Game Theory Approach Research Articles

Electricity trading of multiple home microgrids through V2X based on game theory

Currently, a significant number of householders are contentedly participating in energy markets integrated with renewable energy sources (RES) to control global warming. Further, the high penetration of e-mobility influences the net zero concepts globally. However, uncoordinated scheduling of electric vehicles (EV) battery charging/ discharging parked in residential areas would result in critical issues in the power system in terms of energy availability. Thus, running intelligent charging/ discharging approaches can tackle these obstacles while managing green energy efficiently. This paper proposes a novel smart EV charging/ discharging strategy for multiple home microgrids (H-MG) based on user behavior analytics patterns with a non-cooperative game theory approach named Nikaido–Isoda/Relaxation algorithm (NIRA). In the proposed structure, EVs are active loads besides responsive loads in H-MGs and are involved in demand-side management (DSM) to achieve peak shaving. Furthermore, electricity retailers (ER) are encountered with distinct properties following opposite intentions and could participate in the competitive retail electricity market (REM) in residential areas. Therefore, households can maximize their profit by reducing varied costs, such as EV charging and battery degradation, while minimizing their carbon footprint. In addition, this study works on the distributed decision-making process that could provide optimal power-sharing and the best payoff in an unbiased procedure. After crucial investigations, the simulation outcomes demonstrate the effectiveness of the proposed approach in encouraging various households to participate actively in the REM with other agents to increase their profits. According to acquired results, active residents saved their budget 28%–45% through employing intelligent EV charging/ discharging strategies in the REM with dynamic pricing compared to the fixed tariff.

Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach

The significant research carried out on medical healthcare networks is giving computing innovations lots of space to produce the most recent innovations. Pancreatic cancer, which ranks among of the most common tumors that are thought to be fatal and unsuspected since it is positioned in the region of the abdomen beyond the stomach and can’t be adequately treated once diagnosed. In radiological imaging, such as MRI and CT, computer-aided diagnosis (CAD), quantitative evaluations, and automated pancreatic cancer classification approaches are routinely provided. This study provides a dynamic weighted ensemble framework for pancreatic cancer classification inspired by game theory. Grey Level Co-occurrence Matrix (GLCM) is utilized for feature extraction, together with Gaussian kernel-based fuzzy rough sets theory (GKFRST) for feature reduction and the Random Forest (RF) classifier for categorization. The ResNet50 and VGG16 are used in the transfer learning (TL) paradigm. The combination of the outcomes from the TL paradigm and the RF classifier paradigm is suggested using an innovative ensemble classifier that relies on the game theory method. When compared with the current models, the ensemble technique considerably increases the pancreatic cancer classification accuracy and yields exceptional performance. The study improves the categorization of pancreatic cancer by using game theory, a mathematical paradigm that simulates strategic interactions. Because game theory has been not frequently used in the discipline of cancer categorization, this research is distinctive in its methodology.

The Role of Investment in the Equilibrium of International Political Economy: Game Theory Approach

The Role of Investment in the Equilibrium of International Political Economy: Game Theory Approach

Game Theory Approach to Decision-Making Analysis of Dispute Resolution

Game Theory Approach to Decision-Making Analysis of Dispute Resolution

Optimizing Green Strategy for Retired Electric Vehicle Battery Recycling: An Evolutionary Game Theory Approach

As the global new energy vehicle (NEV) industry rapidly expands, the disposal and recycling of end-of-life (EOL) power batteries have become imperative. Efficient closed-loop supply chain (CLSC) management, supported by well-designed regulations and strategic investments, plays a crucial role in sustainable waste power battery recycling. In this study, an evolutionary game theory (EGT) methodology is used to construct a tripartite game model to investigate the interactions among manufacturers, recyclers, and the government to study the decision-making dynamics of green investments. In addition, numerical simulations are performed to evaluate the sensitivity of the relevant parameters on the stability of the evolution of the system. The results reveal that government green subsidies can stimulate early period investments in advanced recycling technologies. However, as the battery recycling industry matures, a ‘free-rider’ behavior emerges among enterprises, which can be mitigated through the imposition of a carbon tax. Eventually, as the industry reaches maturity, manufacturers and recyclers autonomously invest for enhanced profitability. This research provides valuable insights for government policy formulation, facilitating the formal recycling of retired batteries and fostering sustainability in the NEV sector.

Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: d ata p reservation p roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

New Perspectives in Innovation Failure Analysis: A taxonomy of general errors and strategic management for reducing risks

In turbulent environments, one of the fundamental problems is the high risk of failure in new innovation projects that affects organizational behaviour and performance of firms and also nations. The study here clarifies the theoretical structure of project-level failure proposing a new definition, based on systems approach (Innovation failure is when organization does not achieve the main goal of project because of a set of errors), and a taxonomy of three general types of error in innovation failures: errors in planning and/or design, in project execution/implementation and in marketization. A case study research clarifies the proposed framework of innovation failures with examples from pharmaceutical sector, aerospace and aircraft industries, and information & communication technology sector. Results suggest that the failure in innovation projects is due to proposed errors associated mainly with bounded rationality of organizations in dealing with goal difficulty, environmental complexity and uncertainty. Different strategies of problem solving to reduce errors and risks leading to innovation failures and improve technological development are proposed, such as learning processes, adaptation perspective, game theory approach, stage-gate method, etc. The proposed systemic concept of innovation failure and related taxonomy of general typologies of error can guide R&D managers, designers, analysts, and policymakers etc. to know main determinants of failure in innovation projects and how appropriate strategies can improve organizational performance and R&D processes of firms for competitive advantage in markets and society.

Auditing a supply chain with environmental regulations: a game theory approach

ABSTRACT The seriousness of climate change’s impact on people’s lives has compelled policymakers to create rules for businesses, aiming to limit their impact on the environment. Some companies decide to take the risk of breaking these rules instead of spending resources to follow them. In this study, we investigate the decision-making challenge that arises when a company interacts with a recycler. We expand the original model by including a second recycler. The company can decide to audit the recyclers or skip the audit and save resources. The recyclers can choose whether to follow environmental rules or not. The company wants to minimise costs, while the recyclers aim to maximise profits from recycling. We use game theory to represent how the company and the recyclers interact. The study’s findings reveal that adding the second recycler decreases the willingness of the first recycler to take risks and break environmental regulations.

Game Analysis of Different Source Disclosure Model for Genetic Resources and Implications for China

Inventions that rely on genetic resources are subject to different source disclosure models in the domestic laws of different countries. Different source disclosure models can indirectly affect the quantity and quality of patents issued in a country, thus resulting in different social benefits. Therefore, an analysis of which source disclosure model is applicable to the patent protection of genetic resources is of great importance. The economic approach of game theory is used to analyse the game of interests between patent applicants and the patent examination authority. It is found that the patent examination authority is required to introduce source disclosure rules and that the optimal equilibrium for patent applications can be achieved by requiring companies to comply with a voluntary source disclosure model under intense examination pressure. Based on this, the legislative policies of typical resource-rich countries are selected for analysis to verify the validity of the game conclusions. Finally, policy recommendations for source disclosure requirements for genetic resources in China are proposed based on the game conclusions. genetic resources, source disclosure, patent law, game theory, mandatory model, voluntary model, patent applications, regulatory bodies, cost-benefit perspective

How Neutrality Lost its Appeal as a Political Strategy: Sweden and Finland’s Accesion to NATO

NATO is about to embrace two Scandinavian countries as new members, even though Sweden and Finland opted for neutrality during the Cold War. In the political turmoil of the second half of 20th century, those two countries preferred to stay on the sidelines. The struggle between two poles of global politics raged on, yet even after more than three decades since the collapse of Soviet Union, they choose not to commit to the Western alliance. After a period during which existence of NATO was questioned, it is imperative to explain how once again the alliance attracts newer members to its fold. Evidently, Russia-Ukraine war was the trigger behind the change in strategies but the question how this conflict is different than has other Cold War engagement remains. This paper investigates how NATO membership became the optimal strategy for previously non-aligned countries in Europe even after a bipolar world order faded away. A game theory approach outlining the choices and payoffs for Russia versus Sweden and Finland will be used to understand the changing strategies for all actors.

Hidden Object Game As A Supporting Media For The Introduction Of New Vocanulary

Teaching and learning new/foreign vocabulary (language) using game must consider the needs and objectives of teaching and learning. Hidden object games are a type of puzzle game that has many educational benefits. The purpose of this study is to analyze hidden object games by formulating how hidden object games can be a stimulate medium for learning vocabulary, formulating elements of vocabulary education in them, and describing the motivation to recognize and increase insight into new/foreign language vocabulary for hidden object game players. The method for analyzing hidden object games as educational media uses the basic element game theory approach by Fullerton which focuses on analysis on the formal and dramatic elements of games for learning purposes. The results of the study show that hidden object games can educate and increase the players' vocabulary knowledge independently, especially the initial level vocabulary, namely receptive types, basic noun word classes. Hidden object games can accommodate vocabulary learning at the pre-teaching stage for beginner levels. However, hidden object games have not been able to maximize their benefits for higher vocabulary educational purposes such as productive vocabulary.Keywords: Hidden object game; Educational Game; Vocabulary Learning

Blockchain‐assisted twin migration for vehicular metaverses: A game theory approach

AbstractAs the fusion of automotive industry and metaverse, vehicular metaverses establish a bridge between the physical space and virtual space, providing intelligent transportation services through the integration of various technologies, such as extended reality and real‐time rendering technologies, to offer immersive metaverse services for vehicular metaverse users (VMUs). In vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide Units (RSUs) to obtain metaverse services. However, due to the mobility of vehicles and the limited service coverage of RSUs, VT migration is necessary to ensure continuous immersive experiences for VMUs. This process requires RSUs to contribute resources for enabling efficient migration, which leads to a resource‐trading problem between RSUs and VMUs. Moreover, a single RSU cannot support large‐scale VT migration. To this end, we propose a blockchain‐assisted game approach framework for reliable VT migration in vehicular metaverses. Based on the subject logic model, we first calculate the reputation values of RSUs considering the freshness of interaction between RSUs and VMUs. Then, a coalition game based on the reputation values of RSUs is formulated, and RSU coalitions are formed to jointly provide bandwidth resources for reliable and large‐scale VT migration. Subsequently, the RSU coalition with the highest utility is selected. Finally, to incentivize VMUs to participate in VT migration, we propose a Stackelberg model between the selected coalition and VMUs. Numerical results demonstrate the reliability and effectiveness of the proposed schemes.

Synergy between Electric Vehicle Manufacturers and Battery Recyclers through Technology and Innovation: A Game Theory Approach

Power battery recycling (PBR) has triggered profound changes in the industrial chain of electric vehicles (EVs). The PBR innovation network provides information channels and resource conditions for enterprises, but the mechanism of its impact on the synergistic innovation benefits and sustainable development ability of EV and PBR enterprises still needs further exploration. In this paper, we collect patent data for PBR from 2012 to 2020, identify the structural characteristics of innovation networks, and construct a synergy game model for PBR technology, aiming to analyze the synergistic effect of network embedding and knowledge spillover in PBR enterprises on technological innovation. First, we find that the PBR innovation network exhibits the small-world effect, which has a double-edged sword effect on technological cooperation innovation. Second, structural holes benefits of the main body of PBR technological innovation have a significant impact on cooperation innovation behavior. Third, the enhancement of the relevance and deep complementarity of knowledge cooperation is sufficient to make up for the input cost of PBR technological cooperation innovation, with additional benefits created by the increase in the output of structural holes. However, companies tend to be more inclined toward non-cooperative innovation as the knowledge spillover effect of the innovation network increases.

Comparative Analysis of Peer-to-Peer PV Trading Strategies under the Influence of Network Constraints with Prosumer Sensitivity towards Network Coefficients

With the increase in rooftop photovoltaic (PV) systems at the residential level, customers owning such renewable resources can act as a source of generation for other consumers in the same network. Peer-to-peer (P2P) energy trading refers to a local trading platform where the residential customers having excess PV power (prosumers) can interact with their neighbors without PV resources (customers) to improve the social welfare of society. However, the performance of a P2P market depends on the power system network constraints and trading strategy adopted for local energy trading. In this paper, we compare different trading strategies, i.e., the rule-based zero intelligent (ZI) strategy and the preference-based game theory (GT) approaches, for a constrained P2P platform. Quadratic trading loss and impedance-based network utilization fee models are suggested to define the network constraints for the P2P system. Additionally, a reluctance-based prosumer-sensitive model is developed to adjust the trading behavior of the participants under the heavy distribution losses/network fee. The presented results show that the suggested trading strategies enhanced the average welfare of the participants by approximately 17%. On average, the customers saved about $33.77 monthly, whereas the average monthly earnings of the prosumers were around $28.3. The ZI strategy enhanced the average monetary advantages of all the market participants by an average of 7% for a system having small distribution losses and a network fee as compared to the GT approach. Contrarily, for a system having high losses/a utilization fee, the GT approach improved the average welfare of the prosumers by around 75% compared to the ZI strategy. However, both trading strategies yielded competitive results compared to the traditional market under the standard values of network coefficients.

Throughput maximization with channel access fairness model using game theory approach

The <span>adoption of cognitive radio (CR) technology into wireless sensor networks (WSNs) effectively addresses the spectrum scarcity problem of traditional unlicensed spectrum. Allocating and managing limited network channel to secondary user (SUs) considering dynamic behavior pattern of primary users (PUs) is a critical issue of CR-WSN. Recently, various channel access methodologies using statistical, reinforcement learning (RL), game theory (GT), and deep learning (DL) model have been presented for CR-WSN. However, the existing channel access methodologies has following two limitations: i) fails to assure balance between maximizing throughput of SUs with minimal interference to PUs considering multi-channel CR-WSNs environment; and ii) maximizing throughput with minimal collision assuring access fairness among SUs considering energy constraint CR-WSNs. In addressing the research issues, this paper present throughput maximization channel access fairness using game theory (TMCAF-GT) model. The TMCAF employ both shared and non-shared channel access mechanism employing GT model for assuring throughput maximization with minimal interference and access fairness. Experiment outcome shows the TMCAF-GT provides superior throughput with minimal collision.</span>

An Optimization Approach for Distribution Network Planning Strategy Based on Combined Improved Cloud Model and Evidence Theory

With the continuous promotion of China’s power reform, higher requirements and standards have been put forward for distribution network investment decisions. However, the current work of distribution network project optimization in China has the problems of high subjectivity of investment decisions, fuzzy evaluation method, and high uncertainty of project optimization. In response to the above problems, this paper proposes a distribution network planning scheme optimization method based on the improved cloud model and evidence theory. Firstly, the optimal index system of the planning scheme is constructed from the dimensions of load transfer capacity, grid structure level, load capacity, investment benefit, social benefit, and life-cycle cost. Secondly, for subjectivity and ambiguity problems, the traditional degree of the membership function is replaced by a cloud model optimized by cloud entropy. Thirdly, the problem of uncertainty is addressed through evidence theory, while the game theory approach is used to identify conflicting evidence for evidence revision and fusion in the case of conflicting evidence arising from traditional evidence theory. Moreover, by improving the TOPSIS method and comparing the distances between the basic probability assignments of each planning scheme and the positive and negative virtual optimal solutions, the final decision results can be determined. Finally, the effectiveness and superiority of the proposed method are verified by experiments.

Location-pricing decisions with carbon emission considerations: A behavioral game-theoretic approach

The location-pricing problem aims to solve pricing and facility location problems at the same time. While the demand of customers is known in the classical facility location problem, it is a function of price in the location-pricing problem. Moreover, some other factors, such as production, transportation and inventory costs, and competition, can affect the final price of products, making the location-pricing problem more complicated. Considering the necessity of incorporating environmental aspects into supply chain design and the importance of considering the behavioral characteristics of decision-makers, this study addresses the location-pricing problem based on these two significant perspectives. In this paper, there is a lead company responsible for locating and planning a set of manufacturers in the first echelon. On the other hand, the focal manager controls the total emissions and sets the wholesale price. In the second echelon, there are two retailers that base their pricing strategies on social preferences in order to gain a larger market share. The competition between the retailers determines the demands of the customers. The modeling approach is based on the Stackelberg-behavioral Nash game and is formulated as a bi-level mixed-integer nonlinear programming model. The problem is solved using a combination of an analytical solution method and the utilization of a commercial optimization solver. The proposed model is assessed using a real case study in the energy sector. The results indicate that deviating from purely selfish behavior in the pursuit of profit can lead to beneficial outcomes. Generally speaking, price competition between status-seeking retailers results in lower prices for the leader and followers compared to the standard game theory approach. This leads to an increase in average demand in the market, which results in increased profit for members and consequently improves the performance of the whole system. Also, it is worth mentioning that the status-seeking behavior of retailers results in a decrease in retail prices, which improves social welfare.

Design of a Green Supply Chain Based on the Kano Model Considering Pricing

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.

A game theory-based analysis of merchants’ mobile payment adoption using hybrid SEM-neural network approach

PurposeDriven by the evidence from the literature on the significance of mobile (m-)payment in economic growth and productivity and at the same time the relative dismal adoption of this service, the purpose of present paper is to elucidate the merchants’ m-payment adoption from the perspective of trust, drawing upon the game theory framework, in the Malaysian context.Design/methodology/approachAn online survey consisting of 302 respondents was carried out to investigate the impact of trust and opportunism on merchants’ perceived trustworthiness using a two-staged structural equation modeling–neural network approach to determine the significance and relative importance of variables. This study also applies a game-theoretic approach to analyze the impact of trust on the relationship between merchants and m-payment service providers.FindingsThe results indicate a positive and statistically significant relationship between merchant trust, merchant opportunism and perceived trustworthiness, and a statistically significant negative relationship was found between m-payment provider opportunism and perceived trustworthiness. The findings from the prisoner’s dilemma two-player model indicate that the scenarios of mutual trust and mutual opportunism as paradigmatic of cooperation and defection produce the best and worse outcomes, respectively. An intriguing result was the positive impact of merchant opportunism on perceived trustworthiness, which indicates a very calculative orientation of merchants in m-payment contracting.Originality/valueTo the best of the authors’ knowledge, this is among the first attempts to propose a game theory approach to the interaction between merchants and m-payment providers under the framework of trust and opportunism. A game theory study in the context of m-payment adoption can contribute to the theoretical literature by providing insights into the decision-making processes of merchants. By incorporating trust and opportunism into the game theory model, we can gain a better understanding of how they affect the decision-making process and overall adoption rates. The conclusions and implications provide useful insights for managers of both m-payment platforms and merchants in this relational exchange. The results of the present research can provide insights into the factors that influence merchant decisions and guide them toward suitable partnerships for successful adoption and can guide authorities for policy interventions and supporting adoption efforts.

Duopoly innovation with technology diffusion

ABSTRACT This article captures the effects of technology diffusion on innovation under duopoly competition. With the game theory approach, some interesting results are achieved. First, technology diffusion stimulates innovation and firms launch innovation to share more market size. Second, firms under unilateral innovation launch less innovation than that under bilateral innovation. Finally, neither firms’ innovation constitutes a Nash equilibrium and subsidy may transfer this equilibrium to a new equilibrium that both firms innovate.