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In the context of China’s vigorous promotion of inter-provincial electricity market construction， there exists a competitive dynamic among multiple purchasing provinces participating in the inter-provincial electricity spot market. To devise a rational bidding strategy for these purchasing provinces， a game bidding model for electricity purchasing tailored to the inter-provincial electricity spot market is proposed. Firstly， the marginal inter-provincial electricity purchase quantity-price curve is calculated based on the operational conditions of the electricity market within each province， serving as the bidding curve for its participation in the inter-provincial electricity spot market. Subsequently， a model for market clearance and pricing in the inter-provincial electricity spot market is proposed under the scenario of variable flow directions of tie-lines. A non-cooperative game bidding model for the inter-provincial market， involving multiple purchasing provinces， is established， where the bidding curve obtained under Nash equilibrium represents the optimal electricity purchase bid for the purchasing provinces. Finally， a simulation calculation is conducted on a case study system comprising two selling provinces and three purchasing provinces to validate the effectiveness of the proposed model and obtain the Nash equilibrium solution of the bidding model for electricity purchase.

"Sharing economy" refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach. [ABSTRACT FROM AUTHOR]

The generation maintenance scheduling deals with a time sequence of preventive maintenance outages for a given set of generation units in an electricity market subject to power system restrictions. Incorporating a leader–follower structure in generation maintenance scheduling models is essential because of the inherent conflict between the interests of an independent system operator (ISO) and generation companies (GENCOs). The present paper proposes a new preventive maintenance scheduling model for generation companies facing the risk of involving generation units' disruption and demand variations while ensuring the reliability of the power system. Each GENCO proposes the maintenance schedule of its generation units to the ISO in a non-cooperative manner intending to maximize its net profit. Then ISO reacts to the aggregated schedule according to the power system's reliability index. Thus, a new formula is developed to consider all the interactions between the power system's stakeholders. In this regard, a stochastic multi-leader one-follower approach is applied. The GENCOs are considered independent leaders at the upper-level and the ISO is considered a follower at the lower-level. Then an equivalent single-level counterpart model is presented for each leader. So, the whole problem is converted into multiple individual stochastic single-level models, and then the Nash Equilibrium concept is used to determine GENCO equilibrium strategies. The proposed methodology is evaluated using some modified IEEE reliability test systems. The numerical analysis confirms that the proposed model is more effective in cases with higher uncertainties. Moreover, the performed analysis demonstrated the importance of applying a bi-level approach to the problem. Finally, the superiority of the proposed approach compared to the existing one is confirmed. [ABSTRACT FROM AUTHOR]

In this article, a distributed charging strategy problem for plug-in electric vehicles (PEVs) with feeder constraints based on generalized Nash equilibria (GNE) in a novel smart charging station (SCS) is investigated. The purpose is to coordinate the charging strategies of all PEVs in SCS to minimize the energy cost of SCS. Therefore, we build a non-cooperative game framework and propose a new price-driven charging control game by considering the overload constraint of the assigned feeder, where each PEV minimizes the fees it pays to satisfy its optimal charging strategy. On this basis, the existence of GNE is given. Furthermore, we employ a distributed algorithm based on forward–backward operator splitting methods to find the GNE. The effectiveness of the employed algorithm is verified by the final simulation results. [ABSTRACT FROM AUTHOR]

This study investigates the dynamics of endogenous order placement timing among competing retailers within a single period, driven by the evolution of demand-forecast information. Despite the critical role of accurate market trends and demand forecasts in determining firm success during selling seasons, the existing literature lacks a comprehensive understanding of how firms strategically adjust their order timing with imperfect and evolving information landscapes. By leveraging resources such as predictive analytics systems operated by big data and social media, firms tend to enhance their market demand precision as the selling season approaches, aligning with market practices. With this background, we aim to address the strategic behaviors of competing retailers in timing their orders, filling the aforementioned research gap. We construct a non-cooperative game-theoretical model to analyze the strategic behaviors of competing retailers in timing their orders. The model incorporates factors such as imperfect and evolving information landscapes, considering how firms leverage resources to enhance their market demand precision as the selling season approaches. Our analysis shows two primary equilibria, each shedding light on distinct strategic choices and their implications. First, the better-informed firm decides to execute early orders, capitalizing on the first mover's advantage, particularly when initial information imprecision exceeds a specific threshold. Conversely, a second equilibrium emerges when the better-informed firm delays its orders, yielding the first mover's advantage to the less-informed competitor. These equilibria highlight the correlation between order timing strategies and the trajectory of information evolution within the competitive landscape. Additionally, our study extends beyond equilibrium analysis to investigate these strategic choices on supply-chain performance. [ABSTRACT FROM AUTHOR]

Plant disease control is often implemented at the private property level, and problems of cooperation and coordination between landowners can lead to social inefficiencies. Drawing on the private management problem of sharka on Prunus trees, we analyse an epidemic game and its outcomes according to initial infection levels. We show that, depending on the infection level in each farm, the nature of strategic interactions changes drastically, and a wide range of games can occur, including games with multiple or without pure strategy equilibria and games with coordination or anti-coordination patterns. We characterise the epidemic conditions for which private management produces social inefficiency and analyse the properties of uniform and differentiated subsidy schemes to solve it. We conclude with a discussion of the policy implications of the ongoing deregulation of sharka management in France. [ABSTRACT FROM AUTHOR]

Transactive energy (TE) as a market-based mechanism provides a practical framework to fully manage and control local energy networks with a high penetration of distributed energy resources (DERs). However, the existing designed TE frameworks can rarely encourage prosumers to participate fairly owing to neglecting competition and cooperation. This paper proposes a tri-layer hybrid game-based TE framework, wherein the cooperation of prosumers to trade energy in a peer-to-peer (P2P) fashion is considered in the first layer using the Nash Bargaining Game (NBG) Theory. The competition among prosumers to trade with the most affordable aggregator is modeled at the second layer using the evolutionary game (EG). The third layer also models the competition among aggregators and the competition between aggregators and the cooperation of prosumers by developing a non-cooperative game. Besides, a scale-independent distributionally robust optimization (DRO) is developed based on the Wasserstein ambiguity set to allow prosumers to manage their uncertainty using all potential historical data while ensuring tractability. Finally, a new adaptive proximal alternative direction method of multipliers (ADMM) is introduced to develop a distributed-decentralized decision-making scheme for satisfying network constraints and energy trading in a P2P manner to accelerate the solution procedure and preserve privacy. The tests and implementations demonstrate that the proposed tri-layer TE framework lowered the overall costs for prosumers by 11 % and 2.85 % compared to the total costs in non-cooperative TE and cooperative TE, respectively.

Multi-energy systems are one of the key technologies to tackle energy crisis and environmental pollution. An energy hub (EH) is a minimum multi-energy system. Interconnection of multiple EHs through energy routers (ERs) can realize mutual energy assistance. This paper proposes a peer-to-peer (P2P) energy sharing strategy between EHs including ERs in an interconnected system, which is divided into two levels. In the lower level, a method of determining the charging/discharging constraints of energy storage devices is proposed. Based on the Lyapunov optimization method, virtual queues are used to model the energy storage devices and flexible loads in the system. The objective is to minimize the overall operating cost of the interconnected system. In the upper level, a non-cooperative game model is introduced to minimize the cost of purchasing power from other EHs for each EH. A best response-based method is adapted to find the Nash equilibrium. The simulation outcomes demonstrate that application of the proposed strategy can reduce operating costs of an interconnected system and each EH. On basis of a real-world dataset of interconnected EHs, both analytical and numerical results show the effectiveness of the proposed strategy.

In order to solve the spectrum efficiency optimization problem of device-to-device (D2D) communication in many-to-one reusing mode, the channel allocation and power control problem in the frequency-selective channel environment is investigated. A resource allocation algorithm based on hierarchical optimization is proposed. The highly coupled resource allocation problem is decoupled by introducing the layering idea. Firstly, in the outer layer, a power control algorithm based on discrete power selection and two fast channel allocation algorithms based on priority setting are proposed. By using discrete power control for cellular users, the problem of multiple local optimal solutions is solved, which makes the power control algorithm of the whole system converge. Meanwhile, the proposed channel allocation algorithm reduces the complexity by setting priorities for D2D users, and at the same time realizes the suppression of cross-layer interference. In the inner layer, a resource allocation algorithm based on a non-cooperative game is proposed. By modeling D2D users share the same channel as a non-cooperative game model and introducing the cost function in term of same-layer interference, it achieves the suppression of same-layer interference while improving the spectrum efficiency. And the Nash equilibrium solution of the game is obtained by solving the KKT condition. Simulation experiments show that the proposed algorithm not only improves the spectrum efficiency of the system compared with the existing algorithms, but also proves the superiority of the algorithm in ultra-dense D2D communication scenarios.

To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs' driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver's dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics. [ABSTRACT FROM AUTHOR]

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids. [ABSTRACT FROM AUTHOR]

“Sharing economy” refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach.

The Paris Agreement, which is carried out in a bottom-up manner, is giving great vitality to the transition to low-carbon energy system. Accordingly, as the spread of renewable energy increases, securing flexibility resources is emerging as a keyword in the power system and power market, and many studies on demand response (DR) and energy storage system (ESS) classified as demand-side flexible resources are being conducted. However, most of the studies on DR are on voluntary DR programs that participate through bidding among incentive-based DR or price-based DR programs, and ESS faced difficulties in its distribution due to low economic feasibility. Therefore, in this work, we propose a feasible process for participating in a reliability DR program that guarantees high profitability but involves high uncertainty, and non-cooperative game model is suggested to mitigate the uncertainty through surplus electricity-trading model of ESS. In addition, a subjective decision-making based on prospect theory is proposed to reflect the impact of the uncertainty about the incentive-based program’s engagement. Case study results performed in an environment formed based on actual operation data of the Korean DR market prove that participants’ uncertainty is mitigated when participating in the DR market through power trading of ESS through the proposed model. In addition, a small-scale ESS with an average benefit–cost ratio of 0.78 improves by up to 1.08 (1.04 with a standard scenario).

This paper investigates distributed Nash equilibrium (NE) seeking problems. A bilateral bounded gradient approach, a novel optimization algorithm, is utilized to solve strongly convex problems. Furthermore, for a strongly monotone game, the NE can be obtained in finite time by the bilateral bounded gradient algorithm. In the distributed manner, two types of algorithms are proposed for seeking the NE: consensus‐based strategy and passivity‐based strategy. For each player, nonlinear protocols are proposed to estimate the actions of their rivals, enabling these estimations to converge to the actual actions in fixed time. To solve the optimization problem, the bilateral bounded gradient algorithm is employed, ensuring that all players' actions converge to the NE in finite time. Moreover, to reduce the communication consumption, event‐triggered schemes are introduced in the information exchange of players. Finally, swarm roundup behavior is analyzed by a non‐cooperative game in which the proposed algorithms drive all tanks to hunt the target in finite time. The roundup effectiveness is verified by the simulations. [ABSTRACT FROM AUTHOR]

Today, with the proliferation of complex networks and their large amounts of data, researchers have great concerns about the accurate community detection methods. The difficulty in analyzing these networks stems from their enormous size and the complex relationships among the members of the networks. It is difficult to analyze the deep relationships and mechanisms by just looking at the whole. Traditional methods have some problems and limitations when analyzing these networks such as feature extraction, high reliance on the initial phase settings, computational complexity, neglect of network relationships and content. From the perspective of relationships and interactions between individuals, the environment of complex networks can be compared to a game in which nodes acting as players or agents may join or leave a community based on similar structural or semantic characteristics. Consequently, there is a strong tendency to use cooperative and non-cooperative games to detect communities. Moreover, the amalgamation of deep learning techniques and game theory has recently been proven to be highly effective in extracting communities. Deep learning techniques have demonstrated enhanced capability in feature engineering and automate the process. In this study, the authors make effort to detect rational and accurate communities based on structural and content features with the help of traditional approaches, deep learning, as well as cooperative and non-cooperative games. The efficiency of this study is demonstrated by experimental findings on real datasets, and confirming that it is able enough to identify those communities that are more meaningful. [ABSTRACT FROM AUTHOR]

Exchange rate and its related risk management are too important for main participants in foreign exchange markets. There are many approaches developed in the literature for studying risk management say arbitrage detection, say finding replication portfolio. However, in the current paper, arbitrage opportunities are studied using the game theory perspective. This paper proposes different types of games played in a specified foreign exchange market in the presence of three exchange rates. Proposed games are exchange rate games in two cases of no arbitrage and existence of arbitrage, optimal stopping game, the arbitrage game, threshold strategies used in global game and Non-cooperative exchange rate game. Most of cases, the bang-bang rule of optimal control is used for finding the Nash equilibriums (NE). However, simulated and stochastic approximation (SA) solutions are also given. Most highlights of the current paper are: (I) considering two types of arbitrage opportunities, simultaneously, (II) translating arbitrage detection as game theory concepts, (III) solving the problem using techniques of optimal control theory. Finally concluding remarks are proposed.

In this article, a distributed charging strategy problem for plug-in electric vehicles (PEVs) with feeder constraints based on generalized Nash equilibria (GNE) in a novel smart charging station (SCS) is investigated. The purpose is to coordinate the charging strategies of all PEVs in SCS to minimize the energy cost of SCS. Therefore, we build a non-cooperative game framework and propose a new price-driven charging control game by considering the overload constraint of the assigned feeder, where each PEV minimizes the fees it pays to satisfy its optimal charging strategy. On this basis, the existence of GNE is given. Furthermore, we employ a distributed algorithm based on forward–backward operator splitting methods to find the GNE. The effectiveness of the employed algorithm is verified by the final simulation results.

ABSTRACTLocal governments often provide tax-subsidy programs to attract corporate investment. Using a game-theoretic real options model between a firm and a government, this paper aims to explore the interaction between the government’s tax-subsidy policy and the firm’s investment and financing decisions. The optimal incentive policies are derived for cooperative and non-cooperative bargaining settings between a government and a firm. We show that it is optimal for the government to offer a tax-subsidy combination in the cooperative setting. However, this is not true for the non-cooperative setting, in which the optimal policy is to only levy taxes with no investment subsidy. Whereas firms always have an incentive to rely on debt financing in the non-cooperative setting, firms are reluctant to issue debt in the cooperative setting. Finally, it is generally optimal for the government to collect taxes at a lower rate in the case of high risk high-tech enterprises.

• Non-cooperative game among heterogeneous agents causes the emergence of a technology. • The emergence of technology leads to a superlinear scaling relationship between GDP and population size. • Past technological progress fails in pursuing a sustainable manner for fossil fuel energy usage. • We propose a tax policy to reconcile the superlinear growth of the GDP and energy sustainability. This study investigates lessons in energy efficiency and sustainability that human societies can learn from insect colonies that exhibit altruistic cooperation. Distinguishing from the altruism-cooperation in eusocial insects, modern market-economy institutions are established based on the human nature of self-interest and a coordinated form of a non-cooperative game. Here, we employ a theoretical model to show that a non-cooperative game among heterogeneous agents causes the emergence of a technology, which in turn leads to a superlinear scaling relationship between the GDP and the population size, in good agreement with the data collected from 106 countries in seven regions of the world. However, although the emergence of technology triggers the superlinear growth of the GDP, our study finds a superlinear relationship between the fossil fuel energy consumption and the population size in six continents, which suggests that past technological progress in fact fails in pursuing a sustainable manner for energy usage. From a sustainability perspective, the preservation of natural resources is a goal of Circular Economy strategy. Thus, the results of our study call for an urgent enhancement of the efficiency of energy usage to avoid the potential collapse of sustainability. [ABSTRACT FROM AUTHOR]

Eco-compensation is an important policy tool to trade off economic development and environmental protection in a watershed. This study introduced the gross ecosystem product (GEP) accounting system to identify the eco-compensation provider (ECP) and eco-compensation receiver (ECR) of a river basin. Then, a cooperative game model and a non-cooperative game model between the ECP and the ECR were established, in which system dynamics was incorporated into the game theoretical analysis to aid selection of the optimal compensation scheme. The study further demonstrated the model applications by taking the Tuojiang River Basin as a case. The results showed that the accounted GEP increased from CNY 32.2 billion in 2018 to CNY 35.5 billion in 2020, with a growth rate of 10.2%. Deyang City, as an ECP, had a cumulative difference of CNY 11.759 billion between its accounted GEP and actual GEP output over the three years. Neijiang City, Zigong City, and Luzhou City were taken as the ECR, receiving the GEP revenues as CNY 11.289 billion, 7.661 billion, and 1.497 billion, respectively. Chengdu City changed from the ECP to the ECR and ultimately to the ECP. However, Ziyang City and Meishan City changed from the ECP to the ECR. The eco-compensations increased to varying degrees during 2018–2020 in both game scenarios, with ECP payments and ECR revenues increasing by 16.5% and 4.6% in the non-cooperative game and by 5.0% and 50.8% in the cooperative game. Particularly, the eco-compensation obtained by the cooperative game had better environmental and economic performance, as there was a total of CNY 7.049 billion being saved. However, there was no savings of compensation by the non-cooperative game. It is expected that the results may provide insight into the design of the eco-compensation standard for a river basin. [ABSTRACT FROM AUTHOR]

Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry, the coordinated scheduling problem of parallel machine production and transportation is investigated. The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance. A non-cooperative game model is established, considering the competition and self-interest behavior of jobs from different customers for machine resources. The job from different customers is mapped to the players in the game model, the corresponding optional processing machine and location are mapped to the strategy set, and the makespan of the job is mapped to the payoff. Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model. A Nash equilibrium solution algorithm based on the genetic algorithm (NEGA) is designed, and the effective solution of approximate Nash equilibrium for the game model is realized. The fitness function, single-point crossover operator, and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium. Rules are also designed to avoid the generation of invalid offspring chromosomes. The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes. Compared with other algorithms such as heuristic algorithms (FCFS, SPT, and LPT), the simulated annealing algorithm (SA), and the particle swarm optimization algorithm (PSO), experimental results show that the proposed NE-GA algorithm has obvious performance advantages. [ABSTRACT FROM AUTHOR]

To accomplish IMO's emission reduction targets, the Chinese government has established emission control areas and implemented strict sulfur limitation policies. Faced with a downturn in the shipping industry and the challenge of an insufficient supply of compliant fuel, Hong Kong and Shenzhen in China have implemented different low-sulfur fuel oil subsidy policies. It is particularly important to study non-cooperative games between two ports considering low-sulfur fuel oil subsidies. In this paper, first, non-cooperative game models considering low-sulfur fuel oil subsidies are constructed. Second, the mechanisms of various factors affecting port pricing, throughput and profit are analyzed. Then, a case study is conducted using AIS data of container ships in Shanghai and Ningbo-Zhoushan ports. The study reveals that in both sequential and simultaneous games, the gross tonnage of a ship has an impact on the optimal service price, throughput and profit of the port. The subsidy rate has a positive impact on the profitability of the port itself, to the detriment of competitor ports. In conclusion, a low-sulfur fuel oil subsidy policy has a significant positive impact on the step-by-step implementation of more stringent air pollution reduction policies in port waters. [ABSTRACT FROM AUTHOR]

Rate-Splitting Multiple Access (RSMA) has recently found favor in high-mobility scenarios due to the benefits of relaxing the accuracy of Channel State Information at the Transmitter (CSIT) while maintaining high spectral efficiency. These benefits are particularly important in Satellite-Vehicular Networks (SVNs), where the mobility of the vehicles significantly affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based satellite-vehicular communication system for reliable data transmission. Specifically, we investigate the outage probability of the RSMA-based satellite-vehicular communication system under the Shadowed-Rician model. Considering the satellite-vehicular communication outage problem, an optimization problem is formulated to maximize the Weighted Sum Rate (WSR) of the communication system. However, due to communication overhead and privacy concerns, not all vehicles are willing to participate in interference management. In this study, we exploit an incentive mechanism to efficiently solve this problem. Specifically, we formulated a non-cooperative game to motivate users to report the CSIT and participate in the power allocation. In addition, we have proved the existence and uniqueness of Nash Equilibrium (NE) in the game formulated. Our simulation results show that the proposed non-cooperative game theoretical approach achieves the effectiveness of the RSMA-based satellite-vehicular system. The game theoretical framework is shown to motivate users to participate in interference management, so as to maximize their transmission rates.

This study investigates the dynamics of endogenous order placement timing among competing retailers within a single period, driven by the evolution of demand-forecast information. Despite the critical role of accurate market trends and demand forecasts in determining firm success during selling seasons, the existing literature lacks a comprehensive understanding of how firms strategically adjust their order timing with imperfect and evolving information landscapes. By leveraging resources such as predictive analytics systems operated by big data and social media, firms tend to enhance their market demand precision as the selling season approaches, aligning with market practices. With this background, we aim to address the strategic behaviors of competing retailers in timing their orders, filling the aforementioned research gap. We construct a non-cooperative game-theoretical model to analyze the strategic behaviors of competing retailers in timing their orders. The model incorporates factors such as imperfect and evolving information landscapes, considering how firms leverage resources to enhance their market demand precision as the selling season approaches. Our analysis shows two primary equilibria, each shedding light on distinct strategic choices and their implications. First, the better-informed firm decides to execute early orders, capitalizing on the first mover’s advantage, particularly when initial information imprecision exceeds a specific threshold. Conversely, a second equilibrium emerges when the better-informed firm delays its orders, yielding the first mover’s advantage to the less-informed competitor. These equilibria highlight the correlation between order timing strategies and the trajectory of information evolution within the competitive landscape. Additionally, our study extends beyond equilibrium analysis to investigate these strategic choices on supply-chain performance.

To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs’ driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver’s dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics.

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

In case of supply disruption following major disasters, many supply chains tend to break down due to stock-outs and take a long time to recover. However, by keeping emergency sources of supply, some supply chains continue to function smoothly even after a major disaster. In this work, using a game-theoretic-framework, we consider a two-suppliers-one-retailer supply chain with price-dependent stochastic demand in which suppliers are prone to disruption. To investigate the impact of supply disruption we consider two models: SC model, in which the retailer does not maintain any emergency sources of supply against any supply disruption, and SCB model, in which the retailer maintains a backup supplier to mitigate the impact of supply disruption. We mainly focus on the pricing strategies of the suppliers and the mitigating strategies of the retailer under supply and demand uncertainty. We address two coordinating mechanisms to enhance supply chain performance. Our results indicate that in the presence of supply disruption, even with lower probabilities, the retailer would always prefer to take the advantage of a backup supplier and the optimal reserve quantity increases with disruption probabilities. We further investigate the scenario in which the suppliers would always prefer to cooperate with each other. [ABSTRACT FROM AUTHOR]

Adding a reputation incentive system to peer-to-peer (P2P) energy transactions can encourage prosumers to regulate their trading behavior, which is important for ensuring the efficiency and reliability of P2P transactions. This study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value incentives. First, the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value, and a reputation incentive model of P2P transactions for prosumers was constructed. Then, the penalty coefficient was applied to the cost function of the prosumers, and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was established. Furthermore, the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function, and the Nash equilibrium solution of the game was obtained via a relaxation algorithm. Finally, the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation value. The results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P transactions. It can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism. [ABSTRACT FROM AUTHOR]

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers. The jobs of two agents compete for the processing position on a machine, and after the processed, they compete for the transport position on a transport vehicle to be transported to two agents. The two agents have different objective functions. The objective function of the first agent is the sum of the makespan and the total transportation time, whereas the objective function of the second agent is the sum of the total completion time and the total transportation time. Given the competition between two agents for machine resources and transportation resources, a noncooperative game model with agents as game players is established. The job processing position and transportation position corresponding to the two agents are mapped as strategies, and the corresponding objective function is the utility function. To solve the game model, an approximate Nash equilibrium solution algorithm based on an improved genetic algorithm (NE-IGA) is proposed. The genetic operation based on processing sequence and transportation sequence, as well as the fitness function based on Nash equilibrium definition, are designed based on the features of the two-agent production and transportation coordination scheduling problem. The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes. When compared to heuristic rules such as the Longest Processing Time first (LPT) and the Shortest Processing Time first (SPT), the objective function values of the two agents are reduced by 4.3% and 2.6% on average. [ABSTRACT FROM AUTHOR]

Smart communication systems improve VANETs with the assistance of intelligent software systems. Such electronic tools allow automatic traffic control services to reduce road injuries and to classify routes effectively. Such automated traffic control networks are vulnerable to many kinds of attacks. Denial of service attack is a type of attack when the malicious node disrupts the network's entire communication by flooding the packets. Many methods of detecting DoS attacks in VANETs are available in the literature, but they are still hard to detect and mitigate attacks. Due to these attacks, the vehicle user cannot transmit emergency alert messages and disseminate the traffic data to the destination vehicle in due time. A new detection algorithm is developed and suggested in this analysis to detect and mitigate a malicious node using a game-theoretical approach. Stackelberg's game technique is designed to identify attacks, reduced the packet drop ratio by 12.45%, and increased the throughput by 94.21% compared to the Packet Detection Algorithm, Attacked Packed Detection Algorithm, and Enhanced Attacked Packet Detection Algorithm. [ABSTRACT FROM AUTHOR]

Technological developments play a crucial role in allowing governments and industries to meet carbon emission targets, whilst maintaining cost effectiveness. Mathematical modeling related to climate change has often included technology (including technology transfer between nations) as an effective policy instrument. However, such models often incorporate technology as an exogenous variable, highlighting the need to further interrogate the role of technology, its dynamics and limitations on reducing international pollution levels to improve sustainability, energy reliability and subsequent policy initiatives. Hence, in this study, we consider technology as an endogenous variable within a broader trans-boundary industrial pollution problem with random interference factors to obtain a closed-loop (Markov perfect) Nash equilibrium. We then articulate the Nash non-cooperative and cooperative equilibria via a stochastic linear quadratic differential game paradigm and prove the stability of a cooperative game by using Pareto optimal solution. We show that under such strategies to control carbon pollution a cooperative game is more efficient than a non-cooperative game, emphasizing the importance of technology transfer and collaboration between nations, subsequently serving as a mutual benefit for multi-lateral efforts to reduce global carbon emissions. In doing so, our study highlights the role of government subsidy incentives when collaborating with industry to encourage the integration of carbon-reducing technologies, whilst simultaneously increasing each country's net revenue. Hence, our study provides a novel insight and framework for policymakers when encouraging industry to use carbon capturing and storage technologies. We also emphasize that efforts to coordinate emissions control should be pursued jointly to ensure mutual benefit for government and industry alike. [ABSTRACT FROM AUTHOR]

China's carbon peak strategy constrains industry's carbon footprint, limiting the total amount of electricity production from coal burning, rendering electricity a relatively scarce resource and affecting manufacturing enterprises' production and operation activities. To address these circumstances, this paper applies a non-cooperative game model to describe the electricity resource distribution and product production process under enterprises' competitive state, proposing an optimal electricity distribution analysis framework for manufacturing enterprises. The results of optimal electricity distribution for six subindustries in the 'three innovations' manufacturing industries (referring to a collection of economic activities to establish new industries, new formats and new business models) are analysed to examine the difference in electricity consumption efficiency among industries. The electricity quota of each industrial chain significantly differs before and after redistribution;therefore, the industrial differences of optimal electricity distribution are further explored from the perspective of industrial chains, revealing the supply–demand relationship of upstream, middle and downstream enterprises. Finally, corresponding policy suggestions and directions for further research are presented. [ABSTRACT FROM AUTHOR]

The fixed cost generated from constructing a common platform by all units needs to be allocated among these units in a reasonable way in many management practices. As market information is often incomplete, data envelopment analysis (DEA) approaches have been successfully used in fixed cost allocation problems. The fixed cost was treated as an input measure in previous studies. However, it has no effect on the original inputs and outputs when making the allocation decision after an operating cycle. In this case, it can be regarded as a reduction of outputs rather than an input. This paper developed a new DEA approach based fixed cost allocation model in a decentralized decision-making environment that treats the fixed cost as a reduction of outputs. Unlike the existing approaches addressing efficiencies, the proposed model considers the cost itself in the allocation process. An iterative procedure is then presented to derive the average game cost allocation scheme. A classical numerical example from the prior literature is given to show the applicability of the proposed model. The simulation results show that the developed approach can obtain a unique allocation scheme. [ABSTRACT FROM AUTHOR]

In this paper, a modified distributed Nash equilibrium seeking problem with input saturation has been investigated. The payoff function of each player is related to the communication topology, which is closer to the actual scenes. The sigmoid function is utilized to limit the range of the input. Through the network communication between players and the gradient of the players' payoff functions, the finite-time and fixed-time distributed Nash equilibrium seeking protocols with input saturation are given. Using the Lyapunov stability analysis, it is determined that the action of each player converges to the Nash equilibrium if all the players update their action according to the proposed algorithms. The numerical simulations are also provided to testify the algorithms. [ABSTRACT FROM AUTHOR]

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In order to improve the initiative of lane changing game between connected and automated vehicles and drivers vehicles in the upstream section of the signalized intersection, the left-turn connected and automated vehicle was taken as the research object to analyze the characteristics of the forced lane change game in this section. Firstly, by analyzing the driving intention and lane change behavior of vehicles in the upstream section of the signalized intersections, the drivers expectation function was set to objectively reflect the driving demand of the vehicle, and the safety and driving efficiency of the vehicle were taken as the benefits and quantified. Under the assumption of complete information, the optimal lane changing decision was obtained through the game equilibrium solution to maximize the lane changing benefit. Secondly, in order to improve the comfort of lane changing, a five-degree polynomial was used to plan the lane change trajectory and the game lane changing process between connected and automated vehicles and driver vehicles was realized. Finally, the simulation test was used to verify the model, and the influence of different lane changing positions and remaining green time on the decision-making of connected and automated vehicles were analyzed. The results indicate that in the process of noncooperative game forced lane changing at signalized intersection, the lane changing probability increases by an average of 0.69 % with the increase of the unit lane change position, and the vehicle lane changing probability decreases by an average of 0.82 % with the increase of the unit green light remaining time. The game lane changing characteristics and decision tendency of vehicles on upstream sections of signalized intersections are analyzed by simulation, which is conducive to providing decision guidance for lane changing of connected and automated vehicles. [ABSTRACT FROM AUTHOR]

As an energy-intensive industry, the energy and environmental issues of non-ferrous metals industry receive increasing attention. Dividing the production of non-ferrous metals into two sub-stages including mining and processing (M&P) and smelting and pressing (S&P), a non-cooperative game network data envelopment analysis model with double viewpoints (optimistic and pessimistic) is developed to evaluate the energy and environmental efficiency of China's non-ferrous metals industry. Moreover, we construct a novel network Malmquist productivity index to identify the dynamic evolution of energy and environmental performance. The results of 25 provinces over 2010–2017 show that energy and environmental efficiency of China's non-ferrous metals industry is very low during the study period, and the efficiency gap of two sub-stages increases significantly over time. Additionally, the energy and environmental efficiency in eastern region is more affected by the M&P sub-stage, while the efficiency of that in central and western regions is greatly influenced by the S&P sub-stage. Technical progress plays a key role in the productivity improvement of the M&P sub-stage, but the main driving force of productivity change in the S&P sub-stage is different in the three regions. Based on above findings, several targeted policy implications are suggested to prompt the sustainable development of China's non-ferrous metals industry. [ABSTRACT FROM AUTHOR]