Game Theoretic Approach Research Articles

Analysis of development of fifth generation communication networks

The article is devoted to a comprehensive analysis of key aspects of the development of fifth-generation (5G) networks in Ukraine. Current problems and prospects for the implementation of 5G are studied, taking into account technological, economic and regulatory aspects. Mathematical models and algorithms are proposed for optimizing the allocation of radio frequency spectrum, placement of base stations, implementation of virtualization of network functions Network Function Virtualization (NFV) and software-configured networks Software-Defined Networking (SDN), as well as ensuring quality of service (QoS) for different classes of traffic. The developed solutions allow to increase the efficiency of deployment and operation of 5G networks in conditions of limited resources. A mathematical model of spectrum allocation optimization using linear programming has been developed, which takes into account the efficiency of spectrum use by different operators. To optimize the placement of base stations, an approach based on game theory is proposed, which takes into account bandwidth limitations and placement costs while ensuring the necessary coverage. A mathematical model is presented for optimizing the placement of virtualized network functions on servers with limited resources in order to minimize costs and ensure the required performance. An algorithm based on gradient descent for dynamic resource management and QoS is proposed. Based on a comparative analysis of optimization methods, an integrated approach to solving key problems of 5G implementation is developed. A mathematical model for optimizing the allocation of radio frequency spectrum based on linear programming is proposed, which provides an increase in spectral efficiency by 23–27 % compared to basic methods. A game-theoretic approach to optimizing the placement of base stations is presented, which allows reducing the number of overloaded sectors by 45 % and increasing energy efficiency by 13.3 %. A genetic algorithm for optimizing the placement of virtualized network functions (VNF) is developed, which provides a 21.4 % higher resource utilization. Experimental studies have confirmed that the proposed solutions allow achieving an overall increase in network efficiency by 20–25 % while reducing operating costs by 15–20 %. The results create a methodological basis for effective planning and deployment of 5G networks in Ukraine.

Cognitive Impairment Diagnosis - A Game Theoretic Approach of Cognitive Machine Learning

Cognitive Impairment Diagnosis - A Game Theoretic Approach of Cognitive Machine Learning

A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights From Highly Autonomous Aggressive Malware

A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights From Highly Autonomous Aggressive Malware

Power Efficient Handoff Management in Hybrid V2X Communication: Game-Theoretic Approach to Resource Allocation With Load Reduction

Power Efficient Handoff Management in Hybrid V2X Communication: Game-Theoretic Approach to Resource Allocation With Load Reduction

Correction: Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

Correction: Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

Joint Design and Pricing Problem for Symbiotic Bioethanol Supply Chain Network: Model and Resolution Approach

To fight climate change, the Province of Quebec, Canada, has set targets to reduce greenhouse gas emissions by reducing fossil fuel consumption and integrating biofuel content into gasoline and diesel fuel. Motivated by a real-world case study, this paper presents a novel distributed decision model for designing a symbiotic supply chain network and supporting pricing decisions. A distributed decision-making problem is formulated as a game theoretic approach considering a Stackelberg–Nash equilibrium. A novel mathematical model is proposed to support the decisions of four actors: corn farms, processing depots, pig farms, and biorefineries. In addition to the configuration of a biofuel-based industrial symbiosis, the model offers the possibility of setting purchase prices and supply levels for biomass (corn stover supplied by farms), as well as determining sales prices and production levels for the main product (the cellulosic sugar used for the bioethanol production) and a coproduct (pig feed sold to pig farmers). A three-step optimization process involving the user is proposed to address the computational challenges posed by large design problem instances. The case study of the Province of Quebec is used to evaluate the performance of the proposed resolution approach.

A game-theoretic approach for investigating the amount of land assigned to biomass cultivation and adopt the degree of technology by biorefinery considering government intervention: A case study of Iran

In recent years, there has been a change of approach in the field of using renewable energy and transitioning from fossil energy. Among its reasons, we can mention climate changes and global warming, population growth and increasing demand for energy, the limitation of fossil resources and their price increase, and issues such as energy security. Renewable energy has different types such as solar energy, biofuel energy, wind energy, geothermal energy, etc. Reports show that most of the pollution is related to the transportation sector. Since bioenergy can be used for heat, electricity, and fuel purposes, some countries use it as an alternative or hybrid fuel. Using biofuels has economic, social, and environmental advantages and helps sustainable development. In the current research, the interaction between the farmer, the biorefinery, and the government has been investigated under six scenarios. The farmer determines the amount of land allocated to second-generation biomass cultivation. Biorefinery specifies the technology level and process of biofuel production. Since biofuel production processes are regularly being investigated in laboratories and have a growing trend, The concept of technology level is also considered to examine different states of low-level technology to high-level technology. In some scenarios, the government adopts a policy of indifference, and in some scenarios an active support policy by providing subsidies. At the end, a numerical example is presented numerical sensitivity analysis is performed, and management results are expressed. The results show that the input rate of biomass to the biorefinery is effective in adopting the technology level. Also, the highest price of biomass exists in the case where the farmer decides to develop the land with a non-linear cost because the costs increase.

Optimizing third party funding to alleviate global drug resistance: A game-theoretic modeling approach

Increasing evidence suggests that human movement, including international travel and mass forced population displacement, acts as a catalyst for global antimicrobial resistance (AMR) transmission. Immigration increases the likelihood of antibiotic-resistant bacterial strains being imported into developed countries, particularly from several developing countries with high antimicrobial resistance. On the other hand, self-medication, easy access to over-the-counter drugs, promiscuous use, and lax oversight all contribute to an increase in antibiotic use in most lower-middle- and middle-income countries. It leads to antimicrobial resistance and an increased health burden, particularly in high-income countries. Third-party funding agencies are working to reduce the global antimicrobial resistance burden. Understanding the complex system of antibiotic overuse, third-party funding, and cross-border transmission of antimicrobial-resistant strains is a critical task for public health policymakers. In this study, a modeling framework is developed using a combination of epidemiological and game theory methods. This framework aims to describe the phenomenon of migration between countries, specifically focusing on the movement between developing and developed nations. Additionally, the framework incorporates the analysis of self-medication behavior within the developing population. Simulations of the model are conducted, followed by a sensitivity analysis, in order to investigate the influence of various parameters on the optimal funding amount. Our findings indicate that the total cost due to treatment of infection and funding is a convex function of the amount of third-party assistance. This means that there is an optimal level of funding in place to keep the total cost of antimicrobial resistance as low as possible. The sensitivity analyses in relation to various epidemiological and behavioral parameters indicate that the optimal fund is highly dependent on the other parameters of the system. Third-party assistance may be required, as predicted, to reduce the amount of self-medication, but it is only beneficial up to a certain point. These findings result in useful policy recommendations as well as intriguing new research directions.

Decisions on group buying option in a duopoly environment

Group buying has emerged as a distinctive strategy in today's product and service markets, offering low-priced products to a large number of customers. As many sellers now offer both group and individual purchasing options, this pricing model benefits both sellers and buyers, with sellers reducing prices to drive higher sales volume. In this study, we investigate the competition between two retailers selling substitutable products in the same market through both group and individual purchasing mechanisms. The profit function of duopoly retailers is represented, using a game theoretic approach to evaluate the conditions under which group buying is offered. Additionally, we address the challenge of determining individual and group prices, as well as the optimal group size. The numerical analysis reveals that in collectivist-dominated markets, where group buying is easily viable, offering group prices may not be beneficial for retailers. While, in individualist-dominant markets, retailers tend to provide both group and individual prices.

AI-Driven Route Planning and Scheduling for Electric School Buses

This review paper explores the current state, recent advancements, challenges, and future perspectives of AI-driven approaches for route planning and scheduling of Electric School Buses (ESBs). The integration of artificial intelligence into energy management systems for electric vehicles has gained significant attention, particularly in optimizing school transportation. This study examines various AI techniques, including genetic algorithms, reinforcement learning, and game-theoretic approaches, applied to ESB management. Key focus areas include energy consumption estimation, battery capacity optimization, and Vehicle-to-Grid (V2G) strategies. The paper also addresses critical challenges such as data integration, security concerns, and operational constraints. Future perspectives highlight the potential of advanced AI techniques, smart grid integration, and personalized transportation solutions. By synthesizing current research and identifying key areas for future development, this review contributes to ongoing efforts to improve the efficiency, sustainability, and overall quality of school transportation systems through AI technologies.

Optimizing Design for Repairability: A Game Theoretic Approach for Incorporating Market Analysis

Optimizing Design for Repairability: A Game Theoretic Approach for Incorporating Market Analysis

The Ukrainian Crisis: a Game-Theoretic Analysis of the Complex Interplay between Ukraine, Russia, and the West

Abstract This study gives a general overview of the Ukrainian crisis through the lens of game theory, examining the motivations and constraints of Ukraine, Russia, and the West. Game theory provides structural concepts that explore trust, cooperation, risk-taking, and coordination challenges within the crisis. The analysis considers the impact of power imbalances and external factors like alliances and historical ties, strategic behavior, and other factors. The article concludes by highlighting key takeaways from the game-theoretic approach that can provide a roadmap towards a deeper analysis, and a structural diplomatic approach to conflict resolution.

Sum Rate Maximization of Ad-Hoc Network in Adversarial Communication Environments: A Game-Theoretic Approach

Sum Rate Maximization of Ad-Hoc Network in Adversarial Communication Environments: A Game-Theoretic Approach

Distributed Resource Allocation Over Multiple Interacting Coalitions: A Game-Theoretic Approach

Distributed Resource Allocation Over Multiple Interacting Coalitions: A Game-Theoretic Approach

Distributed Control of Islanded DC Microgrids: A Passivity-Based Game Theoretical Approach

Distributed Control of Islanded DC Microgrids: A Passivity-Based Game Theoretical Approach

Optimal designs for testing pairwise differences: A graph‐based game theoretic approach

AbstractIn a variety of experimental settings there is an interest in comparing pairs–of–treatments. Such experiments usually address one of the following two scientific questions: (1) is there a difference within any of the selected pairs of treatments? or (2) is there a difference within all of the selected pairs of treatments? In this article, we propose max–min optimal designs for testing the above hypotheses using a graph‐based game theoretic approach. Some of the max–min designs obtained are well known, but not recognized as optimal, others are novel and provide an improvement over naive designs.

Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

Evolutionary game theoretic approach with deep learning for health decision-making in critical environment

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

Preventing Collusion in Indian Airlines: A Game Theoretic Approach

Preventing Collusion in Indian Airlines: A Game Theoretic Approach

Strategic safeguarding: A game theoretic approach for analyzing attacker-defender behavior in DNN backdoors

Deep neural networks (DNNs) are fundamental to modern applications like face recognition and autonomous driving. However, their security is a significant concern due to various integrity risks, such as backdoor attacks. In these attacks, compromised training data introduce malicious behaviors into the DNN, which can be exploited during inference or deployment. This paper presents a novel game-theoretic approach to model the interactions between an attacker and a defender in the context of a DNN backdoor attack. The contribution of this approach is multifaceted. First, it models the interaction between the attacker and the defender using a game-theoretic framework. Second, it designs a utility function that captures the objectives of both parties, integrating clean data accuracy and attack success rate. Third, it reduces the game model to a two-player zero-sum game, allowing for the identification of Nash equilibrium points through linear programming and a thorough analysis of equilibrium strategies. Additionally, the framework provides varying levels of flexibility regarding the control afforded to each player, thereby representing a range of real-world scenarios. Through extensive numerical simulations, the paper demonstrates the validity of the proposed framework and identifies insightful equilibrium points that guide both players in following their optimal strategies under different assumptions. The results indicate that fully using attack or defense capabilities is not always the optimal strategy for either party. Instead, attackers must balance inducing errors and minimizing the information conveyed to the defender, while defenders should focus on minimizing attack risks while preserving benign sample performance. These findings underscore the effectiveness and versatility of the proposed approach, showcasing optimal strategies across different game scenarios and highlighting its potential to enhance DNN security against backdoor attacks.