Search Results (92)

Many bargaining problems allocate control over heterogeneous issues rather than a single scalar surplus. This paper studies a two-player game-theoretic Nash bargaining problem over a continuum of issues with stochastic valuation fields. Taking the classical maximum Nash welfare cutoff allocation as a benchmark, we characterize the selected agreement as an endogenous excursion set of the log-relative valuation field. We then study its economic geometry: stationarity and ergodicity yield deterministic many-issue payoff limits, a finite-domain perturbation formula separates local shocks from global cutoff feedback, Kac–Rice methods describe boundary intensity, and a perimeter penalty for fragmented contracts turns the bargain into a finite perimeter variational problem. At regular boundary points, the complexity-penalized bargain satisfies a curvature-adjusted bargaining condition. The analysis connects cooperative game theory, Nash bargaining, fair division, random field geometry, and contract complexity. Full article

Grain storage is vital for a country within a framework of food sovereignty and security. It helps stabilize markets, prices, and imbalances between supply and demand. In Mexico, public storage infrastructure is almost nonexistent, having been transferred to the private sector. The objective of this article is to analyze the relationship between public storage infrastructure and distribution problems that maize producers face in Mexico. A mixed-methods analysis procedure was implemented. Semi-structured interviews were conducted with small, medium, and large distributors, selected using the snowball sampling technique. The analysis identifies a positive association between references to storage infrastructure and distribution problems in the interview materials. Additionally, Spearman’s rank correlation coefficient was applied to the counts to strengthen the analysis. The results indicated a positive and significant relationship between the variables “storage infrastructure” and “distribution problems”, but also that, around the latter, there are others: lack of government support, price fixing, guaranteed price, insecurity, production costs, and inconveniences that require attention to stabilize the maize market. Inadequate infrastructure limits storage capacity, affects grain quality, increases costs, reduces producers’ bargaining power, and contributes to price volatility. It also impacts logistics, transportation, and marketing, especially in less developed regions. Evidence suggests that public storage infrastructure is a strategic element for food security; however, its concentration and predominantly private nature generate territorial inequalities. Full article

Agricultural commodity markets remain central to household survival across many developing economies, yet their volatility is rarely framed as an adolescent sexual and reproductive health problem. This mini review uses a structured narrative approach anchored in a screened evidence map of 1065 records, from which 50 papers were retained and 16 studies were prioritized for full synthesis. We define adolescent reproductive health holistically, including sexual agency, contraceptive information and use, pregnancy intention, antenatal and obstetric care, protection from coercion, and maternal and neonatal outcomes. The review provides a concrete answer to the primary question: agricultural commodity price volatility is a distal, context-conditioned determinant of adolescent reproductive health, not a uniform direct cause. Its effects operate mainly through food security, household income, labor allocation, school continuity, gendered bargaining power, and service access. Negative shocks more consistently erode nutrition, schooling, transport to care, and access to adolescent-friendly services, especially among rural girls in households with weak shock buffers. Positive shocks may increase births or union formation when income effects dominate, but they may also harm health when higher labor demand raises the opportunity cost of caregiving and service use. Direct adolescent-specific causal evidence remains limited; therefore, adjacent evidence on fertility, child health, schooling, and maternal or neonatal outcomes is interpreted through an explicit evidence hierarchy rather than treated as equivalent to direct adolescent evidence. Policy priorities include shock-responsive social protection, school retention, contraceptive supply continuity, adolescent-friendly care, and early warning systems that trigger health and education responses during commodity instability. Full article

The sustainable collaborative operation of multi-park integrated energy systems (MPIESs) with shared energy storage (SES) provides a significant pathway for low-carbon transition, renewable energy utilization, and energy efficiency improvement, thereby supporting regional energy sustainability. However, realizing this potential faces challenges, including source-load uncertainty, conflicts of interest among multiple entities, and the need for privacy-preserving distributed coordination. To address these issues, this paper proposes a distributed robust energy management strategy for MPIESs with SES, which is decomposed into two sub-problems. In the first sub-problem, a robust optimization model incorporating the SES leasing mechanism is established to handle the uncertainties of photovoltaic (PV) generation and loads. In the second sub-problem, a cooperative game model based on Nash bargaining theory is constructed to fairly allocate the cooperative surplus among participating parks. The alternating direction method of multipliers (ADMM) is employed to solve the overall model in a distributed manner, and enabling collaborative scheduling with limited information exchange. Case studies indicate that the proposed strategy reduces the total system operating cost by 17.57% compared to the independent operation mode. The benefit allocation mechanism achieves Pareto improvement and effectively mitigates the uneven distribution of cooperative surplus among parks. Furthermore, the distributed algorithm converges within 13 iterations in the test case, demonstrating good computational tractability. Consequently, the results verify the effectiveness of the proposed framework in balancing economy, fairness, and robustness, thereby promoting the low-carbon and sustainable operation of regional integrated energy systems. Full article

With the increasing integration of transportation and energy systems, highway energy replenishment facilities are gradually evolving into hybrid refueling stations that integrate photovoltaic generation, energy storage, battery charging, and hydrogen refueling. However, due to differences in resource conditions across stations, independently operated hybrid refueling stations find it difficult to simultaneously improve overall economic performance and renewable energy utilization. To address this issue, this paper investigates the coordinated operation and distributed optimization of highway hybrid refueling stations. First, an inter-station hydrogen–carbon–green certificate trading framework is established, and a trading model for a cluster of hybrid refueling stations is then developed on this basis. Then, the inter-station trading problem is decomposed into two subproblems: symmetric trading volume determination and asymmetric Nash bargaining-based price determination. These two subproblems are solved in a distributed manner using the alternating direction method of multipliers. In addition, a hydrogen transportation model is developed to translate trading decisions into feasible transportation arrangements under highway network and hydrogen tube trailer scheduling constraints. Finally, the case study demonstrates that the proposed model enables multi-resource sharing among hybrid refueling stations, reduces the overall system cost by 21.30%, and achieves a fairer distribution of benefits among stations. Full article

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

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

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

Free trading of distributed energy resources (DERs) is an effective way to enhance local renewable consumption and user-side economic efficiency. Yet unrestricted sharing may threaten operational security. To address this, this paper proposes a voltage-constrained, differentiated resource-sharing framework for active distribution networks (ADNs). The framework maximizes users’ economic benefits and renewable absorption while keeping system voltages within safe limits. A local energy market with prosumers and the distribution network operator (DNO) is established. Prosumers optimize trading decisions considering transaction costs, wheeling charges, and operational costs. Based on this, a generalized Nash bargaining model is developed with two sub-problems: cost optimization under voltage constraints and payment negotiation. The DNO verifies prosumer decisions to ensure system constraints are satisfied. This paper quantifies prosumer heterogeneity by integrating market participation and voltage regulation contributions, and proposes a differentiated bargaining model to improve fairness and efficiency in DER trading. Finally, an ADMM-based distributed algorithm achieves market clearing under AC power flow constraints. Case studies on modified IEEE 33-bus and 123-bus systems validate the method’s effectiveness, the allocation of benefits between producers and consumers is more equitable, and the costs for highly engaged producers and consumers can be reduced by 46.75%. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article

With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

As global energy systems shift to low-carbon models, microgrid systems play an increasingly vital role in decentralized energy management. This study proposes a collaborative scheduling strategy, incorporating both power and carbon contribution for multi-microgrid systems. Through the utilization of a cooperative Stackelberg game and a Nash bargaining model, a bi-level game framework is established between grid operators and microgrid alliances, enabling efficient resource sharing and equitable benefit distribution. To accurately assess each microgrid’s impacts, a VCG (Vickrey–Clarke–Groves)-based mechanism is introduced to quantify its marginal contribution to both power supply and carbon mitigation. The contribution factors are then embedded into the bargaining process, guiding incentive-compatible allocation. Furthermore, to improve computational efficiency and enable distributed problem-solving, an enhanced analytical target cascading (ATC) algorithm is applied. Experimental results reveal that this approach improves both economic and environmental performance, effectively reducing carbon emissions and dependence on the main grid. Full article