Search Results (95)

This paper applies quantum game theory to three ethical dilemmas that frequently arise in negotiation: cooperation versus competition, self-interest versus equity, and honesty versus deception. Using quantum extensions of selected games such as the Prisoner’s Dilemma, the Ultimatum Game, the Battle of the Sexes, and the Buyer–Seller Game, we examine whether quantization can generate equilibria that improve classical outcomes while also aligning more closely with ethical principles such as fairness, cooperation, and honesty. The analysis shows that quantum strategies, through entanglement and superposition, can sustain cooperative, fair, or honest behaviour as stable equilibria, outcomes that are typically unstable or unattainable in classical settings. The specific outcomes depend on the chosen quantization method, but across cases, the analysis consistently shows that quantum formulations expand the range of solutions in which efficiency and ethical principles co-exist. 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

Organizations increasingly rely on Agile software development to navigate the complexities of digital transformation. Agile emphasizes flexibility, empowerment, and emergent design, yet large-scale initiatives often extend beyond single teams to include multiple subsidiaries, business units, and regulatory stakeholders. In such contexts, team-level mechanisms such as retrospectives, backlog refinement, and planning events may prove insufficient to achieve alignment across diverse perspectives, organizational boundaries, and compliance requirements. To address this limitation, this paper introduces a complementary framework and a supporting software tool that enable systematic cross-stakeholder alignment. Rather than replacing Agile practices, the framework enhances them by capturing heterogeneous stakeholder views, surfacing tacit knowledge, and systematically reconciling differences into a shared alignment artifact. The methodology combines individual Functional Resonance Analysis Method (FRAM)-based process modeling, iterative harmonization, and an evidence-supported selection mechanism driven by quantifiable performance indicators, all operationalized through a prototype tool. The approach was evaluated in a real industrial case study within the regulated gaming sector, involving practitioners from both a parent company and a subsidiary. The results show that the methodology effectively revealed misalignments among stakeholders’ respective views of the development process, supported structured negotiation to reconcile these differences, and produced a consolidated process model that improved transparency and alignment across organizational boundaries. The study demonstrates the practical viability of the methodology and its value as a complementary mechanism that strengthens Agile ways of working in complex, multi-stakeholder environments. Full article

Wisdom in negotiation is increasingly vital in managing conflicts, yet its behavioural expression remains underexplored. This study explores the behavioural signatures of individuals nominated as wise negotiators within an organisational context. There were 48 participants recruited as wise negotiators from a larger pool of 313 participants. There were three manipulations used: archetypes (personality), reciprocity style, and emotionality, resulting in a 4X3X2 design (24 conditions). Participants were also asked to fill out various wisdom related questionnaires. Each participant had to go through 24 conditions separately before playing an investment game each time. For the analysis purpose, three-way repeated ANOVA and three-way repeated ANCOVA were used. The results revealed that there was a difference in how wise negotiators negotiate differently with different archetypes (p < 0.01), reciprocity (p < 0.01) and emotional situations (p < 0.01). Additionally, there were also interaction effects of archetypes, reciprocity and emotional situations (p < 0.05). Notably, when wisdom variables were statistically controlled, these differences became nonsignificant. A supplementary 2 × 2 design explored gender interactions, showing that outcomes differed by opponents’ gender but not by the gender of the wise negotiator. This finding highlights the role of wisdom traits in strategic negotiation and has implications for training and selection in a high-stakes negotiation context. Full article

Classical game theory provides powerful tools for modeling strategic interaction, but often overlooks the social, cultural, and institutional dimensions of human behavior. To address this gap, Tom Burns and collaborators developed generalized game theory (GGT) and later sociological game theory (SGT). These frameworks extend classical game theory by embedding rules, norms, values, beliefs, roles, and institutional structures into formal models of interaction. This review synthesizes thirty key contributions to this research program, organizing the literature into eight thematic areas and providing an integrated overview of the field. The originality of this work lies in its comprehensive approach, which advances conceptual and formal foundations while exploring practical applications and outlining directions for future research. GGT/SGT develops rule-based modeling, the analysis of norms and values, multiple modalities of action determination, and various equilibrium types, offering a rigorous framework for understanding strategic behavior in complex social contexts. In application, these approaches provide insights into organizational processes, negotiation, legitimacy, distributive justice, and institutionalized procedures, while integrating interactionist and group-theoretical perspectives. By linking formal modeling with normative and institutional analysis, GGT/SGT offers innovative socio-economic decision models that capture uncertainty, fairness, legitimacy, and institutional transformation. It extends classical game theory by bridging mathematics, economics, and sociology, providing a versatile theoretical tool for understanding complex socio-economic systems and improving strategic decision-making in contemporary society. Full article

A growing international consensus holds that science education must advance beyond content coverage to cultivate robust understanding of the Nature of Science (NoS)—how scientific knowledge is generated, justified, revised, and socially negotiated. Yet naïve conceptions persist among students and teachers, and effective, scalable classroom strategies remain contested. This narrative review synthesizes research and practice on games and playful activities that make epistemic features of science visible and discussable. We organize the repertoire into six families—(i) observation–inference and discrepant-event tasks; (ii) pattern discovery and rule-finding puzzles; (iii) black-box and model-based inquiry; (iv) activities that dramatize tentativeness and anomaly management; (v) deliberately underdetermined mysteries that cultivate warrant-based explanations; and (vi) moderately contextualized games. Across these designs, we analyze how specific mechanics afford core NoS dimensions (e.g., observation vs. inference, creativity, plurality of methods, theory-ladenness and subjectivity, tentativeness) and what scaffolds transform playful engagement into explicit, reflective learning. We conclude with pragmatic guidance for teacher education and curriculum design, highlighting the importance of language supports, structured debriefs, and calibrated contextualization, and outline priorities for future research on equity, assessment, and digital extensions. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

While negotiating agents have achieved remarkable success, one critical challenge that remains unresolved is the inherent inefficiency of learning negotiation strategies from scratch when encountering previously unencountered opponents. To address this limitation, Transfer Learning (TL) emerges as a promising solution, leveraging knowledge acquired from prior tasks to accelerate learning and enhance adaptability in new negotiation contexts. This study introduces Transfer Learning-based Negotiating Agent (TLNAgent), a novel framework enabling autonomous negotiating agents to systematically leverage knowledge from pretrained source policies. The proposed transfer mechanism not only enhances negotiation performance but also substantially accelerates policy adaptation in unfamiliar negotiation environments. TLNAgent integrates three core components: (1) a negotiation module that interacts with opponents; (2) a critic module that determines whether to activate the transfer process and selects which source policies to transfer; and (3) a transfer module that facilitates knowledge integration between source and target policies. Specifically, the negotiation module interacts with opponents during the negotiation to execute core decision-making processes; in addition, it trains new policies using reinforcement learning. The critic module serves dual critical functions: (1) it dynamically triggers the transfer module according to interaction analysis; and (2) it selects the source policies via its adaptation model. The transfer module establishes lateral parameter-level connections between source and target policy networks, facilitating systematic knowledge transfer while ensuring training stability. Empirical findings from our extensive experiments indicate that transfer learning considerably enhances both the efficiency and utility of outcomes in cross-domain negotiation tasks. The proposed framework attains superior performance when compared to the state-of-the-art negotiating agents from the Automated Negotiating Agents Competition (ANAC). Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

Driven by the goal of “double carbon”, we propose an investment proportion optimization method based on cooperative game theory to optimize the investment of multiple entities and evaluate the effectiveness of the new power system. The asymmetric Nash negotiation model is introduced to balance the interests of each investment entity. At the same time, a comprehensive investment benefit evaluation index system covering economic, environmental, and social benefits is constructed, and the overall investment benefit evaluation is obtained by using the Delphi method, analytic hierarchy process, and fuzzy comprehensive evaluation method. Through the case analysis of the multi-energy complementary energy system project investment, the validity of the multi-subject investment proportion optimization model and the investment benefit analysis model are verified, and the feasibility of the project investment is demonstrated to provide theoretical guidance and practical reference for the research in related fields. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

In the context of global e-commerce platform supply chains dominated by Alibaba and Amazon, power reconfiguration among tripartite stakeholders (platforms, manufacturers, and retailers) remains a critical yet underexplored issue in supply chain contract design. To analyze the strategic interactions between platforms, manufacturers, and retailers, as well as how platforms select the contract format within a tripartite supply chain, this study proposes a Stackelberg game-theoretic framework incorporating participation constraints to compare fixed-fee and revenue-sharing contracts. The results demonstrate that revenue-sharing contracts significantly enhance supply chain efficiency by aligning incentives across members, leading to improved pricing and sales outcomes. However, this coordination benefit comes with reduced platform dominance, as revenue-sharing inherently redistributes power toward upstream and downstream partners. The analysis reveals a nuanced contract selection framework: given the revenue sharing rate, as the additional value increases, the optimal contract shifts from the mode RR to the mode RF, and ultimately to the mode FF. Notably, manufacturers and retailers exhibit a consistent preference for revenue-sharing contracts due to their favorable profit alignment properties, regardless of the platform’s value proposition. These findings may contribute to platform operations theory by (1) proposing a dynamic participation framework for contract analysis, (2) exploring value-based thresholds for contract transitions, and (3) examining the power-balancing effects of alternative contract formats. This study offers actionable insights for platform operators seeking to balance control and cooperation in their supply chain relationships, while providing manufacturers and retailers with strategic guidance for contract negotiations in platform-mediated markets. These findings are especially relevant for large e-commerce platforms and their partners managing the complexities of contemporary digital supply chains. Full article

This paper analyses how strategic interactions between actors influence the development of circular economy (CE) initiatives in food systems. Using a case study from Saint-Hyacinthe, a mid-sized and agri-food technopole in Québec (Canada), we investigate how cooperation, competition, and power asymmetries shape CE adoption across the supply chain. Drawing on game theory and a typology of management dynamics, the study identifies four patterns: negotiated management, constrained leadership, hierarchical relationships, and competitive behaviour. Empirical data were collected through two collaborative workshops involving public, private, and community-based actors, resulting in 244 coded entries across 12 boards. These allowed us to assess actors’ interests, attitudes, and capacities in relation to CE strategies at upstream, midstream, and downstream stages. The results show that strategies aligned with dominant interests and existing capacities are more likely to be supported, while those requiring structural change are tolerated or marginalized. Findings highlight the role of incentive mechanisms, institutional flexibility, and coordination in enabling more transformative circular initiatives. By adopting a stage-sensitive perspective, this study also fills a gap in the literature by examining how actor dynamics differ across upstream, midstream, and downstream segments of the food system, contributing to CE research by applying game theory to actor configurations and interaction dynamics in food systems. It calls for further exploration of interdependencies and contextual conditions that either facilitate or hinder the emergence of effective, inclusive, and systemic CE transitions. Full article