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For servo mechanical systems, it is typically desirable to consider a series of equality and inequality constraints in the control analysis. While constraint-following control strategies based on the Udwadia–Kalaba (U–K) equation efficiently tackle equality constraints, the handling of inequality constraints remains underexplored, particularly in the presence of time-varying uncertainties. In this paper, an adaptive robust constraint-following control scheme is herein established based on fuzzy description of uncertainty and cooperative game theory. Leveraging the diffeomorphism approach tailored for the inequality constraint, we reformulate the constraint equation and the dynamical model, thereby appropriately incorporating both equality and inequality constraints into a unified constraint-following problem. Subsequently, a robust control strategy with the leakage-type adaptive law is proposed to render the constraint-following error uniformly bounded and uniformly ultimately bounded. Furthermore, to search for the optimal selection for control parameters, this optimization problem is presented as a two-player cooperative game by formulating the fuzzy performance index (i.e., the cost function), with the index being influenced by the tunable parameter (i.e., the player). As supported by rigorous proofs, the existence of the Pareto optimality is guaranteed and its solution is effectively attained, leading to enhanced control system performance. Lastly, a numerical example of an active roll control system (ARCS) is presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The current studies for Tethered Space Net Robot (TSNR) typically treat the tension force induced by the net as a disturbance and employ passive suppression for compensation. However, these approaches not only result in excess fuel consumption but also overlook the intrinsic nature of the net dynamics. When one Maneuverable Unit (MU) maneuvers, it generates a tension force on the net that is transmitted to other MUs. This force not only affects the control accuracy of other MUs but also has a positive effect. In this paper, an Active Energy Management Distributed Formation Control (AEMC) strategy is proposed to reveal this kind of interaction and maximize its advantage. Firstly, an energy recovery framework is established, allowing each MU can effectively utilize the tension force due to the net. Specifically, a neural network estimator is designed to capture the hysteresis relationship in which MUs influence each other by transmitting forces through the net. Furthermore, to achieve the cooperative completion of tasks, a game based control scheme is proposed to optimize the control input and tension force collectively. Through prediction and optimization, MUs actively manage their impacts on each other, thereby controlling the influence of tension force on the tracking errors of others. Finally, numerical simulations are conducted to showcase the effectiveness of the proposed approach. • Proposed a novel approach for Tethered Space Net Robot (TSNR) in debris removal. • Developed an energy management framework to utilize tension force effects. • Proposed a DNN-based estimator to predict tension force. • Designed a cooperative game based control for MUs to manage effects actively. [ABSTRACT FROM AUTHOR]

This research presents a game theory model for extending a cooperative advertising and contract design (pricing and quality investment) problem in a manufacturer–retailer game. The demand of customers depends on selling price, quality, and advertisement expenditures. The manufacturer invests in national advertising (brand name investment) and the quality of the product, and the manufacturer decides on the wholesale price of the product. The retailer invests in local advertisement and decides about sale price of the product. The problem is analyzed in four different conditions: centralized, decentralized, revenue-sharing (contract design), and market segmentation conditions. The market segmentation condition considers customers' characteristics into three different types: price-oriented customers, quality-oriented customers, and customers who are not price or quality oriented (individually). The model is solved in all conditions and the results are presented. Sensitivity analysis of key parameters is performed to analyze different situations and the optimal behavior of the supply chain players. Application of the proposed approach for a Middle Eastern country is provided and a real case of the generic medicines market in Iran investigates the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Contractors' decisions are individually rational and not always based on fairness that is needed in collaborative projects, such as Construction Joint Ventures (CJVs). This paper provides a comparative analysis between rational and fair-based sharing in CJVs risk management, along with the commonly used investment-based approach. The analysis is divided into two processes; (1) the contingency cost (CC) calculation process to generate the CCs based on the probabilities and impacts of the project's different risk factors; and (2) the sharing process to allocate the payoff shares to the participating players using three different cooperative game theory (CGT) solution concepts, namely Kernel, Shapley value and τ-value. The analysis is carried out using two illustrative case studies drawn from the literature. The authors mathematically analyzed the stability of the coalition, and evaluated the results through a questionnaire survey held by construction practitioners to offer their perception on the proposed shares. The results show that fair-based approaches resulted in the most stable coalition formation compared to rational-based and investment-based shares. This shows the need to adopt fairness into the decision-making process of contractors in CJVs. Ultimately, this research shall help the decision-makers to craft agreements that stabilize collaborative construction projects. [ABSTRACT FROM AUTHOR]

One of the most significant solution concepts in cooperative grey game theory is the grey Shapley value. This value is a fascinating one among the models and methods of operations research, and has been the subject of extensive study by other researchers. The objective of this study is to characterize and redefine this value in cooperative games where coalition values are grey numbers. In this study, the grey Shapley value is characterized by the following axioms: G -gain loss, G -null player, and G -differential marginality. Finally, this study concludes with an investigation of some applications involving production costs. This study is based on an investigation of the costs incurred when milk producers collaborate. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a cooperative grey game that may be used to simulate market scenarios with two corners and participants that experience grey uncertainty about the outcome of collaboration is introduced and studied. A strong player known as the big boss is in one corner, while players in the other corner depend on the big boss's support to survive. This paper introduces and studies a class of cooperative grey games suitable to model market situations with two corners where players face grey uncertainty regarding the outcome of cooperation. We discuss how the model of cooperative grey games extends the cooperative game theory literature, and studies its existing and potential applications in two corner market situations with grey data. [ABSTRACT FROM AUTHOR]

Introduction: The rapid development of the transportation industry has led to increased demand for commercial vehicles, particularly trucks, which are subject to complex driving conditions. These conditions significantly impact the comfort and safety of drivers, as well as the potential for cargo damage due to frequent and severe road bumps. To address these challenges, there is a growing need to design vehicle suspension systems that provide superior vibration isolation, ensuring improved driving stability and safety. Modern heavy-duty vehicles, especially trucks, typically feature air suspensions with adjustable load-bearing capacity and variable stiffness, offering advantages over traditional mechanical and hydraulic suspensions. However, as requirements for ride comfort and safety continue to increase, conventional air suspensions are no longer sufficient to meet the demands of modern transportation. The quasi-zero stiffness air suspension (QZAS) has emerged as an advanced solution, combining high static stiffness with low dynamic stiffness, making it ideal for vibration isolation. QZAS integrates air suspension with a negative stiffness cylinder to balance the positive stiffness of the air suspension. This structure achieves an overall zero stiffness effect at the vibration equilibrium position, significantly reducing the inherent frequency of the suspension system and enhancing ride comfort. Despite its advantages, QZAS systems must adapt to varying road conditions and load weights, which require dynamic adjustments to the air pressure in the negative stiffness cylinders to maintain optimal performance. This paper explores the optimization of air pressure in QZAS systems to enhance vehicle performance under diverse driving conditions. Materials and methods: This study employs a multi-objective genetic algorithm for the optimization of four air pressure values in the QZAS system. The goal is to obtain a Pareto optimal solution set that balances various performance indices related to vibration isolation and vehicle stability. Due to the complexities involved in the inflation and deflation processes of the negative stiffness cylinders, which may cause delays in actuator response and hysteresis effects, the Monte Carlo method is utilized to analyze the system's behavior under varying initial conditions and parameter uncertainties. This method allows for an evaluation of the stability and uncertainty of the optimization results. To further enhance coordination within the suspension system, cooperative game theory (CGT) is applied to allocate appropriate weights to the performance indices, ensuring optimal collaboration between the air suspension components and the negative stiffness cylinders. This approach is specifically designed to address the challenges posed by fluctuating road conditions and dynamic load changes. Results: The optimization results show that adjusting the air pressure can significantly improve the acceleration of all four wheels and the body attitude of the vehicle. These enhancements significantly improve vibration isolation and directly contribute to improving the driving safety and comfort of commercial vehicles. The CGt-based optimization method effectively adapts air pressure to different driving scenarios, ensuring optimal suspension pressure. Conclusion: In conclusion, the optimization method based on Cooperative Game Theory effectively improves the vibration isolation performance of the QZAS system by optimizing the air pressure in the negative stiffness cylinders. This approach enhances both the safety and comfort of commercial vehicles, making it a promising solution for vibration control. Future work should focus on real-world validation and exploring its potential for other types of suspension systems. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyze notable distribution dispute cases from Islamic law history. The authors will assess these alongside resolutions proposed by historical authorities, some of which evolved into established Islamic case law. In addition, the authors intend to apply classic fair division rules to these cases, providing alternative solutions. Using a game-theoretical approach, the authors plan to compare Islamic solutions with traditional division rules through axiomatic analysis. The goal of this study is to systematically explore the unique principles underpinning Islamic distributions. Design/methodology/approach: In this study, the authors collate Islamic inheritance law disputes involving conflicting claims, unresolvable by primary Islamic law sources, from historical and modern texts. The authors formally model these as claims problems, surplus-sharing problems and adapted claims problems. Concurrently, the authors gather the proposed solutions and historical backgrounds offered by the era's authorities and jurists. These solutions are axiomatically generalized into rules, while the axioms characterizing distribution rules are checked if they are aligned with Islamic norms and values. This approach facilitates a comparison between Islamic distributions and classic division rules. Findings: The 'Awl and Radd doctrines, used in Islamic inheritance law, are axiomatically equivalent to the Proportional Rule, a prevalent non-Jewish division rule. These doctrines present solutions impervious to manipulation by legal heirs through rights transfer, unlike other possible distributions. Ibn 'Abbas' solution for Awliyya cases uses sequential priorities and diverges uniquely from classic fair division rules in the literature. In addition, it is established that Abu Yusuf's (b. 729) distribution for a legal dispute is axiomatically identical to Abraham ibn Ezra's (b. 1089) division rule. Research limitations/implications: There is a noticeable dearth of comprehensive studies investigating contentious disputes concerning resource claims within Islamic law. Many of these studies are lacking in-depth analyses of diverse cases, casting doubts on their reliability. As a result, a robust focus is needed on case collection prior to any analytical process. Future research should concentrate on collating instances of fair division problems throughout Islamic history, as well as separately collecting methods of Islamic sharing. This procedure may lead to the characterization of various Islamic regulations, thereby emphasizing distinct Islamic principles. In forthcoming studies, conducting an exhaustive axiomatic evaluation of the cases and proposed resolutions is imperative. Practical implications: This research illuminates existing knowledge gaps, setting a course for novel research trajectories. It underlines the fair division literature's oversight of disputes within Islamic law, despite the plentiful existence of contentious cases. The research underscores the relevance of cooperative game theory as a tool for dissecting Islamic legal disputes. By accounting for unique Islamic norms and principles, this study lays a foundation for a nuanced comprehension of the dynamics and outcomes of legal disputes. By integrating an interdisciplinary approach, this research strives to bridge the gap between game theory and Islamic law. Social implications: Beyond addressing a significant research lacuna, this study carries extensive societal implications. By shedding light on enduring debates within Islamic law, it encourages a rejuvenated understanding of the evolution and interpretation of legal disputes. The axiomatic disparities between rulers' and jurists' methods provide invaluable insights within the Islamic context, bolstering the understanding of sociocultural dynamics that influence legal decision-making. This research has the potential to shape legal discourse, guide policymaking and spur scholarly, juristic and societal dialogue. Consequently, it may foster a more comprehensive and enlightened approach toward the resolution of legal disputes in Islamic law. Originality/value: To the best of the authors' knowledge, this study is the first to examine Islamic law's historical legal disputes from a game-theoretical standpoint. Existing studies rarely collect distribution disputes systematically, and none scrutinize the axiomatic rationales underlying authorities' and jurists' distributions, opting instead to focus on historical backgrounds. While the fair division literature extensively examines disputes, it often overlooks those originating from Islamic law, which presents a rich source of disputes that can be modeled as fair division problems. This research makes a distinct contribution by incorporating disputes from Islamic law into the existing body of cooperative game theory literature. [ABSTRACT FROM AUTHOR]

The Myerson value is a characterized allocation rule for analyzing games with a coalitional structure, merging ideas from graph theory and classical cooperative game theory. In this article, we extend these ideas to characterize a solution for cooperative interval games, which consist of a finite set of players, where the coalition values are compact intervals of real numbers. We adapt the axiom of efficiency by connected components, which Myerson proposed in the classical theory of cooperative games with graphs, to interval games. Additionally, we introduce the concept of superadditivity under $ \tau $ and define a new axiom, which we call equity with respect to $ \tau $. With these two properties, we propose a new characterization of the interval Myerson value. [ABSTRACT FROM AUTHOR]

This study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60 °C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60 °C and 30 °C, respectively, for a concentration of 1 vol% compared to base fluid. Similarly, graphene oxide-TiO2 hybrid nanofluids achieved thermal conductivity and viscosity ratios exceeding 43% and 144% compared to the base fluid at similar conditions. This data highlights the significance of nanofluid concentration in influencing thermal conductivity, while temperature was found to have a more pronounced effect on viscosity. To tackle the challenge of modeling the thermophysical properties of these hybrid nanofluids, advanced machine learning models were applied. The Random Forest (RF) model outperformed others (Gradient Boosting and Decision Tree) in both the cases of thermal conductivity and viscosity with greater adaptability to handle fresh data during model testing. Further analysis using shapely additive explanations based on cooperative game theory revealed that relative to temperature, nanofluid concentration contributes more to the predictions of the thermal conductivity ratio model. However, the effect of nanofluid concentration was more dominant in the case of viscosity ratio model. [ABSTRACT FROM AUTHOR]

The co-gasification of waste biomass and low-quality coal to produce syngas as fuel is an effective and sustainable approach in the waste-to-energy paradigm. The modeling of this process is however complex and time-consuming. The data-driven machine learning (ML) approaches enhanced with explainable artificial intelligence (XAI) are capable of solving this issue. Hence, in this study, five different ML techniques including Linear Regression (LR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed for the model-prediction. The ultimate analysis, proximate analysis, and operation setting data were employed for the control factors syngas yield and lower heating value (LHV) prediction. The prediction results showed that XGBoost was superior to other ML approaches with an R2 value of 0.9786, mean squared error (MSE) of 10.82, and mean absolute percentage error (MAPE) of 9.8% during model testing of the syngas yield model. In the case of the syngas LHV model an R2 value of 0.9992, MSE of 0.03, and MAPE of 0.83% was observed. XGBoost was superior for both syngas yield and LHV models. The analysis of feature importance and its quantification was conducted by Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). SHAP and LIME approaches revealed that reaction temperature and biomass mixing ratio were the most important control factors for the syngas yield model. • Waste biomass and coal co-gasification produce sustainable syngas fuel. • Data-driven ML simplifies complex gasification process modeling. • XGBoost excelled in syngas yield prediction with R2 of 0.9786. • Syngas LHV model achieved R2 of 0.9992 with XGBoost. • SHAP and LIME identified key factors: temperature and biomass mix. [ABSTRACT FROM AUTHOR]

This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges. [ABSTRACT FROM AUTHOR]

The technical complexity of organizing energy systems' operation has recently been compounded by the complexity of reconciling the interests of individual entities involved in interactions. This study proposes a possible solution to the problem of modeling their relationships within a large system. Our solution takes into account multiple levels of interactions, imperfect information, and conflicting interests. We present a mathematical statement of the problem of optimal interactions between the centralized system and prosumers in the integrated energy system (IES) with due consideration of the layered architecture of the IES. The paper also contributes a model for arranging the interactions between centralized and distributed energy sources for cases when IES prosumers form coalitions. The implementation of this model is based on multi-agent techniques and cooperative game theory tools. In order to arrive at a rational arrangement of the interactions of prosumers in the IES, the model implements different approaches to the allocation of the coalition's total payoff (the Shapley value, Modiclus, PreNucleolus solution concepts). Furthermore, we propose a criterion for deciding on the "best" imputation. We contribute a multi-agent system that implements the proposed model and use a test IES setup to validate the model by simulations. The results of the simulations ensure optimal interactions between the entities involved in the energy supply process within the IES and driven by their own interests. The results also elucidate the conditions that make it feasible for prosumers to form coalitions. [ABSTRACT FROM AUTHOR]

Urbanization intensifies the need for sustainable public transportation that balances financial viability, environmental sustainability, and social equity. Traditional fare-setting methods often focus narrowly on financial objectives, neglecting broader impacts. This study introduces a novel collaborative game-theoretic model integrating user sentiment analysis to optimize fare policies. By incorporating utilities of passengers, operators, and governments, and employing the Shapley value for fair benefit distribution, this model aims to maximize social welfare. The methodology frames fare optimization as a cooperative game among stakeholders, integrating passenger preferences through sentiment analysis. The social welfare function combines the utilities of all stakeholders and is maximized under operational, environmental, and financial constraints. Implemented in Python and applied to Isparta, Turkey, the model identifies an optimal fare of 19.5 TL (ranged between 14 and 26.50 TL) that maximizes social welfare, aligning closely with existing fares. Shapley value analysis distributes the benefits, assigning 221,457 (35.6%) units to passengers, 54,562 (8.7%) units to operators, and 347,433 (55.7%) units to the government, highlighting significant environmental gains for the government. Sensitivity analyses confirm the model's robustness across varying trip volumes, suggesting its applicability to diverse urban settings. This research contributes to socially equitable and user-centric fare policies by providing a comprehensive framework aligning stakeholder interests. Policymakers can leverage this model to design fare strategies promoting sustainability, efficiency, and collaboration in public transportation systems. [ABSTRACT FROM AUTHOR]

To improve the regulatory capacity of distributed resources within the park and enhance the flexibility of market transactions, this paper introduces a distributed dispatch and profit allocation method grounded in cooperative game theory and the generalized Nash bargaining framework. Initially, models for individual park equipment are established. Subsequently, a distributed dispatch model is constructed, followed by the development of a profit allocation strategy based on contribution levels, using the generalized Nash bargaining method. The model is solved using the alternating direction method of multipliers. The results show that the proposed approach achieves fast convergence, optimizes resource sharing and mutual support within the park, lowers operational costs, ensures a fairer distribution of profits, and promotes increased cooperation among park entities. [ABSTRACT FROM AUTHOR]

Intense market competition has driven small- and medium-sized enterprises (SMEs) in the manufacturing sector to collaborate and form supply chain coalitions, which can improve the information flow and resource sharing and significantly enhance supply chain management efficiency. However, the distribution of cooperative benefits poses a core challenge for the long-term stability of coalitions. This paper addresses the impact of dynamic changes in complex business environments by utilizing triangular fuzzy numbers to represent the value of coalition, effectively depicting the uncertainty and ambiguity in the cooperation process. Compared to traditional models (which do not use triangular fuzzy numbers), this model is better suited to dynamic changes, offering flexible response mechanisms that ensure adaptability and fairness in the decision-making process. In addition, considering the influence of each member's weight in the coalition, the fuzzy comprehensive evaluation method is used to determine the weights. With the goal of minimizing the dissatisfaction of enterprises in benefit distribution, a least square contribution with triangular fuzzy numbers is constructed to replace the marginal contribution of the classical Banzhaf value, and an improved Banzhaf value based on the player's triangular fuzzy number-weighted excess contribution is proposed to arrive at a fair and reasonable benefit allocation strategy in order to enhance the long-term stability and cooperative benefits of coalition. By analyzing an example of the supply chain coalition, the effectiveness of the proposed improved Banzhaf value is verified, which satisfies the uniqueness, the individual rationality, and the group rationality. It not only promotes the level of risk management and decision making under the uncertainty conditions of complex business, but also deepens the theoretical foundation of cooperative game theory and expands its possibilities in practical applications and future development. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the test of availability of a core solution that satisfies all associated parties/players; (ii) the measurement of players'/parties' rational minimal and maximal return expectations from the grand coalition regarding their all individual and sub-group strategies and associated return rationalities; (iii) the determination of the critical players/parties in the grand coalition. The study's main contributions are the provision of a methodology that identifies spatially/geographically critical players/parties and the design of an algorithm for handling exponentially growing strategies alongside increasing numbers of players/parties. In sum, a comprehensive cooperative game theory framework is introduced to measure the significance of location and neighborhood relations in conjunction with the magnitude of the players/parties. The case of the EU has revealed the union's geographically critical countries, with Germany being found to be the most influential. [ABSTRACT FROM AUTHOR]

With the advancement of distributed energy systems, energy sharing has emerged as a crucial trading mechanism on the demand-side, enabling participants to share self-generated energy with their neighbors through contractual agreements. Nevertheless, a comprehensive analysis is needed to balance the benefits among energy prosumers, given their distinct characteristics. This paper proposes a multi-energy sharing framework with flexible demand-side management based on full cooperation. We evaluate the economic and environmental performance of sharing participants, considering the impacts of different operation modes and diverse demand profiles. Cooperative game theory is employed to maximize the social welfare of all participants, with the different allocation schemes are used to distribute the cooperative surplus among stakeholders. The fairness of these schemes is assessed to ensure the feasibility and equity of the proposed framework. The results indicate that the centralized multi-energy sharing framework yields win-win outcomes for both individual and collective interests. Specifically, the total cost and carbon dioxide emissions of prosumers in the shared scenario are reduced by 13% and 16%, respectively, compared to individual operation scenarios. Moreover, thermal energy management is critically important for energy sharing. Furthermore, varying combinations of building types significantly affect cost savings and emission reductions, influencing energy sharing patterns and quantities. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explores the dynamics of international strategic learning alliances through the lens of game theory, incorporating complexity and cooperative game theories to develop a model of organizational evolution. Using simulations and network resources, we examine 1200 cases to assess the costs and benefits of inter-organizational cooperation, with a focus on mutual payoffs and strategic decision-making. Our research addresses key gaps in the literature by analyzing how game-theoretic structures impact the success of alliances, providing actionable insights for firms aiming to enhance strategic partnerships. The findings offer valuable guidance for international partners involved in learning alliances, emphasizing the importance of aligning institutional responses with perceived risks and opportunities. By identifying the motivations and success factors behind strategic alliances, organizations can better formulate optimal strategies for collaboration. This paper contributes to the discourse on inter-firm cooperation by highlighting the complexities of strategic learning alliances and offering new perspectives for future research. [ABSTRACT FROM AUTHOR]

Recent advances in geological exploration and oil and gas development have highlighted the critical need for accurate classification and prediction of subterranean lithologies. To address this, we introduce the Meta-Vision Transformer (Meta-ViT) method, a novel approach. This technique synergistically combines the adaptability of meta-learning with the analytical prowess of ViT. Meta-learning excels in identifying nuanced similarities across tasks, significantly enhancing learning efficiency. Simultaneously, the ViT leverages these meta-learning insights to navigate the complex landscape of geological exploration, improving lithology identification accuracy. The Meta-ViT model employs a support set to extract crucial insights through meta-learning, and a query set to test the generalizability of these insights. This dual-framework setup enables the ViT to detect various underground rock types with unprecedented precision. Additionally, by simulating diverse tasks and data scenarios, meta-learning broadens the model's applicational scope. Integrating the SHAP (SHapley Additive exPlanations) model, rooted in Shapley values from cooperative game theory, greatly enhances the interpretability of rock type classifications. We also utilized the ADASYN (Adaptive Synthetic Sampling) technique to optimize sample representation, generating new samples based on existing densities to ensure uniform distribution. Our extensive testing across various training and testing set ratios showed that the Meta-ViT model outperforms dramatically traditional machine learning models. This approach not only refines learning processes but it also adeptly addresses the inherent challenges of geological data analysis. [ABSTRACT FROM AUTHOR]

Integrating human behavior into agent-based models has been challenging due to its diversity. An example is strategic coalition formation, which occurs when an individual decides to collaborate with others because it strategically benefits them, thereby increasing the expected utility of the situation. An algorithm called ABMSCORE was developed to help model strategic coalition formation in agent-based models. The ABMSCORE algorithm employs hedonic games from cooperative game theory and has been applied to various situations, including refugee egress and smallholder farming cooperatives. This paper discusses ABMSCORE, including its mechanism, requirements, limitations, and application. To demonstrate the potential of ABMSCORE, a new application example is given, which is based on a complex version of the Thomas Schelling's segregation model. The intent of the paper is to provide the potential user with enough information so that they can apply ABMSCORE to their simulation products. [ABSTRACT FROM AUTHOR]

Summary: Relay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach. [ABSTRACT FROM AUTHOR]

Germany has seen the rise of right-wing populism, similar to various other European countries. A recent example is the emergence of the Alternative for Germany (German: Alternative für Deutschland, AfD) party. Since 2015, the party has had much success in elections. Accordingly, in this article we analyse voting power distribution in German states before and after 2015. Within this analysis, we identify which parties gained and lost voting power. To calculate voting power distribution in parliaments, we use cooperative game theory. In particular, we apply the EC value considering that parties exclude coalitions with other parties. Of course, the methodology presented in this article should not be used for analysis alone but should complement existing analyses (from the fields of demoscopy and political science). [ABSTRACT FROM AUTHOR]

Distributed power supply has seen a significant rise because of the increasing demand for renewable energy. This rise has also presented various challenges, including independent operation and a lack of coordination among the sources. As a result of these challenges, these distributed power supplies are combined into a virtual power plant (VPP). This paper addresses these issues of interaction and coordination with a proposal for multitype VPP and modeling technology. First, it recommends establishing a peer‐to‐peer (P2P) transaction framework. Second, Nash bargaining cooperative game theory comes into play, which involves multiple stakeholders in establishing a P2P transaction framework among stakeholders. After that, this proposed model is solved using the alternating direction multipliers method (ADMM). In the end, the verification of this proposed model is achieved by the numerical example that ensures that the VPP alliance operates stably. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the profitability of wind energy and demand response (DR) resources participating in the energy and frequency regulation markets. Since wind power producers (WPPs) must reduce their output to provide up-regulation and DR aggregators (DRAs) have to purchase additional power to facilitate down-regulation, this may result in revenue loss. If WPPs coordinate with DRAs, these two costs could be reduced. Thus, it would be profitable for WPPs and DRAs to form a coalition to participate in the regulation market. To better utilize the frequency response characteristics of wind and DR resources, this paper proposes a cooperation scheme to optimize the bidding strategy of the coalition. Furthermore, cooperative game theory methods, including Nucleolus- and Shapley-value-based models, are employed to fairly allocate additional benefits among WPPs and DRAs. The uncertainties associated with wind power and the behavior of DR customers are modeled through stochastic programming. In the optimization process, the decision-maker's attitude toward risks is considered using conditional value at risk (CVaR). Case studies demonstrate that the proposed bidding strategy can improve the performance of the coalition and lead to higher benefits for both WPPs and DRAs. Specifically, the expected revenue of the coordinated strategies increased by 12.1% compared to that of uncoordinated strategies. [ABSTRACT FROM AUTHOR]

We unified the recent literature with the goal to contribute to the discussion on how genetic diversity might best be conserved. We argue that this decision will be guided by how genomic variation is distributed among manageable populations (i.e., its spatial structure), the degree to which adaptive potential is best predicted by variation across the entire genome or the subset of that variation that is identified as putatively adaptive (i.e., its genomic structure), and whether we are managing species as single entities or as collections of diversifying lineages. The distribution of genetic variation and our ultimate goal will have practical implications for on-the-ground management. If adaptive variation is largely polygenic or responsive to change, its spatial structure might be broadly governed by the forces determining genome-wide variation (linked selection, drift, and gene flow), making measurement and prioritization straightforward. If we are managing species as single entities, then population-level prioritization schemes are possible so as to maximize future pooled genetic variation. We outline one such scheme based on the popular Shapley value from cooperative game theory that considers the relative genetic contribution of a population to an unknown future collection of populations. [ABSTRACT FROM AUTHOR]

This paper proposed an eight-phase transit signal priority control method, CBQL-TSP, based on a hybrid decision-making approach combining cooperative game theory and reinforcement learning, aiming to address the low efficiency of transit signal priority control in intelligent transportation systems. The method abstracted the allocation of bus signal priority into a multi-objective decision-making problem with an eight-phase signal sequence, refining the signal phase sequence into priority and non-priority phases, and constructing a cooperative game model. The paper introduced a hybrid decision-making algorithm, CBQL, to solve the multi-objective decision-making problem of eight-phase signal timing. The algorithm calculated the marginal contributions of each game member by solving the Shapley value function and constructed the state transition probability equation based on the Shapley value ratio. Compared to conventional control methods, CBQL-TSP incorporated the fairness principle of cooperative game theory and the adaptive learning ability of Q-learning, enabling dynamic adjustment of signal timing based on real-time traffic conditions. This method offered higher flexibility and efficiency in priority signal control. This paper established a simulation test platform to compare the average waiting car count and average travel time under different demand levels, evaluating the stability and efficiency of the method. The experimental results demonstrate that the proposed method exhibits higher stability. Furthermore, the overall average travel time is reduced by approximately 24.57%, and the average travel time for buses is reduced by about 37.40%. CBQL-TSP demonstrates higher control efficiency at bus-priority intersections, significantly reducing both overall and bus average travel times. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a novel decentralized machine learning framework that differs from traditional centralized machine learning. It enables multiple participants to collaborate on training models without sharing raw data directly. Participants train the local model with their data and only upload the model parameters. To achieve a fair allocation of benefits by the common global model, a standard is needed to evaluate each model providers' contribution to federated learning. Shapley value is a classic concept from cooperative game theory and is often used in data evaluation for machine learning. This study introduces the Shapley value in privacy-preserving federated learning (PPFL) to construct a contribution measuring module for measuring the contribution of each model provider to the learning task and propose a verification mechanism for the contribution results. Compared to the other related works for contribution measurement by Shapley value, this work achieves higher privacy protection, where local participants' data sets, local model parameters, and global model parameters are concealed. In addition, the verification of the fairness of contribution measurement is also supported. Moreover, this work achieves the access control of aggregated global models through the concept of threshold identity-based encryption, where model consumers can only gain access to the specific aggregated global model if they are authorized by sufficient model providers. [ABSTRACT FROM AUTHOR]

Value capture theory, or the application of cooperative game theory to theory and pedagogy in strategic management, has been growing in influence, but its potential for empirical research is underexploited. I briefly review value capture theory and then offer thoughts on how it may motivate and inform empirical research that both tests specific elements of value capture theory and uses insights and econometric models based on value capture theory to deepen our understanding of many topics of interest to strategy scholars and practitioners. [ABSTRACT FROM AUTHOR]

Game theory emerged as a science in the second half of the 20th century. It managed to prove itself well in the analysis of economic situations involving several subjects of economic activity (players), whose interests are completely or partially opposite. At the same time, in a number of cases, the solution of the game satisfied all players, but was not the most profitable (there was a Nash equilibrium), and in a number of other cases, it was possible to take into account the interests of all parties to the maximum (there was a Pareto optimal solution). The transfer of the principles of game theory to other areas turned out to have a number of difficulties associated, among other things, with the correct interpretation of strategies and gains of the parties in a conflict situation. For this reason, despite the obvious benefit from the possible application of game theory methods to problems of a fair distribution of quotas for catching fish and other seafood, this step has not been taken until recently. In this paper, we consider a scheme for applying the algorithms of the theory of bimatrix and cooperative games on the example of solving the problem of finding the percentage of the allowable catch of the Greenland halibut in the Barents Sea for two countries participating in the catch and give a meaningful interpretation of the results. The basis for the calculations was real data collected by the Russian–Norwegian Fisheries Commission in recent decades to determine the proportions of the catch of the indicated fish species in the respective sea zones. Since not all components of the payoff matrices of the players are uniquely determined, it became possible to perform a parametric analysis of the mathematical model of the conflict situation both in the search for an equilibrium solution and in the implementation of the arbitration scheme. The work is an extended and supplemented version of the report [2]. [ABSTRACT FROM AUTHOR]

Generalized Additive Models (GAMs) are widely used in statistics. In this work, we aim to tackle the challenge of identifying the most influential variables in GAMs. To accomplish this, we introduce a variance allocation approach based on the Shapley value. We derive a closed-form expression for this importance index, which allows for its computation on high-dimensional datasets and with any dependence structure. We discuss the practical implication that when a variable's importance is negligible, it can be safely eliminated from the GAM, simplifying the model. Through our case studies, we demonstrate that Shapley values offer more informative insights than p-values in terms of ranking the importance of variables. All the code is available online in the . [ABSTRACT FROM AUTHOR]

This paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors are identified by Block Matching Motion Estimation Algorithm (BMMEA). Based on these motion vectors, the Region of Interest (ROI) is selected. The selected ROI is grouped using their momentum. In order to add an additional layer of security, the secret image is scrambled using pixel-wise Arnold Transform in the next step. Finally, based on the ROI groups, the scrambled secret data is embedded into the ROI's locations. An ideal and perfect video steganography system should be able to maintain the quality of the video after embedding the secret data. The quality factors of the video steganography approach include perceptible invisibility, payload capacity, and robustness. However, these quality factors are conflicted with each other. In order to address this issue, having these three quality factors as players a cooperative 3-players game theory approach is proposed. This technique provides an optimal solution for the video steganography framework using strategy adaption. The optimal solution is acquired by applying the Iterative Elimination of Strictly Dominant Strategies (IESDS) method. The achieved optimal solution comes up with the best trade-off between the quality factors. Experimental results have deduced the best optimal solution from the proposed three-player game theoretic approach that helps the video steganography approach to resolve the quality and security issues. It is also observed that the optimized proposed method outperforms the contemporary methods by attaining significant outcomes. [ABSTRACT FROM AUTHOR]

We develop a measure-theoretic framework for dynamic Shapley values in Banach spaces, extending classical cooperative game theory to continuous-time, infinite-dimensional settings. We prove the existence and uniqueness of strong solutions to stochastic differential equations modeling competence evolution in Banach spaces, establishing sample path continuity and moment estimates. The dynamic Shapley value is rigorously defined as a càdlàg stochastic process with an axiomatic characterization. We derive a martingale representation for this process and establish its asymptotic properties, including a strong law of large numbers and a functional central limit theorem under α-mixing conditions. This framework provides a rigorous basis for analyzing dynamic value attribution in abstract spaces, with potential applications to economic and game-theoretic models. [ABSTRACT FROM AUTHOR]