Non-cooperative Game Research Articles

Improved differential evolution algorithm for solving multi-person noncooperative game

Improved differential evolution algorithm for solving multi-person noncooperative game

Blockchain Framework for Cognitive Sensor Network Using Non-Cooperative Game Theory

The incorporation of Technology in healthcare and hospital management has given a new perception to the medical procedures, and drug management for patient-centric care. Emerging technologies like blockchain, Internet of Things, and cognitive computing are most adaptable for designing smart healthcare systems. However, due to the diversified tasks involved, a design approach with broader understanding involving multiple factors that represent each area of the healthcare is the need of the hour. Game theory has gained prominence in modeling multi-player problems designated by strategic interdependency. This paper proposes a non-cooperative game strategy between players (stakeholders) to inspect how rationality is exhibited among the players who seamlessly try to get benefitted from the actions of other players. To evaluate the feasibility of the proposed model, a prototype was designed based on Ethereum permissioned blockchain employing Raspberry pi IoT devices and sensor-based cognition. Use of blockchain brings in trust, security and transparency to the system. The simulation results show that the proposed model provides better outcomes in terms of latency (≈ 15 min), throughput and scalability with an increased number of transactions. The comparative analysis elucidates the fact that the proposed method outperforms the existing healthcare systems with a significant improvement of 10-15% in terms of resource utilization and provides faster and accurate patient-centric services.

Research on optimal scheduling of county main new energy consumption based on internal non-cooperative game under curve transaction

Under the “double carbon” goal and background, new energy power generation will usher in great development. The investment and construction of new energy projects and the scale of grid connected transmission are increasing, which puts forward higher requirements for the investment decision-making management and income of county new energy subjects. Based on the power curve trading and non cooperative game theory and the day ahead real-time two-stage adjustment and correction mechanism of county subjects, this paper briefly analyzes the new energy consumption and income of county subjects based on internal non cooperative game under curve trading.

General finite approximation of noncooperative games played in staircase-function continuous spaces

General finite approximation of noncooperative games played in staircase-function continuous spaces

Modeling and simulation: social force model modified by Helbing model in fast pass system

This paper introduced Helbing’s social force model, modified it with game theory. Then how individuals in the space behave in dynamic non-cooperative games was described, different macro grouping characteristics were obtained. Individual behaviours at the micro level were simulated. Setting different parameters and conditions of the model, the macro effects of individual behaviours were observed. The overall behaviour of the system was studied. It could be used to guide the allocation of public resources.

Game Theory Based Dynamic Adaptive Video Streaming for Multi-Client Over NDN

The performance of Dynamic Adaptive Streaming (DAS) in multi-client scenarios can be improved by taking advantage of the aggregation capability of Named Data Networking (NDN). In this paper, we propose a client-side game theory based (GB) ABR algorithm for NDN that can achieve proactive aggregation of requests among clients as much as possible without requiring coordinating with other clients or scheduling by a central controller. We model the interaction between a DAS client and network as an incomplete information non-cooperative game. Then, this game is transformed into a complete but imperfect information game by Harsanyi transformation, and each client can issue an appropriate bitrate request by solving the Bayesian Nash Equilibrium (BNE) problem respectively. By designing the payoff function pair elaborately, the equilibrium point of the game can correspond to the situation that multiple clients issuing the same video bitrate request, that is, requests aggregation, which will reduce the repeated traffic and also achieve fairness. Compared with the existing solutions, through simulation and real-world experiments in multi-client video distribution scenarios, the GB algorithm outperforms the comparison algorithms in terms of overall Quality of Experience (QoE), fairness, and network bandwidth utilization, etc.

Experimental Field Evidence of Common Pool Resources: The Water Judge in Bolivia

Common pool resources (CPRs) are usually exploited one generation after another – often overexploited - meaning there is an intergenerational link between the consumers – e.g., water for farming activities. This key dimension is often not considered in theoretical or field experiments, due to the difficultly in approaching it. We want to overcome this barrier introducing the hypothesis of ‘’intergenerational altruism” for CPRs. The implication is that altruism reduce the exploitation of the natural resources, since the agents recognize that the exploitation not only creates negative externalities for their own generation, but also for all future generations. An alternative hypothesis is the ‘’intergenerational equity’’ where the agents restrain their consumption to equalize their income over time. To prove these hypotheses, we conducted a field experiment in four farming communities located in the Bolivian Department of Chuquisaca during the third quarter of 2019. We consider common water for farming activities as a CPRs, since these communities use this resource for several decades, the intergenerational link is evident. Our intergenerational field experiment includes four treatments based on the replacement rate of the resources – i.e., FAST, SLOW, RESTART or normal replacement, under one-shot non-cooperative game without feedback. We also introduce two variations, the possibility to accumulate water in a dam, which modify the availability of CPRs. Second, the possibility to manage the common farming water through the traditional social arrangement of the Water Judge, which is a representative member of the community delegated to solve problems related with water management, named SAT treatment. The results showed that our hypothesis was not probed since the intergenerational link does not mitigate the overexploitation of CPRs. Nevertheless, we also found that the “Water Judge” could be a cost-effective treatment in small farming communities.

Double Deep Q-Network Based Distributed Resource Matching Algorithm for D2D Communication

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes.

Comparative analysis of dynamic pricing schemes in distributed energy management of residential users in smart grid

Increasing power demand, greenhouse gas emissions, and the old infrastructure are serious concerns in the existing power system. With the advent of the smart grid, demand response (DR) has emerged as an effective approach to handle these issues. The selection of an appropriate DR program is vital to acquire the maximum benefits for the utility and the consumers. In this context, a distributed energy management scheme for residential consumers is presented and analyzed to observe the impact of different pricing schemes. The three dynamic pricing schemes considered in this work are based on linear function, the logarithmic function, and the penalty-based linear function of aggregated load. A non-cooperative game is used to formulate the energy management problem of the consumers. The Nash equilibrium of the game is obtained using the proximal decomposition algorithm. The results are obtained for different cases based on the presence of a storage device, a dispatchable generation unit, and two different modes of operation of an electric vehicle. The best pricing scheme is chosen based on the minimum cost, the peak-to-average ratio of the system load profile, and consumer comfort.

A dynamic theory of spatial externalities

We characterize the shape of spatial externalities in a continuous time and space differential game with transboundary pollution. We posit a realistic spatiotemporal law of motion for pollution (diffusion and advection), and tackle spatiotemporal non-cooperative (and cooperative) differential games. Precisely, we consider a circle partitioned into several states where a local authority decides autonomously about its investment, production and depollution strategies over time knowing that investment/production generates pollution, and pollution is transboundary. The time horizon is infinite. We allow for a rich set of geographic heterogeneities across states. We solve analytically the induced non-cooperative differential game and characterize its long-term spatial distributions. In particular, we prove that there exist a Perfect Markov Equilibrium, unique among the class of the affine feedbacks. We further provide with a full exploration of the free riding problem and the associated border effect.

Asymmetric Evolutionary Game Analysis of Building Information Modeling (BIM) Technology Diffusion

Building Information Modeling (BIM) technology plays a pivotal role in the process of building process informatization. In the context of extensive promotion by all parties, how to effectively evaluate the promotion value of countries and enterprises is particularly important. In order to better analyze the promotion trend of the BIM technology diffusion system in China, a noncooperative evolutionary game model of BIM technology diffusion between the government and enterprises is constructed, and Vensim PLE software is used for simulation analysis in this paper. The model includes government subsidy cost and penalty, social benefit and loss, and income payment matrix of BIM technology input income and cost. The results show that enterprises need not only direct cost subsidy from the government but also indirect cost subsidy in the process of BIM technology diffusion. Appropriate government penalties can effectively promote the initiative of enterprises to adopt BIM technology; BIM has a great impact on the input income and cost of enterprises. In view of this, it is suggested that the first task for enterprises to promote the diffusion of BIM technology is to clarify the definition of BIM's high investment cost and do a good financial impact analysis.

A peer-to-peer energy trading market embedded with residential shared energy storage units

The high penetration of distributed energy resources with local renewable energy consumption facilitates the emergence of peer-to-peer (P2P) energy trading, where a peer can share excessive energy with local peers. P2P energy trading is expected to be a promising business model in the future transactive energy market. Influenced by the sharing economy principle, shared energy storage(SES) is investigated to reduce the costly initial investment and improve the utilization rate of storage devices. Given the context, this paper proposes an equilibrium model of a P2P energy trading market, considering the deployment of shared energy storage in the residential consumer-side. The market equilibrium problem consists of two interwoven games. Considering the benefit conflicts between energy sellers and buyers in P2P energy trading, the P2P transaction between peers can be described as a non-cooperative game. Considering the limited SES capacity resources, the competition among SES users can be viewed as a generalized Nash equilibrium problem. These two games in the P2P market interact with each other and can be summarized as a holistic equilibrium problem, in which the P2P transaction price is endogenously determined and storage capacity is allocated. The market equilibrium problem can be solved based on Karush-Kuhn-Tucker (KKT) optimality conditions and linearization techniques, finally cast as a mixed-integer linear program. Numerical simulation demonstrates that P2P energy trading benefits all participants and the introduction of shared energy storage can further reduce energy costs.

Public Provision of Goods and Services under Cost Uncertainty: The Government’s Bureaucratic Organization

The digital transition is a challenge that developed countries are currently facing. The transition process is associated with different degrees of uncertainty, which are particularly relevant for changes that have to do with the provision of goods and services produced by public administrations. Our paper uses a partial equilibrium model to study the effects of uncertainty on the public provision of goods and services produced by bureaucratic agencies, including the incentive of the government to consolidate production. We assume that bureaucratic agencies may play either a cooperative game with each other and a non-cooperative game against the government (i.e., a consolidated bureaucracy) or a non-cooperative game with each other and against the government (i.e., competing bureaus). Both the government and the bureaus face tradeoffs between maximizing the electorate preferences and extracting some political and/or bureaucratic rents. We find that a cooperative (competitive) bureaucratic solution depends on the nature of the goods produced. We find that costs’ uncertainty affects the level of public production and the way the policymakers extract their rents.

DALF: An AI Enabled Adversarial Framework for Classification of Hyperspectral Images

Hyperspectral image classification is very complex and challenging process. However, with deep neural networks like Convolutional Neural Networks (CNN) with explicit dimensionality reduction, the capability of classifier is greatly increased. However, there is still problem with sufficient training samples. In this paper, we overcome this problem by proposing an Artificial Intelligence (AI) based framework named Deep Adversarial Learning Framework (DALF) that exploits deep autoencoder for dimensionality reduction, Generative Adversarial Network (GAN) for generating new Hyperspectral Imaging (HSI) samples that are to be verified by a discriminator in a non-cooperative game setting besides using aclassifier. Convolutional Neural Network (CNN) is used for both generator and discriminator while classifier role is played by Support Vector Machine (SVM) and Neural Network (NN). An algorithm named Generative Model based Hybrid Approach for HSI Classification (GMHA-HSIC) which drives the functionality of the proposed framework is proposed. The success of DALF in accurate classification is largely dependent on the synthesis and labelling of spectra on regular basis. The synthetic samples made with an iterative process and being verified by discriminator result in useful spectra. By training GAN with associated deep learning models, the framework leverages classification performance. Our experimental results revealed that the proposed framework has potential to improve the state of the art besides having an effective data augmentation strategy.

Subsidized and unsubsidized price competition in a multi-echelon natural gas supply chain with governmental and private members

Determining the optimal prices among supply chain members, including both governmental and private natural gas producers, processing plants (PPs), and distribution centers (DCs) is an important and challenging task. This paper analyses the competitions between the members of a multi-echelon natural gas supply chain for determining their prices, which specify the demand for their products. This study proposes a novel game theory model to investigate cooperative and non-cooperative pricing scenarios. While, in Scenario 1, all supply chain members make decisions cooperatively as a centralized model, the members compete simultaneously to obtain the Nash equilibrium in Scenario 2. The Karush-Kuhn-Tucker (KKT) conditions are defined for the non-cooperative game and the Nash equilibrium is obtained using two different methods. The proposed model is applied to a realistic case study based on a data set derived from the natural gas industry to analyze the optimal pricing decisions under the two scenarios and verify the practicality of the proposed approach. Moreover, the variability in the profits of the members with respect to the changes in the base demands and self-price sensitivity parameters are studied. The results show that the government’s total profit in the cooperative scenario is more than the non-cooperative scenario, while the profit of the private DC in the Nash equilibrium of the non-cooperative scenario is more than that of the cooperative scenario. Moreover, increasing the base demand for the private DC leads to a decrease and increase in its profit in Scenarios 1 and 2, respectively. These findings illustrate the potential sources of conflicts between governmental and private members of the natural gas supply chains when seeking collaboration to improve their profits and highlight the need for future research on incentive mechanism designs facilitating such collaboration.

Partial Order Games

We introduce a non-cooperative game model in which players’ decision nodes are partially ordered by a dependence relation, which directly captures informational dependencies in the game. In saying that a decision node v is dependent on decision nodes v1,…,vk, we mean that the information available to a strategy making a choice at v is precisely the choices that were made at v1,…,vk. Although partial order games are no more expressive than extensive form games of imperfect information (we show that any partial order game can be reduced to a strategically equivalent extensive form game of imperfect information, though possibly at the cost of an exponential blowup in the size of the game), they provide a more natural and compact representation for many strategic settings of interest. After introducing the game model, we investigate the relationship to extensive form games of imperfect information, the problem of computing Nash equilibria, and conditions that enable backwards induction in this new model.

Game-Based Multitype Task Offloading Among Mobile-Edge-Computing-Enabled Base Stations

The widely used Internet of Things (IoT) mobile devices (MDs) require fast processing capability to handle a large volume of computing tasks. Mobile-edge computing (MEC) can augment the capability of IoT MDs through offloading their computing tasks to the MEC-enabled base station (MEC-BS) that covers them. Most of the existing research works only focus on the computation offloading problems for a single MEC-BS. However, the load of a MEC-BS will rise as the increase of the scale of the offloaded tasks, especially during rush hours, and further it will result in deterioration of system performance. In this article, we propose a game-based multitype task offloading scheme among MEC-BSs. The tasks offloaded from IoT MDs can be further offloaded among MEC-BSs to alleviate high-load MEC-BSs. Aiming at balancing the computing delays of the tasks on each MEC-BS, a noncooperative game is formulated to model the computation offloading for the tasks with different types, indicated by computation amount, data size, and delay tolerance. The existence and convergence of the Nash equilibrium of the game are first proved using the variational inequality and regularization techniques. Then, we design a distributed iterative algorithm to efficiently solve the game problem. Simulation results show the fast convergence of our algorithm. The reduction of total computing delay optimized by our scheme can reach 45%–50% on average in multiple scenarios, and the superiority of our scheme is also demonstrated in comparisons with reference schemes.

5G Ultra-Reliable and Low Latency Communication Resource Scheduling for Power Business Quality Assurance

The resource scheduling method of 5G URLLC (Ultra-Reliable and Low Latency Communication) is studied in this paper to assure the Quality of Service (QoS) of various power business, which utilizes the limited spectrum and power in low-band cellular communication system to meet the requirement of power terminal about transmission rate, scheduling delay and fairness in an efficient manner. Firstly, based on the high reliability and low latency characteristics of URLLC, a multi-cell downlink system model is built. Then, a resource allocation problem model for maximizing downlink throughput is proposed and solved step by step. Power allocation algorithm bases on pricing mechanism and non-cooperative game and an Delay-based Proportional Fair algorithm (DPF）is designed to schedule channel resource dynamicly..Simulation results show that the proposed resource scheduling method can reduce the scheduling delay of power terminals under the constraints of transmission reliability and fairness while meeting the different QoS requirements. The proposed method outperforms some known algorithms.

Existence and Generic Stability of Strong Noncooperative Equilibria of Vector-Valued Games

In this paper, we obtain an existence theorem of general strong noncooperative equilibrium point of vector-valued games, in which every player maximizes all goals. We also obtain an existence theorem of strong equilibrium point of vector-valued games with single-leader–multi-follower framework by using the upper semicontinuous of parametric strong noncooperative equilibrium point set of the followers. Moreover, we obtain some results on the generic stability of general strong noncooperative equilibrium point vector-valued games.

A new energy storage sharing framework with regard to both storage capacity and power capacity

Energy storage systems have received widespread attention due to their advantages on rapid response, smooth fluctuations, and the reduction of temporal and spatial imbalance. At present, most researchers mainly consider the allocation of energy storage capacity while using an average allocation of the power capacity, which may lead to conflicts among users when executing the energy sharing strategies for the case with limited power capacity. In order to better improve energy efficiency and reduce electricity costs, this paper proposes an energy storage sharing framework considering both the storage capacity and the power capacity. Since each prosumer intends to minimize its costs which depend on the behavior of other prosumers, the relationship among prosumers forms a non-cooperative game. By using an algorithm that combines the alternating direction multiplier method and heavy ball method, a generalized Nash equilibrium is found to serve as an energy sharing strategy. Simulation studies and comparisons show that the proposed energy storage sharing framework driven by a dynamic electricity price mechanism can reduce prosumers’ net demands, electricity costs and improve the efficiency of energy storage utilization.