Game Theory Research Articles

ABSTRACT Despite repeated EU initiatives to build an integrated defence market, fragmentation continues to weaken the European Defence Technological and Industrial Base (EDTIB), threatening its competitiveness and strategic autonomy. This paper develops a stochastic evolutionary game-theoretic model treating Member States as two subpopulations choosing between national and European procurement conventions, analyzing persistence conditions and transition dynamics. Analytical results show that a consolidated market becomes stochastically stable only when efficiency gains are sufficiently large and their distribution sufficiently balanced. Unequal payoffs or population sizes increase resistance to institutional change, even when consolidation would improve aggregate welfare. Incorporating collective action shows how coordinated strategic behavior can trigger transitions, even when individual incentives are feeble. The results imply that efficiency alone is insufficient: effective EU industrial policy must couple market integration with compensation mechanisms to reduce resistance. These findings explain the unremitting EDTIB fragmentation and identify practical levers – as targeted subsidies, phased integration and institutional design – for accelerating consolidation.

Due to differences in physiological characteristics and drug metabolism between children and adults, drug efficacy evaluation and safety monitoring in pediatric drug development present significant challenges. This paper proposes a data-driven incentive mechanism for pediatric drug development based on medical imaging data. This approach optimizes drug market pricing through precise imaging data, promoting accessibility and R&D efficiency for pediatric drugs. This study first collects multi-source computed tomography (CT), magnetic resonance imaging (MRI), and X-ray data, focusing on images of common pediatric diseases. After data preprocessing, a convolutional neural network (CNN) is used for feature extraction to extract key image information. Image difference methods and a U-Net image segmentation network are then used to evaluate drug efficacy and safety, quantify efficacy changes, and analyze side effects. Next, a drug efficacy-safety evaluation model is developed, and game theory is employed to design a R&D incentive mechanism. Monte Carlo simulation is combined with risk assessment to comprehensively consider factors such as cost, R&D investment, and market demand during the pricing optimization phase. A dynamic pricing strategy is implemented to ensure both economic benefits and social accessibility of the drug. Experiments have shown that the drug has a good development effect, with an average tumor volume reduction of 32.7% (95% CI: 28.4%-36.9%). The drug's impact on organ volume is within ± 2cm³, and the market pricing strategy selects a relatively optimal price point.

As intelligent transportation systems continue to advance, on-board surveillance video has become essential for train safety and intelligent scheduling. However, high-resolution video transmission faces bandwidth limitations, and existing deep learning-based super-resolution models find it difficult to meet real-time requirements due to high computational complexity. To address this, this paper proposes an “edge–end” collaborative multi-terminal task inference framework, which improves inference speed by integrating resources of in-vehicle end devices and edge servers. The framework establishes a real-time-priority mathematical model, uses game theory to solve the problem of minimizing multi-terminal task inference latency, and proposes a multi-terminal task model partitioning strategy and an adaptive adjustment mechanism. It can dynamically partition the model according to device performance and network status, prioritizing real-time performance and minimizing the maximum inference delay. Experimental results show that the dynamic model partitioning mechanism can adaptively determine the optimal partition point, effectively reducing the inference delay of each end device in high-speed mobile and bandwidth-constrained scenarios and providing high-quality video data support for safety monitoring and intelligent analysis.

Blockchain technology relies on cryptographic mechanisms for transaction security and data integrity. However, the growing computational complexity, high transaction costs, and scalability issues pose significant challenges to blockchain adoption. Traditional cryptographic methods—such as hashing, key generation, encryption, and decryption—introduce excessive computational overhead, leading to energy inefficiencies and increased latency. This research proposes an optimization-driven crypto analysis framework that integrates metaheuristic algorithms, combinatorial optimization, reinforcement learning, and game theory to enhance the efficiency and security of blockchain cryptographic processes. The framework focuses on optimized cryptographic computation, gas fee reduction in smart contracts, security enhancement against cryptanalysis, and improved scalability of consensus mechanisms. Experimental evaluations demonstrate up to 39.4\% reduction in cryptographic execution time, 29.4\% savings in smart contract gas fees, and 33.3\% improvement in decentralization of Proof-of-Stake validators. These results validate the effectiveness of the proposed framework in achieving secure, scalable, and cost-efficient blockchain operations.

Objective This study utilizes evolutionary game theory to analyze the collaborative evolutionary mechanisms among governments, international organizations, healthcare institutions, and enterprises in natural disaster emergency response, aiming to explore how public policy can optimize the behavior of each stakeholder. Methods A four-party evolutionary game model was constructed to examine strategy interactions and cooperative mechanisms among all parties. Numerical simulations were conducted to verify how key parameters affect the evolutionary outcomes. Results The results indicate that government regulatory intensity, intervention strategies of international organizations, the philanthropic orientation of healthcare institutions, and the sense of corporate social responsibility among enterprises significantly influence the efficiency of emergency response. Numerical simulations further show that increasing government penalties, reducing international organizations’ dependency losses, improving the resource utilization efficiency of healthcare institutions, and raising both the cost of non-compliance and the market trust benefits for enterprises can encourage stakeholders to adopt more cooperative strategies that serve the public interest. Conclusion This study reveals the “double-edged sword effect” of government regulation, the “time window effect” of international organizational intervention, the “multiplier effect” of resource efficiency in healthcare institutions, and the “trust-benefit mechanism” of corporate social responsibility, offering new insights for optimizing public policy.

Abstract In the marine aquaculture industry, weather index insurance provides ideal market conditions for risk diversification and loss compensation. Government subsidies are essential for increasing insurance uptake and protection levels in fisheries insurance. However, the current mariculture insurance market lacks completeness in premium subsidy responsibilities and has unreasonable proportions for subsidy allocation. To address these issues, this study focuses on quantifying the subsidy scale of marine aquaculture weather index insurance and develops a three-party evolutionary game model involving simulations to determine the appropriate scales for various subsidy methods. The results highlight that a reasonable subsidy proportion is crucial for balancing stakeholder interests and maximizing social welfare. The modest subsidy levels are 85%–90% for premium subsidies, 55%–60% for reinsurance, and 45%–100% for operating expense subsidies. This study concludes with practical policy recommendations.

Purpose This paper aims to provide product design recommendations for electric vehicle supply chains (EVSCs) to improve market penetration in cold regions amidst growing market competition. Design/methodology/approach Based on two green product design approaches, we propose two electric vehicle (EV) designs with distinct cost structures: power battery expansion (Scheme N) and cold-resistance R&D (Scheme R). From a product design perspective, we consider the characteristics of the EVSC and use game theory to develop a dual oligopoly supply chain (SC) model under joint competition in price and range. By introducing range as a decision variable, we comprehensively analyze the effects of range anxiety, low temperature coefficients, production and R&D capabilities and competitive intensities on optimal solutions for firms. Findings The findings of the study are as follows: (1) the applicability and advantages of Scheme R increase as the gap between joint competition intensities narrows. High levels of range competition are consistently detrimental to the EVSC, as they hinder improvements in EV range and may reduce profits when consumer range anxiety is mitigated. (2) R&D capability significantly influences the choice of the optimal scheme. Scheme N should be selected only when R&D capabilities are limited and optimal cold-resistance performance is low. Unexpectedly, in colder temperatures with higher consumer anxiety, consumers tend to prefer purchasing EVs under Scheme N. Originality/value This study extends the green SC research by incorporating the specific needs of EVSCs and exploring the competitive dynamics of products with different structural approaches within the same market.

ABSTRACT Achieving high levels of sustainability in the energy sector is central to the UN’s goals. A key strategy for reaching this objective is the integration of sustainable practices within energy supply chains. However, existing research remains fragmented. To address this gap, we conducted a Bibliometric-Systematic Literature Review (B-SLR) of 772 articles (2004–2025) from Scopus, using VOSviewer to map the intellectual landscape of sustainable energy supply chains. Results reveal the need to integrate advanced decision-making models (AI, game theory), deepen studies on circular economy interactions, and explore blockchain’s potential for enhancing transparency, traceability, and optimisation of energy supply chains.

Assessing the accessibility of urban metro stations is essential for optimizing metro system planning and improving travel efficiency for residents. This study proposes an innovative evaluation framework—the CWM-GRA-TOPSIS model—for comprehensive metro station accessibility assessment. First, a multi-dimensional indicator system is established, encompassing three key dimensions, to-metro accessibility, by-metro accessibility, and land use accessibility, which are further refined into 14 secondary indicators for detailed analysis. A Combination Weighting Method (CWM) is then introduced, integrating the Analytic Hierarchy Process (AHP) for subjective weighting and the Criteria Importance Through Intercriteria Correlation (CRITIC) method for objective weighting, with their integration optimized through Game Theory. Subsequently, the overall accessibility of metro stations is evaluated and ranked using a hybrid Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The proposed method is applied to Wuhan, China, to demonstrate its effectiveness and applicability. Results show that the CWM-GRA-TOPSIS model, by balancing objectivity and practical relevance, provides a more reliable and systematic approach for identifying accessibility disparities and formulating targeted improvement strategies for urban metro systems.

China’s rapid population ageing collides with a legacy of mid-rise walk-ups built without elevators, creating a persistent mobility barrier and a classic coordination problem within buildings. Using evolutionary game theory (EGT) this thematic review synthesizes evidence on how incentives, rules and learning dynamics shape cooperation over time and advances a proposed cooperative evolutionarily stable strategy (ESS) for elevator retrofitting. This thematic review examines how incentives, rules and learning dynamics shape cooperation over time through reviewing international and Chinese databases (2015–2025), screened studies using pre-specified inclusion/exclusion criteria and extracted variables on players, strategies, payoff construction and policy instruments as findings were synthesized via thematic analysis aligned to EGT constructs (players, strategies, payoff matrices, replicator dynamics and evolutionarily stable strategies). Four themes emerged including (i) asymmetric payoffs by floor position and negative externalities facing lower floors bias outcomes toward stalemate, (ii) calibrated government intervention including subsidy thresholds, majoritarian voting rules and transparent compensation reconfigures payoffs and enables coalition building, (iii) governance and implementation matter as empowered neighborhood committees, staged procedures and pay-per-use financing reduce transaction costs and household risk, (iv) diffusion effects are clear as visible completions raise perceived benefits and lower perceived risks in adjacent estates which accelerates adoption. The reviewed evidence identifies a gap in the monetary valuation of externalities, city-comparable compensation formulas and post-installation governance indicating the need for multi-stage models that extend beyond the vote. Based on identified finding’s, recommendations for future policies include bundling instruments rather than rely on a single lever, legislate lifecycle obligations (sinking funds tied to title), publish standard, floor-indexed cost-sharing and compensation schedules grounded in observable impacts, resource community intermediaries to mediate and verify and seed high-visibility demonstration projects to harness social learning making cooperation both attractive and durable.

ABSTRACT This paper extends the framework of classical eco‐evolutionary game theory by establishing an asymmetric two‐community resource coupling model. By defining distinct community types, we simulate the asymmetry in environmental resource management and consumption across communities. Under the assumption that only one community is responsible for environmental resources, we theoretically and numerically analyze the equilibria and their stability. The model exhibits rich dynamic behaviors, including Hopf bifurcations and a heteroclinic network composed of six heteroclinic cycles within the system. To prevent resource collapse, we derive the maximum resource consumption threshold for the irresponsible community. The results show that the conditions for system stability in single‐community models no longer apply in multicommunity systems, and excessive cross‐community interactions may cause systemic risks. This work extends existing research and provides a new theoretical perspective for understanding the asymmetric evolution of multicommunity resource coupling.

This study is devoted to the analysis of regional collaboration in emergency supply chain reserves. To address this critical research issue, we have developed an AI-assisted tripartite evolutionary game model involving governments, manufacturers, and suppliers across different regions under demand uncertainties and resource disparities. In this study, we employ replicator dynamic equations to derive strategic evolution paths and utilize numerical simulations enhanced by AI-powered global sensitivity analysis for subsequent parameter sensitivity analysis, enabling a systematic examination of equilibrium conditions and stability strategies. Our research findings demonstrate that when government incentive mechanisms provide greater benefits than speculative gains then supply chain enterprises evolve toward collaborative strategies, with the system achieving optimal stability at the equilibrium where collaboration benefits outweigh costs. Our AI-enhanced analysis results also reveal that while higher subsidies accelerate enterprise participation, they may reduce government motivation, necessitating carefully balanced penalty scales to sustain long-term cooperation—findings validated through sensitivity analyses of key parameters. The study’s integration of game theory with AI techniques offers both theoretical innovation in multi-agent decision modeling and practical value for strengthening national emergency management frameworks.

Redesigning deep neural networks: Bridging game theory and statistical physics.

Exploring the synergistic effects of electricity‑carbon policies: A new perspective on power supply and demand based on game theory

ID-HFL: Incentive-Driven Heterogeneous Federated Learning Based on Game Theory and Differential Privacy

Multi-factor vaccination game and optimal control of a SVIS epidemic model.

ABSTRACT Food security and the sustainability of ecosystems are seriously threatened by land degradation, especially in the Indo‐Gangetic Plains where soil erosion, salinization, and waterlogging are the main causes. This study integrated these three forms into a unified, versatile, and globally adaptable Land Degradation Index (LDI) for comprehensive land degradation assessment, using Sultanpur district, Uttar Pradesh, India as a case study. Soil erosion susceptibility was initially assessed using the Revised Universal Soil Loss Equation, whilst salinization and waterlogging were independently evaluated with a Frequency Ratio using 25 and 21 factors, respectively. The resulting susceptibility maps demonstrated high predictive accuracies, with Area Under the Curve (AUC) values of 97.3%, 94.5%, and 90.1%, respectively. LDI was subsequently calculated using these maps as inputs to integrate the three degradation processes into a unified index representing overall land degradation intensity. It identified and mapped the most severely affected areas, revealing that approximately 20% of the agricultural land in the study area was impacted by land degradation. This study also developed novel random forest regression models integrated with SHapley Additive exPlanations (SHAP)‐based Game theory to examine the behaviour of conditioning factors in high and low susceptibility zones of Sultanpur district. The models fitted with MSE < 0.0046, RMSE < 0.07, MAE < 0.04, RMSLE < 0.05, and MRE < 0.04 and R 2 > 0.86. Soil salinization was found to be the primary driver of soil fertility loss in this study. This salinization is primarily driven by vegetation loss, changes in soil pH, overuse of nitrogen‐rich fertilisers, and proximity to canals. Identifying the key drivers of land degradation and understanding their localised impacts provides vital insights for promoting sustainable agriculture and guiding evidence‐based policymaking. These findings further highlight the broader global relevance of adopting site‐specific land management strategies, particularly through vegetation restoration, balanced fertiliser use, and efficient irrigation, to sustain the productivity and resilience of agro‐ecosystems like Sultanpur district.

Using Reinforcement Learning and Game Theory for Determining Cooperative Nodes in Multi-hop Wireless Networks

An adaptive strategy quantum particle swarm optimization method based on intuitionistic fuzzy entropy and evolutionary game theory

Critics of President Trump have alleged that he has reduced independent agencies to mere extensions of the executive branch during his second term. The reality is that Democratic and Republican presidents routinely leverage the opportunity to reshape independent agencies in openly partisan ways because presidents have majority control of the appointments for the leadership of virtu ally all agencies. I examine a large data set of independent agency votes from the Obama and first Trump terms to show that independent agency commissioners vote in predictably partisan ways when addressing substantive policy changes. The partisan design of independent agencies undercuts agency claims to “independence.” As a result, substantive policy changes at independent agencies generally become transient victories that are likely to be reversed by the next administration from the opposing party. I make the case for restoring a degree of independence to independent agencies by institutionalizing “constructive gridlock” in their leadership. I call for creating an even split in independent agency commissioners from the two major political parties. Gridlock between the two major political parties is almost uniformly panned as a problem plaguing our legislative process. Our current Congress epitomizes the potential dysfunctionality of legislative gridlock with divided government and high levels of partisanship leading to a dearth of statutes and frustration at inaction. But I argue that creating partisan balance in the leadership of independent agencies would have positive policy effects and legitimize decisions made by unelected leaders. Institutionalizing an even partisan division in the leadership of independent agencies would necessitate bipartisanship for agency action and pressure elected leaders in Congress to act when independent agency leaders cannot overcome their ideological differences. This approach would further an underlying purpose of independent agencies for appointees from both major political parties to work jointly to legitimize rulemaking and adjudications. Well-known safeguards exist to foster the autonomy of independent agency appointees from the executive and legislative branches. But I argue that the majoritarian structure of (almost all) independent agency commissions and the partisan nature of the appointments process ensure that politics, rather than bipartisanship and independence, prevail when the stakes matter. To prove this point, I have gathered a data set of over 5,000 commissioner votes by the Securities and Exchange Commission (“SEC”), Nuclear Regulatory Commission (“NRC”), and Federal Election Commission (“FEC”) from the Obama and Trump administrations. I compare the impact of three-two partisan splits in commissioners (SEC and NRC) with three-three commissioner political splits (the notable institution with partisan balance—the FEC). A large majority of votes in the SEC and NRC on uncontroversial issues are unanimous. However, I show that the partisanship inherent in the majoritarian structure of independent agencies is clear in the subset of ideologically driven votes concerning substantive policy changes. In contrast, the FEC is more frequently affected by strategic, partisan gridlock of commissioners, which often stops votes from happening about controversial election issues. Critics may deride stalemates as a sign of independent agency failure. But I argue that independent agency gridlock concerning politically charged questions shows the virtues of political balance by taking divisive political issues out of the hands of unelected appointees and sending them back to the democratically elected leaders in Congress. I use game theory to illustrate the potential impact of the shift to political balance in independent agencies. Using a range of prisoner’s dilemma simulations, I show how parity in political leadership may increase the potential for agency deadlocks concerning politically divisive questions, while also increasing the payoffs from bipartisanship. At first glance, greater deadlocks may appear to create a potential bias towards the status quo of regulation, which may incentivize the side that benefits to dig in their heels. But the nature of evolving regulatory challenges frequently requires changes even to settled frameworks, which would pressure commissioners to work on building bipartisan consensus. At the same time, divisive questions may lead to lasting impasses among independent agency commissioners. The logic of partisan balance in the leadership of independent agencies would be to place the onus on Congress and the President to address enduring regulatory stalemates through the legislative process. That does not necessarily mean that Congress will address regulatory gridlock. Rather, the more important the issues that independent agencies fail to resolve, the higher the degree of pressure that elected leaders in Congress would face to act.