Search Results (9)

In this paper, we address the challenges posed by limited knowledge in security games by proposing a novel system grounded in Bayesian–Markov Stackelberg security games (SSGs). These SSGs involve multiple defenders and attackers and serve as a framework for managing incomplete information effectively. To tackle the complexity inherent in these games, we introduce an iterative proximal-gradient approach to compute the Bayesian Equilibrium, which captures the optimal strategies of both defenders and attackers. This method enables us to navigate the intricacies of the game dynamics, even when the specifics of the Markov games are unknown. Moreover, our research emphasizes the importance of Bayesian approaches in solving the reinforcement learning (RL) algorithm, particularly in addressing the exploration–exploitation trade-off. By leveraging Bayesian techniques, we aim to minimize the expected total discounted costs, thus optimizing decision-making in the security domain. In pursuit of effective security game implementation, we propose a novel random walk approach tailored to fulfill the requirements of the scenario. This innovative methodology enhances the adaptability and responsiveness of defenders and attackers, thereby improving overall security outcomes. To validate the efficacy of our proposed strategy, we provide a numerical example that demonstrates its benefits in practice. Through this example, we showcase how our approach can effectively address the challenges posed by limited knowledge, leading to more robust and efficient security solutions. Overall, our paper contributes to advancing the understanding and implementation of security strategies in scenarios characterized by incomplete information. By combining Bayesian and Markov Stackelberg games, reinforcement learning algorithms, and innovative random walk techniques, we offer a comprehensive framework for enhancing security measures in real-world applications. Full article

Against the backdrop of an increasingly sound supply chain system and thriving e-commerce, it is becoming increasingly common for retailers to introduce their own brands of products and for manufacturers to open up direct online sales channels. The existence of these two supply chain decisions is complex and involves interactions. Moreover, the introduction of online direct sales channels will bring about differences in prices and service quality between channels, resulting in a free-riding effect on the internet. However, existing related research rarely considers the role of network free-riding effect in this supply chain system. This article integrates the network free-riding effect into the supply chain model by setting the network free-riding rate. According to whether retailers introduce their own brand products and manufacturers open up online direct sales channels, four supply chain scenarios are formed, and the reverse recursion method is used to obtain the profit functions for each of these four scenarios. Then, a Stackelberg game model is established to determine the response strategies of manufacturers and retailers based on the changes in profits of manufacturers caused by retailer decisions and the changes in profits of retailers caused by manufacturer decisions. Through analysis, it was discovered that a key factor affecting decision-making within the supply chain system is the retailer’s channel advantage. When the channel advantage of retailers is strong, manufacturers will open up online direct sales channels to weaken the channel advantage of retailers. Retailers will not introduce their own brand products but are more inclined to cooperate with manufacturers. When the channel advantage of retailers is weak, retailers will attract consumers and consolidate their channel advantage by introducing high-quality, low-priced private label products, while manufacturers will maintain cooperation with retailers and adopt a strategy of not opening up online direct sales channels. We also analyzed the Nash equilibrium state under different channel advantages of retailers. Full article

An integrated zero-carbon power plant aggregates uncontrollable green energy, adjustable load, and storage energy resources into an entity in a grid-friendly manner. Integrated zero-carbon power plants have a strong demand response potential that needs further study. However, existing studies ignore the green value of renewable energy in power plants when participating in demand response programs. This paper proposed a mathematical model to optimize the operation of an integrated zero-carbon power plant considering the green value. A demand response mechanism is proposed for the independent system operator and the integrated zero-carbon power plants. The Stackelberg gaming process among these entities and an algorithm based on dichotomy are studied to find the demand response equilibrium. Case studies verify that the mechanism activates the potential of the integrated zero-carbon power plant to realize the load reduction target. Full article

Critical infrastructure is essential for the stability and development of modern society, and a combination of complex network theory and game theory has become a new research direction in the field of infrastructure protection. However, existing studies do not consider the fuzziness and subjective factors of human judgment, leading to challenges when analyzing strategic interactions between decision makers. This paper employs interval-valued intuitionistic fuzzy numbers (IVIFN) to depict the uncertain payoffs in a Stackelberg game of infrastructure networks and then proposes an algorithm to solve it. First, we construct IVIFN payoffs by considering the different complex network metrics and subjective preferences of decision makers. Next, we propose a lexicographic algorithm to solve this game based on the concept of a strong Stackelberg equilibrium (SSE). Finally, we conduct experiments on target scale-free networks. Our results illustrate that in an SSE, for the defender in a weak position, it is better to defend nodes with high degrees. The experiments also indicate that taking fuzziness into account leads to higher SSE payoffs for the defender. Our work aims to solve a Stackelberg game with IVIFN payoffs and apply it to enhance the protection of infrastructure networks, thereby improving their overall security. Full article

We studied a supply chain consisting of multiple suppliers and multiple retailers. We use the Cournot–Stackelberg game, the Market–Stackelberg game, and the Market–Nash game to simulate the situation where the upstream seller’s market dominance power gradually decreases while the downstream buyer’s market power increases. The equilibrium decision and supply chain performance under the three models are compared and analyzed, as well as their responses to external market changes such as demand fluctuation and market competition intensity. The research shows that (1) in a seller-dominated supply chain, the increase in buyer power reduces market equilibrium production and wholesale price; (2) in the face of strong demand fluctuations, equivalent power between upstream and downstream can contribute to the stabilization of production and wholesale prices; (3) when market demand fluctuation is small, market power brings a higher profit level, and supply chain participants would like to actively compete for market power. However, when the demand fluctuates greatly, the profit advantage brought by market dominance is no longer significant, and there is no need to spend much to fight for market dominance; (4) the fierce competition of upstream suppliers will induce upstream to give up the competition for market dominance, and make the market power less attractive to downstream retailers. While the fierce horizontal competition downstream will stimulate both suppliers and retailers to actively compete for market power, (5) sufficient market competition will improve total supply chain profit, so encouraging competition is conducive to the overall economic development of society. Full article

Reinforcement learning has shown a great ability and has defeated human beings in the field of real-time strategy games. In recent years, reinforcement learning has been used in cyberspace to carry out automated and intelligent attacks. Traditional defense methods are not enough to deal with this problem, so it is necessary to design defense agents to counter intelligent attacks. The interaction between the attack agent and the defense agent can be modeled as a multi-agent Markov game. In this paper, an adversarial decision-making approach that combines the Bayesian Strong Stackelberg and the WoLF algorithms was proposed to obtain the equilibrium point of multi-agent Markov games. With this method, the defense agent can obtain the adversarial decision-making strategy as well as continuously adjust the strategy in cyberspace. As verified in experiments, the defense agent should attach importance to short-term rewards in the process of a real-time game between the attack agent and the defense agent. The proposed approach can obtain the largest rewards for defense agent compared with the classic Nash-Q and URS-Q algorithms. In addition, the proposed approach adjusts the action selection probability dynamically, so that the decision entropy of optimal action gradually decreases. Full article

In big cities, there are more and more parking lots and charging piles for electric vehicles, and the resources of parking and charging vehicles can be aggregated to provide strong computing power for vehicular edge computing (VEC). In this paper, we propose a VEC framework that uses charging vehicles in parking lots to assist edge servers in processing computational tasks, and an edge crowdsourcing platform (ECP) is designed to manage and integrate the idle computation resources of electric vehicles in parking lots to provide computation services for requesting vehicles. Based on game theory, we first model the interactions among the edge server, the ECP and the requesting vehicles as a Stackelberg game, and theoretically prove the existence of a Nash equilibrium for this Stackelberg game. Then, a genetic algorithm-based game-strategy solving algorithm is proposed to find the optimal strategy for the edge server and ECP. The simulation results demonstrate that the performance of our proposed solution is better than other traditional solutions. Compared with the solution without ECP, our solution can increase the utilities of the edge server and the requesting vehicle by 13.3% and 10.99%, respectively. Full article

Excellent service plays a vital role in the sustainability of enterprise and supply chains development in today’s increasingly fierce market competition. However, due to the inevitable spillover effect in the competitive network, enterprises’ initiative to improve the service level is reduced. From the perspective of negative spillover effect, optimization and decision-making in the competitive network of retailer-dominated supply chain are examined in this study. Considering four competitive situations in practical operation management, the corresponding double-layer compound nested Stackelberg game models are constructed, and the optimal equilibrium solutions are derived. Employing comprehensive comparison and analysis of the results, it is found that when the negative spillover effect of service increases, the optimal profit and service level of the leading supply chain or its retailers decrease, and the optimal retail price and overall optimal profit also gradually decline. For the leading supply chain, the centralized decision-making can achieve higher profits, and also more willing to improve the level of service. However, for the following supply chain, when the negative spillover effect of service is weak, the optimal service level under decentralized decision is higher, while when the spillover effect of service is strong, the optimal service level under integrated decision is higher. In addition, the supply chain-to-chain competition can bring negative incentives to the retailer that provides services, while for the rival that does not provide services, it can generate a certain free-riding effect that benefits them, and the effect is enhanced with the increase of competition. Full article

The collaboration between renewable energy (RE) and an electric vehicles battery switch station (BSS) is a win-win strategy for both. In order to effectively coordinate and manage RE and BSS belonging to different investors, a new cooperative operation mode considering a multi-stakeholder scenario is proposed. By analyzing the relationship of cooperation and competition between them, their pursuit of maximizing their own interests is modeled as a Stackelberg game model. As the leader, the RE company determines the charge/discharge prices to guide the demand response of BSS. As the follower, the BSS optimizes its charge/discharge strategy according to the prices to maximize its own profits. The proposed model no longer requires that the RE company and BSS belong to a same interest subject, and there is no need for unified coordinated control between them. To solve the game equilibrium, a solution combining the backward-induction method and genetic algorithm is proposed by comparing and analyzing the characteristics of the strong and weak Stackelberg equilibriums. Finally, the effectiveness of the proposed model was validated by case studies. The simulation results show that both the RE company and the BSS have the motivation to participate in the game, and that a win-win outcome can be achieved automatically in the process of pursuing their own interest maximization in the game. Full article