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In order to solve the problem of uncertainty processing and the fuzziness of index weight in the risk analysis of gas explosion, a risk assessment model of gas explosion using the Bayesian network and game theory is proposed. Firstly, the topological structure model of gas explosion is constructed based on expertise and accident-causing theories. At the same time, the subjective and objective weights of experts are determined by game theory optimization. Then, the prior probability and conditional probability of risk factors are calculated based on the fuzzy set theory. Secondly, the Bayesian inference technology is used to calculate the probability of gas explosion and the posterior probability distribution of risk factors. Finally, the sensitivity analysis and key cause chain analysis are used to find out the key risk factors and key risk paths affecting gas explosion. The case study shows that the probability of gas explosion in the working face of a coal mine in Shandong Province is 6.5%. The main ignition sources of gas explosion are electric failures, friction between rocks, blasting sparks and coal spontaneous combustion, while the main causes of gas accumulation are ventilation system failures and ventilation system design. Through sensitivity analysis, it can be seen that two factors of ventilation system failures and electric failures are the most sensitive. According to the analysis of the key causal chain, it shows that the combination of the path of electric failures→ventilation system failures→ventilation measures→gas control measures→gas transfinite and the path of electric failures→electric spark→ignition source leads to the highest possibility of gas explosion. Compared with normal conditions, the probability of gas explosion increased by 301.5%. At the same time, the combination weighting method based on game theory has comprehensive advantages in improving the objectivity and accuracy of index weights, and the evaluation results accords with the actual situation of coal mines.

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion, a series of discussions on network security issues are carried out based on game theory. From the perspective of the life cycle of network vulnerabilities, mining and repairing vulnerabilities are analyzed by applying evolutionary game theory. The evolution process of knowledge sharing among white hats under various conditions is simulated, and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed. On this basis, the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm (WCA) to produce better replacement individuals with greater probability from the perspective of both attack and defense. Through the simulation experiment, it is found that the convergence speed of the probability (X) of white Hat 1 choosing the knowledge sharing policy is related to the probability (x0) of white Hat 2 choosing the knowledge sharing policy initially, and the probability (y0) of white hat 2 choosing the knowledge sharing policy initially. When y0 ​= ​0.9, X converges rapidly in a relatively short time. When y0 is constant and x0 is small, the probability curve of the “cooperative development” strategy converges to 0. It is concluded that the higher the trust among the white hat members in the temporary team, the stronger their willingness to share knowledge, which is conducive to the mining of loopholes in the system. The greater the probability of a hacker attacking the vulnerability before it is fully disclosed, the lower the willingness of manufacturers to choose the ''cooperative development'' of vulnerability patches. Applying the improved wolf colony-co-evolution algorithm can obtain the equilibrium solution of the ''attack and defense game model'', and allocate the security protection resources according to the importance of nodes. This study can provide an effective solution to protect the network security for digital twins in the industry.

Abstract The sudden inrush of water poses a serious threat to the safety of workers during tunnel construction in the karst region of southwest China. To mitigate this risk, a model is proposed to assess the risk of water surge through a tunnel by combining improved game theory with uncertainty measure theory. Eight indicators of risk were extracted based on the solubility of rock, its geological structure, capacity for surface catchment, and hydrogeological factors, and were incorporated into the proposed model. The subjective weights of these indicators were obtained using the analytic hierarchy process, while their objective weights were calculated through the entropy weighting method and the criteria importance through intercriteria correlation method. An improved game theory-based method of combinatorial weighting was then used to construct the corresponding weight vectors. Single-indicator measurement functions and multi-indicator measurement matrices were utilized to classify and evaluate the indicators of the risk of a surge in water level based on a confidence criterion. The proposed method was applied to five typical karst sections of the Yanjin Tunnel of the Chongqing–Kunming High-speed Railway Project, and the method was validated by comparing the recorded and estimated inflow volume ranges during the project’s construction, showing consistency with the actual evaluation results. This proposed model thus offers a practical tool for assessing the risk of water inrush in karst tunnels.

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing.

Game theory has been studied extensively in recent centuries as a set of formal mathematical strategies for optimal decision making. This discipline improved its efficiency with the arrival, in the 20th century, of digital computer science. However, the computational limitations related to exponential time type problems in digital processors, triggered the search for more efficient alternatives. One of these choices is quantum computing. Certainly, quantum processors seem to be able to solve some of these complex problems, at least in theory. For this reason, in recent times, many research works have emerged related to the field of quantum game theory. In this paper we review the main studies about the subject, including operational requirements and implementation details. In addition, we describe various quantum games, their design strategy, and the used supporting tools. We also present the still open debate linked to the interpretation of the transformations of classical algorithms in fundamental game theory to their quantum version, with special attention to the Nash equilibrium.

Weapon target assignment is a critical challenge in military contexts. Traditionally, commanding officers manually decide weapon assignments, but the problem's complexity has grown over time. To address this, automated systems have been introduced. These systems fall into two categories, which are static (time-independent) and dynamic (considering changes over time). Static systems solve the problem for a single time step without considering temporal changes. Dynamic systems incorporate time as a variable, adapting to evolving scenarios. Two main approaches exist, which are asset-based and target-based. Asset-based approach maximizes the survival probability of assets, which is our focus in this study. We propose a solution using game theory that spans the entire area and all time frames. We employ game theory, treating continuous functions of time as utility functions for vessels. Continuous probability-to-kill values for weapons are defined across the area. Threat trajectories yield continuous kill probabilities for the weapons, translating to vessel utility. To avoid inefficiencies, we align individual vessel utility with global utility. The Nash Equilibrium provides the optimal weapon assignment strategy. Our study uses a naval environment for analysis. In summary, our research leverages game theory to dynamically assign weapons to naval vessels, aiming to maximize asset survival. [ABSTRACT FROM AUTHOR]

The permanent magnet eddy current brake (ECB) discussed in this paper are expected to brake a strong impact load for a large-scale machinery, and its design requirement is to brake smoothly within a given displacement limit. A design method is proposed to be able to design and calculate the based on the design requirements. According to the requirements of the brake displacement, an expected brake force can be calculated. Then, through the analysis of the permanent magnet operating point and the analysis and calculation of the design parameters, a reasonable design scheme that matches the prime design requirements can be obtained. When facing strong shock loads, in addition to satisfying braking requirements, different shock conditions can lead to different practical braking process. Making the corresponding braking process smoother can be regarded as an additional design objective, thus the design scheme obtained can be optimized in a certain range accordingly. A multi-objective optimization design of the ECB is carried out in combination with the Stackelberg game theory. After verifying the optimization results, it can be obtained that the given design and optimization methods are applicable and can satisfy the design and optimization objectives. [ABSTRACT FROM AUTHOR]

The assessment of seawater quality is a complex process as it involves the influence of several factors and is characterized by dynamic changes in these factors. Herein, a novel model coupled with the semi-partial subtraction set pair potential method (SSSPP) and game theory was discussed for the dynamic assessment of seawater quality. In the proposed model, the identity-discrepancy-contrary degrees were calculated to construct the evaluation matrix, and then the classification and the dynamic risk trends of the seawater quality were determined. A case study and comparison with the single-factor approach and Nemerow index evaluation method were carried out to confirm the accuracy and dependability of the suggested model. The results indicate that the proposed method can be used to measure trends in seawater quality evolution and also to diagnose key indicators affecting seawater quality. It provides a new and effective way to accurately identify the quality of seawater and its trends., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/ distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of game theory and pop culture as a potent strategy for amplifying social media engagement. A methodological analysis demonstrates how game theory principles can be effectively applied to social media campaigns by leveraging the universal appeal of pop culture. The study introduces a simple yet illustrative model that outlines the strategic use of pop culture elements to attract new followers and maximise net profit, considering variables such as potential follower base, cost of pop culture integration, and the preferences of different audience segments towards pop culture-infused products. Findings underscore the untapped potential of conscious game theory application in social media strategy design, offering a fresh perspective on achieving optimal engagement through a balance of appeal to pop culture enthusiasts and those favouring traditional content. This contribution is novel in its approach. It provides a comprehensive review of game theory's utility in pop culture and presents a detailed framework for its application in social media campaigns. By bridging theoretical concepts with practical application, this research opens new avenues for strategic engagement in the digital age, making a significant addition to both academic research and practical marketing strategies. [ABSTRACT FROM AUTHOR]

The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network. The employed computing power carries out the pool mining attacks on the attacked mining pool. To realize the win-win game between the malicious mining pool and the employee, the paper proposes an Employment Attack Pricing Algorithm (EAPA) of mining pools in blockchain based on game theory. In the EAPA, the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack, the revenue increment of malicious mining pools, and the revenue of the employee. It establishes a game model between the malicious mining pool and the employee under the employment attack. Then, the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation. In the strategy, the malicious mining pool analyzes the conditions for the employment attack, and uses the derivative method to find the optimal utilization value of computing power, employees analyze the conditions for accepting employment, and use the derivative method to find the optimal reward value of computing power. Finally, the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation. The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee. The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time. The EAPA outperforms the state-of-the-art methods such as SPSUCP, DPSACP, and FPSUCP.

With the ever-increasing popularity of Internet of Things (IoT), massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces (APIs) that can be accessible remotely. In this context, finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile applications. However, the number and diversity of candidate IoT Web APIs places an additional burden on application developers’ Web API selection decisions, as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs selected. Considering this challenge and latest successful applications of game theory in IoT, a Diversified and Compatible Web APIs Recommendation approach, namely DivCAR, is put forward in this paper. First of all, to achieve API diversity, DivCAR employs random walk sampling technique on a pre-built “API-API” correlation graph to generate diverse “API-API” correlation subgraphs. Afterwards, with the diverse “API-API” correlation subgraphs, the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem. A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search problem. At last, a set of experiments are designed and implemented on a real dataset crawled from www.programmableweb.com. Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

Abstract Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks. This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk. The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion, lateral motion, yaw motion, sprung mass roll motion, unsprung mass roll motion, and an anti-rollover artificial potential field (APF) is proposed based on this. The motion planning method, which is based on model predictive control (MPC), combines trajectory tracking, anti-rollover APF, and the improved obstacle avoidance APF and considers the truck dynamics constraints, obstacle avoidance, and anti-rollover. Furthermore, by using game theory, the coefficients of the two APF functions are optimised, and an optimal path is planned. The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios. The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.

Supply chain process is interdependent. Starting from procurement of raw materials, production, distribution, and finally the goods reaching consumers will influence each other. The costs of goods and services will be somewhat impacted by these social habits. Producers and merchants must comprehend these social behaviors in order to properly establish prices and the distribution of goods. This includes determining the prices of goods and services. Offering manufacturers who participate in cooperative advertising schemes money for a percentage of the costs connected with local advertising encourages retailers to launch additional promotional activities. The aim of this research is to investigate how cooperative pricing and advertising can improve supply chain coordination using consumer demand functions. A model based on game theory that takes the dynamics of power in the supply. A series of numerical simulations is presented to illustrate the optimal solution of channel members based on scenarios that illustrate, understand and compare the fundamental results of the game models. The results of this research are that retail price decisions are influenced by the level of competition and product differentiation. The results show that retail margins depend on local ( ) and national ( ) advertising effectiveness values. In addition, retailers can gain greater profits by setting higher prices in conditions of low price elasticity but must consider consumer sensitivity to price to maximize profits.

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN's efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention.

Purpose – In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method. Design/methodology/approach – First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system. Findings – The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results. Originality/value – This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

The authors' aim was to present a practical decision-making framework for decision makers (DMs) to use in evaluating interdisciplinary strategic approaches to degree program development. A framework based on ordinal game theory was applied using a recent decision to create an interdisciplinary business analytics program at a small, public university. The optimal Nash, in which each decision maker receives the most desirable outcome, was determined. The proposed methodological framework can be used for the design, evaluation, and postcommitment support of any interdisciplinary degree program.

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN’s efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention. [ABSTRACT FROM AUTHOR]

In the era of open innovation, it is of great significance to analyse strategic trade-offs for enterprises' technology acquisition between external and internal. This paper considers the relative pay-offs amongst two manufacturers' selection for their respective scarce technology of the Internet of Things, using game theory models and numerical analysis. In addition to self-innovation relying on internal resources, enterprises can obtain technology from external sources, including technology imported from a third party or cross-licensing with its competitor. Unlike empirical analysis, this study focuses on the strategic firms' interactions at the micro-level study and operational considerations, such as the factors influencing the mode of enterprise technology acquisition, firm competition and cooperation behaviour mechanism, and the selection of innovation initiators. The findings provide implications for competitive enterprises in high-tech industries to acquire complementary technologies by identifying what conditions lead to win-win outcomes. In particular, firms can adjust their licensing levels under certain conditions to adapt to the disparities in each other's development capabilities, thus achieving cross-licensing agreements. [ABSTRACT FROM AUTHOR]

With the acceleration of energy storage construction, investors are paying attention to how the costs of storage projects can be recovered. Market rules and incentive policies affect the dissemination of energy storage costs. This article simulates the interactive behavior of multiple agents based on evolutionary game theory, taking into account market factors such as prices, capacity, and incentive policies. It views the dissemination of storage costs as an interactive decision-making problem involving multiple agents' adoption of technology, and analyzes how market factors influence the outcomes of cost dissemination. [ABSTRACT FROM AUTHOR]

This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network's status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.