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Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP. [ABSTRACT FROM AUTHOR]

In the article, we studied the continuous problem for multi-agent pathfinding. We show continuity of the path-traversing time functional and the existence of the optimal path for a single agent. Also, we consider game theory interpretation for multi-agent pathfinding with continuous routes as a game with strategies in a Banach space. Finally, we briefly discuss near-optimal routes and connection of heuristics for pathfinding and integral geometry problems. [ABSTRACT FROM AUTHOR]

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley's utility and interpretation with real-world data. The method is available as an open source Python package named geoshapley. [ABSTRACT FROM AUTHOR]

Demand for perishable food is sensitive to product prices and is affected by the prices of similar or alternative products. While brand loyalty influences the demand for products, determining a reasonable price requires a precise pricing strategy. In this paper, a pricing model for perishable food is presented in which the brand value of the product and the price of other manufacturers as competitors are considered. To this end, this study first predicts the price of competitors using a combination of optimized Neural Networks and presents an optimized model using a Genetic Algorithm. This algorithm combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a Genetic Algorithm (GA). The proposed model is then used to merge with a game-theory model for the pricing of perishable foods. In this game-theory model, pricing approaches are developed based on identified prices of competitors. In the coordination contract game-theory model, Multi Retailer- one Supplier and Price-sensitive demand of Perishable product are developed with and without quantity discount contract. Obtained results indicate that independent procurement provides retailers with higher profit, while lower profit will be presented when coordination is not considered. Also, with coordination, the ordering cycle increases, and the ordering frequency decrease. [ABSTRACT FROM AUTHOR]

Privacy and efficient data processing are crucial research areas in the field of outsourcing computing. Thus, the contribution of the paper is twofold. First of all, we develop a secure offloading technique that can process data while preserving privacy. Although RSA and Paillier cryptosystems are highly beneficial for mathematical operations on basic encrypted data due to their homomorphic properties, images are processed using permutation-ordered binary (POB) and Shamir's secret sharing (SSS). Second, a non-cooperative game theory model was formulated to examine the interactions between service providers, characterizing each edge as an M/M/1 system with an infinite queue. Through formal verification using rigorous mathematical reasoning, the suggested strategies for task offloading and resource allocation are shown to be effective in terms of security, quality of service (QoS), and energy consumption. The best-response dynamics approach is used to calculate the final strategy for each participant and assess how fairly resources are allocated. Finally, the potential of our approach is demonstrated numerically, revealing a faster convergence towards attaining Nash equilibrium in a duopoly cloud-edge data center market, granted that the necessary and sufficient conditions are met.

The rapid spread of infectious diseases poses a critical threat to global public health. Traditional frameworks, such as the Susceptible–Exposed–Infectious–Recovered (SEIR) model, have been crucial in elucidating disease dynamics. Nonetheless, these models frequently overlook the strategic interactions between public health authorities and individuals. This research extends the classic SEIR model by incorporating differential game theory to analyze optimal control strategies. By modeling the conflicting objectives of public health authorities aiming to minimize infection rates and intervention costs, and individuals seeking to reduce their infection risk and inconvenience, we derive a Nash equilibrium that provides a balanced approach to disease management. Using Picard’s iterative method, we solve the extended model to determine dynamic, optimal control strategies, revealing oscillatory behavior in public health interventions and individual preventive measures. This comprehensive approach offers valuable insights into the dynamic interactions essential for effective infectious disease control.

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization.

Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (game-theory-based drought index, GTDI) by combining two single-type indices: an agricultural drought index (standardized soil moisture index, SSMI), which implies drought hazard-bearing conditions, and a meteorological drought index (standardized precipitation evapotranspiration index, SPEI), which implies drought hazard-causing conditions. In addition, the entropy-theory-based drought index (ETDI) is introduced to incorporate a spatial comparison with GTDI to illustrate the rationality of gaming weight integration, as both entropy theory and game theory belong to linear combination methods in the development of the integrated drought index and entropy theory has been applied in related research. Leaf area index (LAI) data are employed to confirm the reliability of GTDI in identifying drought by comparing it with SPEI, SSMI, and ETDI. Furthermore, a comparative analysis is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss GTDI's level of advancement in monitoring changes in hazard-causing and hazard-bearing impacts. The results show that GTDI has a very high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than that of ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and hazard-bearing impacts. The case studies show that drought events in the Wei River basin are dominated by a lack of precipitation. The hazard-causing index, SPEI, dominates the early stages of a drought event, whereas the hazard-bearing index, SSMI, dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought prevention and monitoring.

This article addresses a cooperative game-oriented optimal constraint-following problem for fuzzy mechanical systems. The state of the concerned system is affected by possibly (fast) time-varying uncertainty. The fuzzy set theory is adopted to describe such uncertainty. The task is to drive the system to obey a set of prescribed constraints optimally. Since the control objective may be changing along with the system uncertainty, a time-varying β-measure is defined to gauge the constraint-following error; based on which, an adaptive robust control scheme with two tunable parameters is then proposed to render it to be uniform boundedness and uniform ultimate boundedness. For the seeking of the optimal design parameters, two cost functions, each of which is dominated by one tunable parameter, are developed with the fuzzy information, and thereout a two-player cooperative game is formulated. Finally, the optimal design problem is successfully solved: with the existence, uniqueness, and analytical expression of the Pareto optimality.

This study uses Stackelberg game theory, considering different combinations of carbon emission reduction policies and that high-carbon-emission enterprises may face various carbon emission reduction regulations, to explore the production inventory problems in a multinational supply chain system. The purpose is to determine the manufacturer's optimal production, shipping, carbon reduction investment, and the retailer's replenishment under the equilibrium for different carbon emission policy combinations. To develop the production inventory models, this study first develops the total profit and carbon emission functions of the supply chain members, respectively, and then obtains the optimal solutions and total profits of the manufacturer and the retailer under different carbon emission policy combinations through the mathematical analysis method. Further, this study used several numerical examples to solve and compare the proposed models. The results of numerical analysis show that regardless of the increase in carbon price or carbon tax, the manufacturer and retailer will adjust their decisions to reduce carbon emissions. Specifically, an increase in the carbon price contributes to an increase in the total profit of manufacturers, while an increase in the carbon tax reduces the total profit of manufacturers. This study also explores a sensitivity analysis on the main parameters and has yielded meaningful management insights. For instance, in cases where low-carbonization strategies are required, the manufacturer or retailer can effectively reduce the carbon emissions resulting from production or purchasing activities, thereby significantly reducing overall carbon emissions. It is believed that the results of this study can provide enterprises/supply chains with reference to their respective production, transportation, carbon reduction investment, and inventory decisions under carbon emission policies, as well as information on partner selection and how to adjust decisions under environmental changes. [ABSTRACT FROM AUTHOR]

In the fast-evolving real world, multi-objective decision-oriented (MODO) problems are commonplace. where game theory plays a vital role. One example of a MODO problem that requires strategic decision-making to figure out a balance between conflicting objectives in a competitive market context is the telecom market share (TMS) problem. The proposed study focuses on the TMS problem, where two stakeholders, Player I representing the telecom company and Player Il as the investor, aim to maximize profits and minimize risks. To address uncertainties in proposed problems, the parameters of the multi-objective telecom market share (MOTMS) problem are transformed into single-valued neutrosophic trapezoidal fuzzy numbers (SVNTFNs). In the proposed study. two different methodologies have been proposed and applied to solve the MOTN/IS problem with parameters of SVNTFNs. The first involves constructing linear programming problems (LPPs) and applying the Nash bargaining approach to balance conflicting interests in the telecom share market. The second uses the single-valued neutrosophic fuzzy goal programming (SVNFGP) approach. In the case of the TMS. the proposed approaches give decision-makers a clear method for determining a compromise between maximizing profits for telecom companies and minimizing risks for investors. [ABSTRACT FROM AUTHOR]

Mobility as a service (MaaS) is an emerging mobility service that has received high expectations from the transportation sector in recent years. MaaS integrates multiple transportation services into a single platform, enhancing the accessibility of various transportation resources. However, the complex integration involved in MaaS leads to uncertainty in stakeholder interaction and system implementation. For the successful deployment and development of MaaS, this study aims to identify stakeholder enablers and barriers as well as to solve the uncertainty in MaaS implementation. We construct a tripartite evolutionary game model among government, transportation service providers (TSPs) and travelers. By synthesizing the evolutionary stability of each participant through simulation, we explored the impact of factors on stakeholder strategy choices and further analyzed the stability of equilibrium points in the system. The results show that: 1) Government subsidies and penalties, TSPs' costs and revenues, travelers' utility and expenses all impact the strategy choices of stakeholders. 2) The costs of government policy-making and TSPs will influence the evolution of MaaS system into two different development modes: free-market model and policy-supported model. 3) Travelers' utilities play a crucial role in promoting the stability of travelers' adoption of MaaS. This study provides new insights into identifying stakeholder demands. The findings contribute to formulating specific policy recommendations and incentive measurements to help in the strengthen and forward movement of MaaS development in the future. • Employed tripartite evolutionary game theory to explore MaaS stakeholder interactions. • Identified the key factors influencing stakeholders' decisions. • Integrated analysis of model outcomes with business models for comprehensive evaluation. • Simulation analysis was conducted to verify the validity of the results. [ABSTRACT FROM AUTHOR]

In this paper, we use an evolutionary game theory approach to build a relationship model of students and clubs for the purpose of improving student enthusiasm for participating in club activities. First, the process of the model building is introduced, which mainly includes the basic assumptions and the equilibrium point stability analysis. Based on this analysis, we find that the motivation adjustment of students and clubs is a dynamic process and that unilateral efforts alone cannot achieve an ideal result. Then, we use real data from Yanshan University to evaluate the model, the results of which indicate that the model can analyze the relationship between students and clubs effectively. Finally, we provide relevant suggestions based on the model established in this study, whereby we contribute a theoretical basis and practical guidance for how students can actively participate in clubs, as well as how clubs can better develop themselves. [ABSTRACT FROM AUTHOR]

Objective: Our study aims to enhance the precision of internal control construction within public hospital supply chains and minimize the subjective bias influence. We have integrated the game theory combination weighting method into the design of internal control paths and based on this, developed a series of pioneering solutions. This innovative approach is anticipated to heighten the effectiveness and scientific rigor of the internal control design scheme within the supply chain., Method: Firstly, we utilized literature review and expert interviews to delve into the key factors of public hospital supply chain internal control, forming an index system for public hospital supply chain internal control that aligns with current informatization requirements. Subsequently, we incorporated the Game Theory Combination Weighting Method into this study. By means of the Analytic Hierarchy Process and the Entropy Weighting Method we determined the subjective and objective weights of each index and obtained their comprehensive weights through the Game Theory Combination Weighting Method. Then, based on the analysis results, we designed a series of internal control construction schemes and implemented these schemes at Weifang Maternal and Child Health Hospital between 2019 and 2023. Finally, using the Fuzzy Comprehensive Evaluation Method to assess and compare the actual effects before and after the implementation of the schemes, thereby validating the effectiveness of the Game Theory Combination Weighting Method in the design of the internal control path of public hospital supply chains., Results: The fuzzy comprehensive evaluation results for the years 2019 and 2023 demonstrated that after implementing our design schemes using the Game Theory Combination Weighting Method, the hospital's satisfaction in aspects such as plan-side control, purchase-side control, asset-side control, expenditure business control, and contract management control has significantly improved., Conclusion: Our research indicates that the Game Theory Combination Weighting Method is applicable to the path design of internal control links in public hospital supply chains. This method has effectively enhanced the targeted improvement of weak links within the construction of internal controls in the supply chain of public hospitals and is of great significance for improving the scientific nature of supply chain internal control management., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Yu.)

The snow-shaped distribution network is developed and upgraded from the traditional single-loop network and double-loop network structure. It is composed of three or four groups of single-loop networks in pairs that form a feeder cluster in the form of a ring network. Based on this topology, the coordinated optimization operation problem is studied. First, the game elements are analyzed, and then a cooperative game-based, coordinated optimization operation model is established by taking the minimum network loss and voltage deviation of the system as the utility function. Then, the alternating direction multiplier method (ADMM) is employed in Cplex solver to solve the established optimization model. Finally, a numerical case is given to verify the effectiveness of the proposed model and method. The results show that, by using the complementary difference relationship of source–charge characteristics between different regions in the snow-shaped distribution networks, the cooperative game model can realize the energy interconnection among different regions to improve the operation level. [ABSTRACT FROM AUTHOR]

Abstract Conflict game theory is employed to analyze the countermeasure relationship between radar systems and jammers. To address this issue, a novel utility function and a Conflict Power Allocation Game (CPAG) algorithm are introduced for determining the Nash Equilibrium (NE) solution of the game. The utility function enables the calculation of optimal power response functions for both the radar system and the jammer, with the NE solution of the CPAG algorithm obtained through numerical computation. Simulation results demonstrate the convergence of the proposed CPAG algorithm to the NE solution, validating its efficacy. Finally, an analysis of the relationship between the utility function and pricing factors is conducted based on the proposed CPAG algorithm and simulation outcomes.

Introduction: The rise of autonomous vehicles has brought about a transformative shift in transportation, witnessing the coexistence of human-driven and autonomous vehicles on highways in the United States, Europe, and China. This coexistence poses challenges to traffic operations, particularly in intricate scenarios like highway ramps. The interaction between autonomous truck platoons, displaying heightened maneuverability, and human-driven vehicles has emerged as a critical concern. Consequently, this research aims to propose and investigate three avoidance modes (overall, gap and cross) employed by truck platoons, evaluating their comprehensive impact on human-driven vehicles.Methods: Multiple scenarios are simulated utilizing the Simulation of Urban Mobility (SUMO) software, collecting data on three distinctive avoidance modes concerning Travel Time (TT) and Time to Collision (TTC). Employing principles of game theory, a comprehensive assessment is undertaken to evaluate the traffic efficiency and safety of each mode. Comparative analyses against a no-avoidance baseline are conducted, offering a holistic evaluation of each mode’s applicability across diverse scenarios.Results: The findings highlight the commendable performance of gap mode and overall mode in enhancing traffic efficiency, while cross mode excels in fortifying traffic safety. Overall, the gap mode emerges as the optimal choice among the three.Discussion: This study introduces a game-theoretic approach to managing human-machine mixed traffic flow, establishing a foundational framework for theoretical research in decision-making for emerging mixed traffic environments. It considers safety and efficiency perspectives across different types of traffic entities. The insights gained contribute to the evolving discourse on the integration of autonomous vehicles into existing traffic systems, addressing the intricate challenges posed by the coexistence of various vehicle types on highways.

The rapid progression of Cloud Computing (CC) technology has ushered in innovative ecosystem concepts such as Mobile Cloud Computing (MCC). In this context, the incorporation of Unmanned Aerial Vehicles (UAVs) into these cloud ecosystems has unlocked new avenues for use cases such as delivery services, disaster response, and surveillance. However, this integration presents challenges in resource management and service selection due to the unique constraints of drones and variations in service quality. This paper proposes a Game Theory-based UAV-cloud of Service Selection Architecture (GT-SSA) to address resource management and service selection challenges. By leveraging game theory in our proposal, GT-SSA optimizes decision-making for Client Drones and Provider Drones, enhancing service selection efficiency. GT-SSA proved its resilience to scalability concerns, as evidenced in Discovery Delay, Consumption Delay, End-to-End Delay, and Energy consumption. Moreover, when GT-SSA is compared with the Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS), GT-SSA outperforms GTCS in terms of Successful Execution Rate, Average Execution Time, and Energy consumption. Our research also reveals that game theory surpasses fuzzy logic in terms of service selection efficiency.

The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques.

Breast cancer is a critical health issue affecting the well-being of women. Breast cancer is one of the most common causes of the increase in the mortality rate of women around the world. Early detection of breast cancer can, to some extent, decrease the number of deaths caused and also improve treatment outcomes and patient survival rates. Traditional Machine learning (ML) and Deep learning (DL) approaches have proven to be more successful in predicting breast cancer. In terms of privacy and early detection, Federated Learning (FL), a decentralized ML approach, offers a promising solution for training predictive models on distributed healthcare data while ensuring privacy and security. This paper proposes a novel framework that combines the benefits of integrating Shapley values and game theory concepts with FL for breast cancer prediction. The framework uses Shapley values for feature selection from 30 features of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine machine learning repository (UCI ML). The framework also addresses the issue of poor-performing clients by introducing a payoff mechanism based on individual client accuracy. Clients with higher accuracy are given greater influence in the model aggregation process, encouraging client competition and improving the overall model performance. Our framework proves to be promising by achieving a prediction accuracy of 94.73% in the FL environment. The proposed approach provides a privacy-preserving solution for breast cancer prediction in an FL environment, by combining Shapley values and game theory. The results of this study can help in the development of more accurate and robust breast cancer prediction models, contributing to improved patient outcomes and healthcare decision-making.

Wireless Sensor Networks (WSNs) play an instrumental role in monitoring and data collection across a plethora of domains. As these networks expand and get more intricate, the need for energy-efficient and smart sensing models becomes paramount. This paper introduces the energy-efficient collaborative sensing model using game theory for WSNs. This model is unique in its approach, amalgamating game theory and reinforced learning to ensure optimal energy consumption without compromising the quality of service. A cornerstone of this model is the introduction of the Selection Propensity Index (SPI), which helps in the decision-making process of choosing the right sensors for specific tasks. Moreover, the model employs a Distributed Anticipatory Time-slot selection Algorithm (DATA), an RL-based algorithm that facilitates collaborative communication. This aspect ensures that the sensors while communicating, select the time slots that would result in minimal energy expenditure and optimal data transmission. Simulations are performed over the proposed model and the results obtained showcase that the proposed model outperforms the existing similar methods in terms of energy efficiency, packet drop ratio, and throughout. The proposed model exhibits an enhancement in terms of network lifetime by 202%, throughput by 30%, and is faster by more than 60% compared to the existing methods running on full load, thereby providing an innovative approach to energy efficiency in sensor networks.

In the ever-changing world of decision-making, when game theory and reinforcement learning(RL) come together, they create a fascinating combination that shows a new way to solve complex problems in many fields. The combination of game theory and RL is a powerful convergence that opens up a hopeful new frontier for dealing with complex decision-making problems in many different fields. Research on the convergence of game theory and RL has shown to be beneficial, providing essential insights into challenging decision-making issues in various disciplines. This study investigates the recent developments of game theory and RL approaches through a systematic review and highlights the significance of game theory in boosting reinforcement algorithms and increasing the interaction of autonomous vehicles, safeguarding edge caching, and more. It offers a thorough account of the developments at the confluence of game theory and RL. The reviewed papers mainly focus on broad themes and address three important research questions: the impact of game theory on multi-agent reinforcement learning (MARL), the significant contributions of game theory to RL, and the significant impact areas. Following the methodology, search outcomes, and study areas is a discussion on game theory-related terminology, followed by study findings. The review’s conclusions offer ideas for further study and open research questions. The importance of game theory in advancing MARL, the potential of game theory in promoting RL strategies, and the opportunities for combining game theory and RL in cutting-edge fields like mobile edge caching and cyber-physical systems(CPS) are all emphasized in the conclusion. This review article advances our knowledge of the theoretical underpinnings and real-world applications of game theory and RL, laying the groundwork for future improvements in decision-making techniques and algorithms.

Purpose – The purpose of this paper is to review the utilization of game theory in the entrepreneurship literature. Game theory can potentially be employed to assess strategies incentivizing productive entrepreneurial activities and subsequent economic development. Therefore, the author reviews entrepreneurship articles and explores the application of game-theoretic models and concepts in the literature. Design/methodology/approach – First, the author provides an overview of the entrepreneurship ecosystem concept, highlighting key challenges in its study. The author also briefly highlights successful applications of game theory in the innovation literature. Second, the author systematically reviews and synthesizes entrepreneurship research employing game-theoretic models and concepts. The author's objective is to provide a state-of-the-art overview of the use of game theory in entrepreneurship. Findings – Broadly, the author categorizes entrepreneurship-game theory articles into three groups based on their scope and purpose: entrepreneurial policy applications, inter-firm applications and entrepreneurship theory applications. Entrepreneurial policy applications include entrepreneurs and the government or policy as the main players in a game. Inter-firm applications encompass games between entrepreneurs and other private entities. Entrepreneurship theory applications include articles that utilize game theory to advance the author's understanding of entrepreneurial behavior and/or mechanisms in the market. Originality/value – To the best of the author's knowledge, no previous paper has reviewed the use of game-theoretic approaches and models in entrepreneurship literature. This study addresses this research gap.

The analysis of the factors affecting Qatar's gas productive behavior is of particular importance due to its joint membership with Iran in the Gas Exporting Countries Forum and the gas field of South Pars-North Dome. In this research, initially, the Qatar's gas productive behavior against Iran's gas production from the gas field of South Pars and other variables affecting the supply and demand of global gas (the gas production of other members of this Forum, global gas demand, natural gas price, the global price of crude oil and shale gas production) was investigated using seasonal data from 2001 to 2021 and auto-regressive method with distributed lags (in the long term). Then, using the Game Theory and Nush Equilibrium, the optimal production strategy was extracted from the gas field of South Pars - North Dome (in the period under review) in order to compare with the Qatar's gas productive behavior against Iran's production from the South Pars gas field. The results showed that when there is a change (increase/decrease) in the mentioned variables, the Qatar's gas productive behavior does not always change in line with these variables; also, in the long term, with a one percent increase in Iran's gas production from the South Pars, Qatar's gas production has increased by more than one percent. Finally, the results showed that the optimal production strategy for Qatar from the gas field of South Pars-North Dome in the period under review has been an increase in production, which is in line with the behavior of this country against Iran's gas production from the gas field of South Pars in the long term.

Abstract A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.