Search Results (157)

This paper investigates the problem of contract coordination in a two-tier multi-unit auction supply chain consisting of a seller and an auction house. We theoretically show that the conventional commission-based mechanism distorts the transmission of demand information from the demand side to the supply side, thereby preventing effective supply chain coordination. In contrast, guaranteed auction contracts can achieve coordination under both cooperative and non-cooperative game frameworks. Under the cooperative game setting, profits are allocated according to a Nash bargaining solution, in which each party receives its disagreement payoff and a bargaining-power-weighted share of the surplus, with risks and returns being allocated symmetrically. Under the non-cooperative game setting, the supply chain leader can appropriate a larger share of the total profit while bearing relatively lower risk. These results indicate that, as the supply chain leader, the auction house can select different cooperation modes under guaranteed auction contracts according to its bargaining position, but profit allocation should be benchmarked against the cooperative game outcome in order to enhance the long-term competitiveness and stability of the supply chain. Full article

Coordinating multiple electric–hydrogen regional energy systems (EHRESs) under renewable uncertainty while ensuring rational benefit allocation remains a significant challenge. To address this issue, this paper proposes a cooperative energy mutual-assistance strategy for multiple EHRESs. An electric–hydrogen coupled operational framework integrating power-to-hydrogen technology is established, and a tiered carbon trading mechanism is incorporated to curb carbon emissions. Renewable-generation uncertainty is modeled using chance constraints. To solve the coordinated operation problem in a distributed manner, the cooperative model is decomposed into two tractable subproblems and solved using ADMM. In addition, an asymmetric Nash bargaining-based payoff allocation method incorporating aggregated contribution rates is developed to reflect heterogeneous participant contributions. Case studies are conducted to evaluate convergence, economic performance, emission reduction, and payoff allocation. The results show that the proposed method supports contribution-aware energy trading and benefit sharing among EHRESs while reducing carbon emissions by 8.54% and operating costs by up to 17.17%. Full article

Vehicular Edge Computing (VEC) can effectively guarantee the service experience of user vehicles, but resource-limited Roadside Units (RSUs) may face insufficient computing capacity during task peak periods. Utilizing Assisting Vehicles (AVs) with idle resources to share computing power can alleviate the pressure on RSUs. However, existing studies often fail to adequately incentivize selfish assisting vehicles to contribute resources and frequently lack a global optimization perspective from the overall system welfare. To address these challenges, this paper proposes an incentive-driven utility-balanced task offloading framework that aims to maximize social welfare while jointly optimizing resource allocation and profit pricing. Specifically, we first formulate the resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem. To solve this problem, we introduce hybrid action representation learning to VEC for the first time and propose the HyAR-PPO algorithm to jointly optimize discrete offloading decisions and continuous resource allocation. This algorithm maps heterogeneous hybrid actions to a unified latent representation space through a Variational Autoencoder for the solution. Subsequently, equilibrium prices among user vehicles, Computation Service Providers (CSPs), and assisting vehicles are determined through Nash bargaining games, satisfying individual rationality constraints and achieving Pareto-optimal fair profit distribution. Experimental results demonstrate that the proposed framework can effectively coordinate multi-party interests. Compared with mainstream methods, the approach based on hybrid action representation learning achieves a significant improvement in social welfare, with its advantages being more pronounced in medium-to-large-scale scenarios. Full article

Between 1959 and 1962, Alvin Scodel, J. Sayer Minas, and colleagues conducted some of the earliest laboratory studies of strategic interaction using non-zero-sum games. Working at the margins of economics in the Journal of Conflict Resolution, they documented a striking pattern: subjects frequently chose options that reduced an opponent’s payoff by more than their own, even when mutual cooperation was both individually and collectively optimal. These results—especially the behavior observed in their so-called Game H4, a Harmony Game in which cooperation strictly dominated defection—anticipate a central insight of evolutionary game theory: what matters for adaptation is relative payoff, not absolute gain. This essay reinterprets the Scodel–Minas experiments through a Darwinian lens, arguing that they provide an early empirical challenge to Nash-equilibrium reasoning and to models that evaluate strategies solely in terms of absolute utility. By reconstructing the H4 payoff structure and embedding it within a simple evolutionary framework, I show how small levels of “competitive” behavior can destabilize cooperative equilibria that appear self-evident under standard assumptions. I then revisit three later “puzzles” in the evolution of cooperation—altruistic punishment, the fragility of “win–win” treaties, and rejections in ultimatum bargaining—to ask how differently they might have been framed had the Scodel–Minas findings been part of the canonical experimental literature. Rather than treating these phenomena as surprising anomalies, a historically informed, relative-payoff perspective suggests that they could have been recognized much earlier as natural expressions of an already documented pattern. Full article

For interconnected multi-microgrids, it is crucial to improve operational economy and renewable energy utilization while ensuring system security. However, existing studies still face limitations in handling multi-time-scale uncertainties and enhancing the incentive for energy trading. Therefore, this paper proposes a cooperative distributed energy management strategy for interconnected microgrids based on model predictive control. First, a multi-time-scale framework is introduced into the multi-microgrid model, where rolling optimization and adaptive prediction/control horizons are used to cope with stochastic fluctuations of sources and loads. Then, a cooperative game model for the multi-microgrid coalition is formulated, and the asymmetric Nash bargaining problem is equivalently decomposed into a two-stage procedure of “coalition operation cost minimization–transaction bargaining”. Next, an algorithm for a distributed alternating-direction method of multipliers is employed for solution. Finally, multi-scenario simulations are carried out to compare three operation modes: independent operation, cooperation only, and model predictive control-based cooperation. The results show that compared with the independent operation mode, the total operation cost of the system is reduced by 22.8% using the proposed method and by 6.3% compared with the mode only adopting the cooperation mechanism, which demonstrates the effectiveness of the proposed strategy. The proposed strategy also enhances sustainability by improving local renewable energy accommodation, reducing reliance on upstream grid electricity, and supporting more resilient operation of interconnected microgrids under uncertainty. Full article

Global value chains (GVCs) have evolved into highly interconnected and geographically fragmented production networks, increasing exposure to systemic disruptions and revealing the limitations of static input–output and conventional network approaches. This study develops a unified analytical framework for modeling the structure, dynamics, and resilience of GVCs by integrating input–output economics with network theory, control theory, optimal transport, information theory, and cooperative game theory. The framework represents GVCs as time-varying, multi-level networks and formalizes shock propagation through stochastic normalization and state-space dynamics. Entropy-regularized optimal transport is employed to model friction-dependent substitution and supply chain reconfiguration, while Koopman operator methods approximate nonlinear adjustment dynamics. Cooperative flow-based indices are introduced to assess systemic importance and bargaining power. The analysis produces a coherent set of structural and dynamic indicators capturing vulnerability, adaptability, and controllability across country–sector nodes. Overall, the framework provides an empirically applicable toolkit for diagnosing structural fragilities, comparing resilience across economies, and supporting scenario-based evaluation of industrial and trade policies in complex global production networks. Full article

Shared energy storage provides a promising solution for the operation of microgrid clusters. This paper explores a hybrid electric–hydrogen shared energy storage model within microgrid clusters, aiming for clean energy generation and economical energy supply despite renewable energy’s unpredictability and complex stakeholder interactions. First, the proposed method features a shared energy storage operator that hosts electric storage and power-to-gas, enabling multi-microgrids energy sharing. To address market dynamics, a hybrid game theory approach using Nash bargaining and Stackelberg games is employed to manage interactions among the shared energy storage operator, microgrid operators, and internal end-users, while accounting for their differing interests. Second, to address uncertainty in renewable energy output, a distributionally robust optimization model is implemented with conditional value at risk, focusing on risk in extreme scenarios. The Adaptive Alternating Direction Method of Multipliers algorithm and Karush–Kuhn–Tucker conditions are used to solve the optimal decision scheme for each entity. Finally, a case study is used to verify the model’s effectiveness. Simulation results show that hybrid electric–hydrogen energy sharing improves resource utilization, leading to significant revenue increases for microgrids and higher profitability for shared energy storage operator. The game-theory-based approach ensures equitable revenue distribution and a 9.86% increase in coalition revenue. It provides a flexible approach to balance economic efficiency and system robustness by allowing decision-makers to adjust risk preference parameters and use historical sample data for informed decision-making. Full article

Wireless Body Area Network (WBAN) is an emerging medical health monitoring technology. However, WBANs encounter critical challenges in balancing reliability, energy efficiency, and Quality of Service (QoS) requirements for life-critical medical data. The design of its Medium Access Control (MAC) protocol has challenges since dynamic body-shadowing effects and heterogeneous traffic patterns. In this paper, we propose the Nash Bargaining Rate-optimization MAC (NBR-MAC), a hybrid MAC protocol that integrates TDMA-based Guaranteed Time Slots (GTS) with CSMA/CA-based contention access. Unlike traditional schemes, we model the rate allocation as an Asymmetric Nash Bargaining Game, introducing a rigorous disagreement point to guarantee minimum service for critical nodes. The utility function is normalized to resolve dimensional inconsistencies, incorporating sensor priority, buffer status, and channel quality. The Nash Bargaining solution is derived after proving convexity and verifying the axioms. Superframe time slots are allocated based on sensor data priority. Simulation results demonstrate that the proposed protocol enhances transmission success ratio and throughput while reducing packet age and energy consumption under different load conditions. Full article

This paper proposes a method based on interval linear robust optimization to address the potential impacts of multiple uncertainties on the operational security of Regional Integrated Energy Systems (RIESs). The model considers the uncertainty in user loads and renewable energy outputs and determines the value ranges of related parameters through statistical analysis to characterize the boundaries of these uncertainties. To transform the stochastic disturbances into a solvable problem, the model introduces energy balance constraints under the worst-case scenario, ensuring that the system remains feasible under extreme conditions. The research framework integrates Nash bargaining theory, demand response mechanisms, and tiered carbon trading policies, constructing a cooperative game model for RIESs to minimize the overall operation cost of the alliance while providing a reasonable revenue distribution scheme. This approach aims to achieve fairness and sustainability in regional cooperation. Simulation results show that the method can effectively reduce the collaborative operation cost and improve the fairness of revenue distribution. To address potential issues of information misreporting and dishonesty in real-world scenarios, the model introduces an adjustable fraud factor in the revenue distribution process to characterize the strategy deviations of participants. Even under potential fraud risks, the mechanism can maintain an optimal revenue structure and lead the participants toward a stable fraud equilibrium, thereby enhancing the robustness and reliability of the overall collaboration. Full article

With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial for achieving economical and low-carbon operation of IES. To this end, this paper unveils a comprehensive modeling and optimization framework: Firstly, a Spatio-Temporal Diffusion Model (STDM) is proposed, which generates high-quality wind power forecasting data by accurately capturing its spatio-temporal correlations, thereby providing reliable input for IES dispatch. Subsequently, a stochastic optimal scheduling model for electricity–heat–carbon coupled IES is established, comprehensively considering carbon capture equipment and a carbon quota mechanism. Finally, a multi-IES Nash bargaining cooperative game model is developed, encompassing bilateral energy trading and bilateral carbon trading, to equitably distribute cooperative benefits. Simulation results demonstrate that the STDM model significantly outperforms baseline models in both forecasting accuracy and scenario quality, while the designed bilateral market mechanism enhances system economics by reducing the total operating cost by 19.63% and lowering the total carbon emissions by 4.09%. Full article

To address the challenges of renewable energy curtailment under normal conditions and severe power outages under extreme scenarios, this paper proposes a hydrogen-integrated comprehensive energy system (H-IES) configuration method aimed at enhancing the resilience of distribution networks. The proposed method improves energy utilization efficiency while achieving a balance between economic performance and resilience. First, an operational model of the H-IES is established considering the operating characteristics of distribution networks under extreme conditions. On this basis, a Nash bargaining-based equilibrium model is developed, where economic performance and resilience act as game participants negotiating toward equilibrium. By applying the particle swarm optimization algorithm, the Nash equilibrium solution is obtained, realizing a Pareto-optimal trade-off between the two objectives. Finally, case studies demonstrate that the proposed configuration improves the resilience index by 3.13% and reduces total cost by 10.86% compared with mobile battery energy storage. Under the Nash bargaining framework, the equilibrium configuration increases renewable energy utilization and provides up to 21.6% higher resilience compared with an economy-only optimization scheme. Full article

Green co-creation plays a vital role in promoting sustainability by engaging both firms and consumers in value creation, yet most studies examine competition and cooperation separately without considering their interplay. This study investigates the dynamics of coopetition in green co-creation by developing a two-stage biform game that integrates competitive interaction and cooperative bargaining within a unified framework. The results show that (1) greater green co-creation efforts, representing deeper firm–customer interactions, improve both parties’ equilibrium outcomes; (2) cooperation leads to greater green effort investment than pure competition; and (3) when Nash bargaining conditions are satisfied, coopetition improves both individual profits and total welfare compared with sole competition. These findings highlight that coopetition not only strengthens mutual economic benefits, but also enhances sustainability performance by balancing competitive and cooperative forces. This study provides an analytical foundation for understanding firm–customer coopetition and offers actionable insights for advancing sustainable value creation in green supply chain management. Full article

The unequal status between manufacturers and live-streamers often undermines supply chain profitability and social welfare. However, the “volume guarantee” commission mode, designed to mitigate this issue, has proven ineffective in practice. This paper adopts a Nash bargaining fairness framework to analyze this paradox, incorporating two defining features of live-streaming commerce: the social network effect and the streamer’s cost of purchasing public domain traffic. We develop a dynamic game model involving the platform, manufacturer, streamer, and consumers to examine commission mode selection and supply chain decision-making. Our analysis yields four key findings: (1) Under Nash bargaining fairness, the “volume guarantee” mode is invariably redundant, regardless of who sets the sales threshold. Bargaining power only influences profit distribution via commission rates without distorting optimal product pricing or traffic acquisition decisions. (2) The social network effect boosts product prices, traffic purchases, total profit, and social welfare, with its impact amplified by the streamer’s fanbase size. Thus, collaborating with top-streamers is advantageous for manufacturers. (3) While higher platform traffic costs do not affect the optimal product price, they reduce traffic purchase volume, thereby decreasing supply chain profits and social welfare. (4) To enhance social welfare, platforms can implement differentiated traffic pricing, offering discounts to top-streamers. This study provides critical managerial insights for designing fair contracts and fostering equitable cooperation in live-streaming ecosystems. Full article

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as inputs to a Multilayer Perceptron, achieving 96.67% accuracy for overall stress detection, and 95.93% and 94.44% for water and nitrogen stress identification, respectively. A Random Forest model, using only the green band, achieved 92.22% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using a Scale-Invariant Feature Transform descriptor (SIFT) achieved 93.33% for water stress, while RF with RGB bands and canopy cover reached 85.56% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency. Full article