Game Theoretic Approach Research Articles

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.

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.

Understanding after-market service channel selection for complex product-services supply chains: A game-theoretical approach

Today, cooperation between an OEM (Original Equipment Manufacturer) of Complex Products and Systems (CoPS) and her multiple Key Component Suppliers (KCSs) is characterized by a strategic partnership where long-term benefits are sought for both parties. Since CoPS after-market service can be provided by the KCS, the OEM, or an MRO (Maintenance, Repair, and Overhaul) party, different after-market service channels can be established. In this paper, we model each of these channels using game theory, compare their main features, and discuss the selection of the optimal after-market service channel. We show that the duration of the after-market service is not influenced by the service channel if the service cost remains constant. If the after-market service costs are equal for all parties, each party would prefer the other to perform the service. In the case of different costs, both the cost of the service and potential market size have the same effect on the OEM’s preference for the after-market service channel as on the CoPS ordered quantities. Furthermore, OEM and KCS after-market service channel preferences depend on market size, and “win-win” situations are discussed for the different channels, along with their impact on the long-term stability of the strategic partnership between KCS and OEM. Finally, a sensitivity analysis is carried out to examine the impact of several key parameters. The results presented in the paper serve to deepen the understanding of complex product-service systems and offer a number of managerial insights to select an appropriate after-market service channel for KCSs.

ІНКРЕМЕНТНИЙ ПІДХІД ДО ПЛАНУВАННЯ РОБОТИ СТУДЕНТІВ НАД СПІЛЬНИМ ПРОГРАМНИМ ПРОЄКТОМ

As of today, well-known product and outsourcing IT companies use Agile models for the software development life cycle (SDLC), the advantages of which include rapid adaptation to changes, continuous feedback, improved product quality, increased team engagement, focus on customer value, transparency and control, as well as the reduction of internal and external risks. All Agile models are iterative, meaning the development process is divided into separate, similar iterations, which have different names in different models. Familiarization with and practical application of modern Agile models by students at the first (bachelor’s) level of higher education in the field of information technology is an important task for future IT specialists. To achieve this task, the most popular Agile model, Scrum, was chosen, which is also incremental, meaning that each iteration delivers additional functionality that is ready for use. The main roles of participants and their responsibilities, artifacts, and ceremonies of the Scrum process are reviewed. The article proposes applying the Scrum model for iterative and incremental software development that will solve a multi-criteria optimization problem using a game-theoretic approach. The formulation and general algorithm for solving the multi-criteria optimization problem using a game-theoretic approach are presented, as well as an example of formulating the Product Backlog, which contains twelve functional requirements to be implemented by the student Scrum team over twelve iterations (sprints). The recommended process for dividing students into Scrum teams and assigning roles to participants is described. An example of incremental implementation based on the Scrum model of the SDLC for solving the multi-criteria optimization problem using a game-theoretic approach is also provided.

The Social Contagion of Risk Perceptions of Emerging Technologies through Evolutionary Game in Networks

Emerging technologies are leading the fourth industrial revolution, bringing profound changes to modern life. However, the contagion of public risk perceptions pertaining to these technologies may result in new social stability risks according to the social amplification of risk framework (SARF). As such, understanding the formation and contagion mechanisms behind public risk perceptions of emerging technologies is critical to promoting their increased receptivity. Based on an evolutionary game theoretical approach to complex networks, this paper constructs a social contagion model of the risk perception of emerging technologies and uses simulations to analyze the influence of factors such as individual cognition and social environment. Results indicate the following: (1) the rate of risk perception contagion gradually increases with the level of individual risk perception and trust in communicators; (2) the risk perception contagion rate gradually decreases as the level of risk governance participation increases; and (3) comparing different social networks, kinship networks have a greater effect than social media networks on risk perception contagion.

Evolutionary game theoretical approach for reducing carbon emissions in a complex supply chain organization

PurposeOur research explores the intricate behavior of low-carbon supply chain organizations in an ever-evolving landscape, emphasizing the profound implications of government-mandated low-carbon policies and the growing low-carbon market. Central to our exploration is applying a combined game theory model, merging Evolutionary Game Theory (EGT) with the Shapley Value Cooperative Game Theory Approach (SVCGTA).Design/methodology/approachWe establish a two-tier supply chain featuring retailers and manufacturers within this novel framework. We leverage an integrated approach, combining strategic Evolutionary Game Theory and Cooperative Game Theory, to conduct an in-depth analysis of four distinct low-carbon strategy combinations for retailers and manufacturers.FindingsThe implications of our findings transcend theoretical boundaries and resonate with a trinity of economic, environmental and societal interests. Our research goes beyond theoretical constructs to consider real-world impacts, including the influence of changes in government low-carbon policies, the dynamics of consumer sensitivities and the strategic calibration of retailer carbon financing incentives and subsidies on the identified ESS. Notably, our work highlights that governments can effectively incentivize organizations to reduce carbon emissions by adopting a more flexible approach, such as regulating carbon prices, rather than imposing rigid carbon caps.Originality/valueOur comprehensive analysis reveals the emergence of an Evolutionary Stability Strategy (ESS) that evolves in sync with the phases of low-carbon technology development. During the initial stages, our research suggests that manufacturers or retailers adopt low-carbon behavior as the optimal approach.