sector will have three different stocks for the game. This study used the companies that listed in Bursa Malaysia and the prices of each stock listed in this research obtained from Datastream. The rate of return of stocks are considered as an input to get the payoff from each stock and its coalition sectors. The value of game for each sector is obtained using Shapley value solution concepts formula to find the optimal increase of the returns. The Shapley optimal portfolio, naive diversification portfolio and market portfolio performances have been calculated by using Sharpe ratio. The Shapley optimal portfolio outperformed the naive diversification portfolio and market portfolio in 6 months before and after the GE14.

Evaluating the impact of energy efficiency programs on generation maintenance scheduling

This paper presents a novel approach to generation maintenance scheduling (GMS) problem. The GMS problem is one of the main issues in the restructured power system, due to its effect on the security risks and the profit of the generation company (Genco). In order to consider the risk of GMS for producers, the GMS problem from the perspective of producers is modeled using non-cooperative game theory, and to determine the Genco's optimal strategy profile, the Nash equilibrium is used. In this paper, the coordinating procedure to satisfy reliability criterion and greenhouse gases (GHGs) reduction is done by the independent system operator (ISO). In recent years, extensive researches have been fulfilled on the implementation of energy efficiency programs in the restructured electricity industry. Energy efficiency programs play a significant role in meeting our energy requirements, energy security risks, and GHGs problems. In this paper, an economic-environmental model of efficiency power plants (EPP) has been applied to analyze impacts of the energy efficiency programs in the generation maintenance scheduling (GMSEPP). Also, the impact of energy efficiency on emission production in GMSEPP is considered. The expansion of the EPP has been facilitated by regulatory support schemes. In this work, the energy efficiency feed-in tariff is considered as a regulatory support system to encourage the investors. The Implementation of the energy efficiency programs on generation maintenance scheduling can lead to the improvement in the reliability and reduction in GHGs in the modified IEEE RTS 24 bus.

Noncooperative Game Theory To Ensure the Marketability of Organic Fertilizers within a Sustainable Circular Economy

To optimize the environmental performance and the conflicting economic interests of the main stakeholders that interact within circular integrated waste management systems (CIWMSs), life cycle anal...

Energy‐efficient resource allocation for device‐to‐device communication through noncooperative game theory

SummaryWireless network with high data rate applications has seen a rapid growth in recent years. This improved quality of service (QoS) leads to huge energy consumption in wireless network. Therefore, in order to have an energy‐efficient resource allocation in cellular system, a device‐to‐device (D2D) communication is the key component to improve the QoS. In this paper, we propose a noncooperative game (NCG) theory approach for resource allocation to improve energy efficiency (EE) of D2D pair. A three‐tier network with macrocell base station (MBS), femtocell base station (FBS), and D2D pair is considered, which shares the uplink resource block. A resource allocation strategy with constraints is arrived, which maintains minimum throughput for each user in the network. The proposed resource allocation strategy optimizes the EE of D2D pair in the three‐tier network, which achieves Nash equilibrium (NE) and Pareto optimality (PO). Simulation results validate that EE is uniform and optimum for all D2D pair, which converges to NE when channel is static and it converges to PO when the channel is dynamic.

Noncooperative Gaming for Energy-Efficient Congestion Control in 6LoWPAN

Wireless sensor networks (WSNs) provide a significant contribution to the advancement of the Internet of Things (IoT) as it can transfer massive volume of data to the Internet with the help of low-power and low-cost self-operating sensing devices. This article deals with the congestion problem which is one of the critical issues in IPV6 over low-power and lossy wireless personal area network (6LoWPAN)-based network as it causes significant packet loss that leads to degraded throughput and extra energy consumption. We propose a congestion control mechanism for 6LoWPAN by using the noncooperative game theory. The proposed method named noncooperative gaming for energy-efficient congestion control (NGECC) determines the optimal data transfer rate of all the source nodes (leaf nodes) to avoid congestion at the intermediate nodes. NGECC considers both the channel occupation and the buffer overflow as the main factors of the packet loss in the network. For the simulation of the NGECC method, we use Contiki OS and Cooja simulator, which operates based on the IEEE 802.15.4 standard and implements the 6LoWPAN protocol stack. The performance of NGECC has been evaluated by comparing it with the analytical results, the default routing protocol for low-power and lossy network functioning, and two other existing methods. The simulation results show that the NGECC outperforms the existing methods in terms of sending rate of data packets, packet delivery ratio, throughput, weighted fairness index, energy consumption, and delay.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

With wireless mesh networks widely deployed in daily life, more and more researchers have begun to put their eyes on the problem of resource allocation and load balancing as well as networking security in wireless mesh networks. The admission control strategy takes great advantages in resource allocation and network load, thus having been widely applied in wireless networking. To maximize the benefits of game participants, it is essential to build an effective admission control model for user admission and resource allocation. Game theory, as an effective analytical modeling tool, is widely used in wireless networking researches. The access points determine whether to allow user’s access according to the admission control mechanism, for which this paper investigates resource allocation in wireless mesh networks. Our main works and contributions are as follows. Firstly, this paper analysis the importance of admission control in wireless mesh network, and addresses the problem of resource allocation and user admission control with game theory on the basis of existing researches. Secondly, this paper depicts the architecture and features of wireless mesh network as well as the functionality of admission control. Then, an admission control algorithm with the non-cooperative game theory for bandwidth and connections allocation in each network area is proposed based on the networking coverage, in which the Nash equilibrium existence is proved. This paper evaluates the proposed algorithm with blocking rate and system efficiency in the networks, which indicates that it is effective and efficient to allocate resource for each area dynamically.

Combining Cooperative With Non-Cooperative Game Theory to Model Wi-Fi Congestion in Apartment Blocks

The unlicensed spectrum utilized by Wi-Fi can be regarded as an economic commons in many deployments. Operators of Wi-Fi-enabled devices are usually non-cooperative, vying for spectral resources when in close range to each other, typically adopting a strategy of maximizing their transmission power. With an ever-growing number of wireless devices, this will ultimately lead to depletion of the spectrum, unless players collaborate. Previous studies used cooperative game theory to explore various collaboration strategies, enabled by the presence of some central authority or controller that executes an agreed-upon interference mitigation policy. However, the regulatory nature of unlicensed spectrum dictates that players cannot be forced into such collaboration. Most deployments therefore involve a mix of cooperative and non-cooperative players. In this paper, we propose a new way of modeling use cases involving a central authority or controller by combining non-cooperative and cooperative game theory. Our model uses the non-cooperative concept of Nash equilibriums as well as the cooperative concept of Nash bargaining. To the best of our knowledge, this paper is the first to propose a hybrid non-cooperative and cooperative game theoretic model for communication networks that offers the players the opportunity to strategize between non-cooperative and cooperative natures. It is successfully applied to the case of a densely-populated apartment block. The results show that, if only a subset of players joins the collaboration, most of the remaining non-joining players may obtain an SINR that is worse than what they would have obtained in the fully non-cooperative scenario; they are punished for their uncooperative behavior.

Application of Game Theory in Integrated Energy System Systems: A Review

With the rapid development of society, global energy is in short supply. China has put forward an integrated energy system focusing on interconnecting energy resources and harnessing their complementary advantages to address the energy crisis, thus no longer pursuing the production, transportation and supply of a single source of energy. In the continuous development of integrated energy systems, the elements of participation and interaction are becoming more complex. Game theory, which can effectively solve the problems arising from multi-agent trading, is naturally introduced to the integrated energy system. This paper provides a comprehensive overview of the introduction of game theory into the integrated energy system. First, the development of integrated energy is briefly introduced, game scenarios in integrated energy systems is proposed, and game scenarios considering the energy supply side, distribution network, demand side and common planning and dispatching problems in integrated energy systems are summarized. Secondly, main game theory models in the integrated energy system are summarized, such as cooperative game theory model, the non-cooperative game theory model and the Stackelberg game theory model. Finally, the future prospect and challenge for the application of game theory in integrated energy systems is proposed. The new game models are introduced to the integrated energy system, and a mixed game is considered to solve related problems. It is hoped that this work can serve as a reference for the researchers in this field.

Optimizing the strategy of activities using numerical methods for determining equilibrium

The paper considers issues on the theoretical substantiation of options for choosing an optimal strategy to integrate an agricultural enterprise into the wholesale market by using methodological tools of the non-cooperative game theory. We have proposed modeling the behavior of an agrarian enterprise in the market by achieving a Nash equilibrium under various scenarios of competitors’ activities and volumes of information on market conditions. The methodology has been substantiated to apply the iterative algorithms to calculate equilibria in a general class of non-quadratic convex polyhedra in order to form the methodologies and construct algorithms for a behavior of agricultural enterprises in market activity. It was determined that decision-making occurs in parallel to the real conditions of activity of an agricultural enterprise in the wholesale market. The comprehensive application of numerical methods based on solving the optimization problems provides a smooth approach to the Nash equilibrium. A game can have multiple isolated Nash equilibria if players have non-quadratic payment functions when solving such problems. Based on the above, the results were determined of local convergence, since global results have strong constraints in non-quadratic problems. However, there is a connection with semi-global practical asymptotic stability if players have quadratic payoff functions. It has been shown that there is a shift in the convergence in proportion to the amplitudes of disturbance signals and the third derivative of payoff functions for non-quadratic payoff functions. This shift in the convergence corresponds to the shift in a numerical example. It has been determined that the learning strategy developed in accordance with the main provisions of the theory of games remains attractive if one has partial information on the state of the market. Application of the indicated action strategy provides a company with a possibility to improve its initial position by measuring its own payoff values only and not using estimates of potentially uncertain parameters. It has been proposed to use applied tools from the game theory to determine an optimal action strategy for an agricultural enterprise for its integration into the wholesale market of vegetable products

Application Task Allocation in Cognitive IoT: A Reward-Driven Game Theoretical Approach

In this study we consider the scenario of sensors belonging to different platforms and owned by different owners that join the efforts in an opportunistic way to improve the overall sensing capabilities in a given geographical area by forming clusters of nodes. The considered nodes have cognitive radio and exploit device-to-device communications. A solution is proposed which relies on a Cluster Head (CH) that guides the whole task allocation strategy. The addressed challenges are the following: i) collaborative spectrum sensing for effective communications within the cluster; ii) assignment of each request of sensing tasks to a single node in the cluster. The first challenge is addressed by proposing a collaborative sensing procedure where each node communicates to the CH the received signal energy of licensed users so that the latter makes a decision on the availability of the band by fusing the received information towards a minimisation of the uncertainty in detecting the free spectrum. The second challenge is addressed by proposing a non-cooperative Game theory based approach in which cluster nodes make effort to selfishly increase utility by winning the task. Each node takes part to the competition by considering two elements: the gain that is won for its contribution to sensing and for the execution of the task (in case it wins the competition); the cost in terms of energy to be consumed in case the task is executed. A Nash Equilibrium Point (NEP) is found for the aforementioned game in which each object has no incentive to deviate uni-laterally from the NEP. Extensive simulations are performed to evaluate the impact of probability of false alarm, utility function weighting factors and presence of licensed users on the cumulative system utility.

Allocating common costs of multinational companies based on arm's length principle and Nash non-cooperative game

Allocating common costs among the subsidiaries of multinational companies (MNCs) is widely conducted in practice. It is of paramount importance that optimal allocation plans can be developed. In this study, we propose an allocation method based on the arm's length principle (ALP), which is well accepted for the internal transactions between MNCs and subsidiaries. Unlike the available studies addressing efficiencies, this study considers profits in common cost allocation. We first deduce a general mathematical expression of the ALP for common cost allocation. Based on it, allocation models are developed, aiming to maximize the profits of both MNCs and subsidiaries. We further develop a solution approach including an algorithm based on the Nash non-cooperative game theory. We prove several interesting characteristics of the algorithm, including (i) the algorithm is convergent, (ii) the optimal solution is a Nash equilibrium and unique, and iii) the optimal solution is not affected by any initial allocation plan. The results of a case application highlight the applicability of our allocation method and solution approach. Through the study, we obtain several important practical insights, including (i) both the ALP and cooperate tax rates affect MNCs’ profit maximization, and (ii) subsidiaries’ profit maximization is affected by the ALP only.