Integrating machine learning algorithms and game theory for optimized shipyard site selection in Istanbul

Optimization of dynamic trading strategies in carbon markets: A decision framework based on Stackelberg game and prospect theory

This paper investigates Target-Attacker-Defender (TAD) differential games comprising an active target, multiple attackers, and defenders, including some anomalous agents within the defenders' team. Firstly, we introduce two types of anomalous defenders, characterized by coefficients in the performance metrics, termed “greedy” and “fearful.” Then, we explore two distinct scenarios: (i) when the attackers possess unlimited observation capabilities while visibility limitations constrain the defenders and target, and (ii) when all agents are subject to imperfect information, meaning they only possess limited visibility. The interactions among agents are modeled as nonzero-sum games to analyze their optimal decision-making processes. Due to the agents' visibility constraints, a corresponding visibility network emerges during their interactions. To address this problem, we employ inverse game theory to derive Nash equilibrium strategies with adaptive state feedback for agents. Furthermore, we analyze the impact of anomalous defenders on the victory conditions and capture time of the defenders' team. Finally, we validate the effectiveness of our results through numerical simulations.

We study a public good game with N citizens and a Governor who allocates resources from a common fund. Citizens may voluntarily contribute or be compelled to do so if audited, in which case shirkers face a penalty. The Governor decides how much of the fund to devote to public good provision, with the remainder embezzled. Crucially, the Governor's utility combines material payoffs from embezzlement with belief-dependent reputational concerns. We fully characterize the symmetric subgame perfect equilibria (SSPE) of the game. The model always admits at least one pure-strategy equilibrium, ranging from universal free-riding with complete embezzlement to full contribution with efficient provision. Mixed-strategy equilibria exist only in a narrow region of parameter values and may involve multiple equilibria. Our analysis highlights the roles of penalties, audits, and reputational incentives in sustaining contribution and provision, thereby linking public good provision with the broader literature on corruption, embezzlement, and psychological game theory.

Compared with other construction operations, high-altitude operations are more dangerous. Falling from a height is the main type of accident in construction. It is important to study the human risk of falling from height to reduce falling accidents. Based on the Human Factors Analysis and Classification System (HFACS) model, a preliminary evaluation index system for fall risk in building construction was established. Through the Delphi method and sensitivity analysis, the initial indicators were screened, the index factors that did not meet the requirements were removed, and the final human risk index evaluation system was determined. The system includes five first-level indicators and 17 s-level indicators of organizational influence, unsafe supervision, preconditions for unsafe behavior, and unsafe behavior. Subsequently, the analytic network process–entropy weight method (ANP-EWM) is used to subjectively and objectively weight the evaluation indicators, and the combined weight is obtained through game theory. The matter–element extension model is constructed to evaluate the human risk of falling from height in construction. Finally, an empirical analysis is carried out with the Y project as a case study. The novelty of this study lies in integrating human-factor analysis with the matter–element extension model for fall risk assessment in construction, while combining ANP, the entropy weight method, and game theory to balance subjective and objective weighting. The proposed model provides a practical tool for evaluating and controlling human risk in high-altitude construction operations. The results show that the correlation degree calculated according to the matter–element extension model is K4 = 3.5, and the human risk of falling from height in the construction of Y project has generally reached an excellent level. However, the evaluation level of some evaluation indexes is still low, which is consistent with the actual situation of construction enterprises in Y project. This model provides a direction for the study of human risk assessment of falling from different construction heights.

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Despite the increasing educational attainment of women and the overall rise in female labor force participation, it remains common for women to withdraw from the labor market during marriage and childbearing. After giving birth, women face the choice of whether to return to the labor market or to take care of their children full-time. This choice is often attributed to the subjective will of the women themselves or the division of labor within the family, with less consideration given to the role of institutional factors, gender roles, and the negotiation mechanisms within the family. This section, based on a game theory perspective, assumes that both spouses are completely rational and establishes a simple two-person non-cooperative game model to explore the impact of the division of child-rearing responsibilities on women’s labor market participation. The results show that, in the absence of government intervention measures, there will be an equilibrium solution within the family where the wife withdraws from the labor market and the husband takes on less child-rearing tasks. However, with the implementation of public policies such as parental leave, allowances, or flexible employment, the game outcome may change, leading to a more equal division of child-rearing responsibilities and facilitating women’s re-employment. This paper argues that women’s withdrawal from work is not a completely autonomous choice but rather a result based on the established institutional environment and the distribution of decision-making power within the family.

Optimal selection of railway alignment schemes is a critical phase in railway line design. However, existing studies primarily adopt an engineering perspective, often overlooking economic and social factors. Current decision-making models also face limitations in quantifying qualitative indicators and balancing subjective and objective weights, thereby undermining the scientific rigor and effectiveness of the evaluation process. To address these issues, this study proposes a multi-attribute optimization model for railway alignment selection based on combined weighting and the interval number model. First, the key factors influencing railway alignment are systematically identified through expert consultations and an extensive literature review. On this basis, a multi-dimensional evaluation index system comprising 20 indicators is established, encompassing four dimensions: technical feasibility, economic rationality, environmental impact, and regional coordinated development. Subjective weights are derived using the Analytic Hierarchy Process (AHP), while objective weights are calculated through an improved CRITIC method. Game theory is then employed to integrate them. To further enhance the objectivity and robustness of the evaluation, the study develops a decision-making model based on interval number distance. This model quantifies qualitative indicators using interval numbers and ranks alternative schemes by computing their distances to an ideal solution. A case study on the Jieshipu-Pingliang section of the Dingxi-Pingliang railway confirms the method's validity. The results show that the comprehensive weighting method reduces the influence of weight factor sensitivity on route selection. Furthermore, the scheme ranking derived from the interval number model closely aligns with the scheme recommended by the design institute and demonstrates stability. The research findings can provide a scientific reference for optimizing railway alignment in economically underdeveloped regions.

Purpose Crowdfunding (CF) is rapidly emerging as a burgeoning industry, revolutionizing the financing of entrepreneurial projects. In donation-based Crowdfunding (DCF), Donors provide financial support without expecting financial returns, driven solely by social or personal motivations. This study aims to assess the creation of social impact through donors' strategic decisions in DCF of Social entrepreneurship projects. Design/methodology/approach This research uses a case study approach to analyze a DCF campaign in Iran for a social entrepreneurship project. Finally, the results of a social return on investment (SROI) analysis were examined through game theory. Findings Social value was measured through an SROI assessment that compares food distribution and employment generation interventions. The values obtained from the social impact calculation were then incorporated into a game theory model to examine how trust and perceived SROI jointly influence donors' strategic decisions and the resulting equilibrium outcomes. Originality/value First, this study demonstrates that DCF can lead to widespread social impact and make a significant contribution to social entrepreneurship initiatives. Second, integrating trust and SROI within a game theory framework revealed that donors' preferences and strategic decisions determine the social impact generated in CF campaigns for social entrepreneurship projects. Third, trust, as a catalytic factor, can enhance the success of high-impact social entrepreneurship projects in DCF campaigns. This study contributes to the success of entrepreneurs and CF platforms by providing a comprehensive understanding of the factors that drive a DCF project's success.

To further expand market share, some ride-hailing platforms with relatively high volume (H-platforms) may choose to introduce ride-hailing platforms with relatively low volume (L-platforms), thereby intensifying competition as more consumers are aware of L-platforms. To attract more consumers, L-platforms may choose to access H-platforms. This incurs additional commission costs. This paper develops a game theory model to explore whether the H-platform should introduce the L-platform and whether the L-platform should access the H-platform when considering the consumer’s reference price. The results show that the perceived reference price level (PRPL) influences the operation mode choice of H-platform when the L-platform’s service quality is low and the commission ratio is high. Furthermore, the PRPL influences the L-platform’s operation mode choice when the L-platform’s service quality is moderate (low) and the commission ratio is small (moderate). Additionally, our findings suggest that the H-platform introducing the L-platform always benefits consumers, but may hurt drivers.

During the critical period of agricultural green transformation, clarifying the evolutionary logic of farmers’ green production behavior under a multi-stakeholder framework provides significant insights for implementing “Dual Carbon” goals, establishing long-term mechanisms for high-quality agricultural development, and resolving deep-seated contradictions in agricultural non-point source pollution. Based on the social co-governance and public participation framework, this paper constructs a tripartite evolutionary game model involving government departments, farmer groups, and the general public, grounded in cost–benefit analysis, social governance friction, and evolutionary game theory. Through simulation, the study explores the equilibrium states and the specific impacts of varying parameter values on stable points. The findings reveal that: (1) The “interest price scissors” (benefit disparity) between green and conventional production is the key determinant of farmers’ strategic equilibrium. Once this structural contradiction is resolved, green production becomes the optimal strategy. (2) Farmers are highly sensitive to marginal cost–benefit fluctuations, leading to a sequential behavioral cascade: farmers retreat first, followed by the government, and finally the public. (3) Public participation cost is the pivotal variable for activating the co-governance mechanism, and the application of digital governance tools determines the time required to reach equilibrium. (4) A “Success Paradox” exists in government regulation; incentive mechanisms must be adjusted promptly after initial success. (5) Integrated policy combinations outperform single instruments; breaking the “locked-in” state requires a policy shock of sufficient intensity. This research offers a theoretical basis and policy enlightenment for optimizing the social co-governance landscape and promoting sustainable agricultural modernization.

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator.

The European Union aims to achieve climate neutrality by 2050, with biogas and biomethane expected to play an increasingly important role in the decarbonisation of the energy system. This study investigates the economic and social determinants shaping the development of biogas production in European countries and identifies an optimal investment strategy for new biogas plants under varying environmental conditions. An expert–mathematical method was applied to assess and hierarchise twenty economic and social factors influencing biogas production, based on evaluations provided by 71 experts from eleven European countries. Subsequently, individual choice criteria derived from game theory were used to determine the optimal strategy for biogas plant construction under conditions of uncertainty. The results indicate that six determinants—EU-level production support mechanisms, investment costs, national support instruments, process efficiency improvements, community involvement, and agricultural raw material prices—account for 52.9% of the total impact on biogas development potential. Among the analysed investment options, large-scale biogas plants with an installed capacity of 3 MW were identified as the optimal strategy, offering the lowest unit production costs and the lowest risk of cost overruns across diverse economic and social environments. These findings provide policy-relevant insights for supporting efficient and socially acceptable biogas deployment in Europe.

Traditional evaluations of revetment projects primarily focus on structural safety and economic analysis, which cannot comprehensively reflect the overall effectiveness of such projects. To address this issue, this paper establishes a comprehensive evaluation index system for ecological revetments based on ecosystem theory and sustainable development principles. The system is tailored for the Pinglu Canal Ecological Revetment Demonstration Project. It assesses three key aspects: structural stability, ecological health, and socioeconomic benefits. Subjective weights were calculated using the Fuzzy Analytic Hierarchy Process (FAHP). Objective weights were determined by optimizing the Projection Pursuit (PP) model with the Tent-improved Crocodile Ambush Optimization Algorithm (TCAOA). Game theory was employed to compute the combined weights. The evaluation grade of the ecological revetment project was subsequently determined using a cloud model. The results show that the cloud eigenvalues of the project’s comprehensive evaluation are (1.096, 0.209, 0.047), and the application effectiveness is rated as “Excellent”. The cloud expected values for structural stability, ecological health, and socioeconomic benefits are 1.02, 1.18, and 1.15, respectively. All of these values are at the “Excellent” level. Compared with GA-PP and PSO-PP, TCAOA-PP converges faster and more stably. It requires only 347 iterations, achieves a coefficient variation of 3.8%, and reduces computation time by 23%. By revealing the nonlinear coupling relationships among indicators, the model presented in this paper provides a methodological foundation for establishing an evaluation framework that is ecologically interpretable for bank protection. This study has important practical significance for promoting the high-quality development of inland waterways and the construction of ecological revetments.

Abstract The paper considers the assignment of students to seminars regarding three hierarchical objectives: maximizing the students’ preferences, maximizing the within seminar diversity, minimizing the between seminar diversity variation. While the first objective pictures the students, preferences, the second and third picture the school’s preference of having comparable seminar groups. To reach this aim the paper extends the well-known Maximally Diverse Grouping Problem and its balanced version by the first objective, the students’ interests. The students’ interests are pictured by a preference sequence the students have for the offered seminars, e.g. because of the scheduled time, the topic or the lecturer. We present solution approaches that include properties from game theory in the assignment and result in an assignment of students to seminars including the students’ as well as the school’s preferences. Our results show that the presented solution approaches are able to solve instances of practical relevant size within half an hour (close to) optimality. Furthermore, in our artificial test instances, including student preferences in the assignment only led to a small reduction of the maximal diversity for instances of realistic size (2–3% difference for seminars with 20 students).

This paper examines the evolution of auction design from ancient traditions to modern digital markets, focusing on the development of strategy-proof mechanisms that incentivize truthful bidding. The research analyzes classical formats including first-price and English auctions, highlighting their strategic limitations, before exploring the Vickrey-Clarke-Groves (VCG) mechanism which achieves dominant-strategy incentive compatibility through externality pricing. The study then empirically contrasts Google's Generalized Second-Price (GSP) auction with Meta's VCG-inspired approach in digital advertising markets. Cost-per-click analysis reveals an average premium of $1.52 in GSP auctions, with industry-specific variations reaching $3.88. A computational complexity analysis demonstrates that GSP achieves O(n log n) time complexity compared to VCG's O(nk), providing algorithmic justification for GSP's prevalence in latency-sensitive real-time bidding environments. These findings suggest that real-world mechanism selection depends critically on the interplay between theoretical properties, market structure, and computational constraints. CCS CONCEPTS Theory of computation~Theory and algorithms for application domains~Algorithmic game theory and mechanism design~Computational pricing and auctions.

Logistics platforms (LPs) increasingly use multidimensional data to provide supply chain financing (SCF) to small and micro logistics enterprises (SMLEs). However, platform-centered data control can weaken financial institutions’ (FIs’) trust in platform data, thereby reducing the effectiveness of data-driven credit enhancement. To address this issue, this study integrates the social–ecological systems framework with evolutionary game theory and develops a tripartite evolutionary game involving FIs, LPs, and SMLEs. By comparing scenarios with and without regulatory governance, the study examines how regulatory governance affects the strategic evolution of data-driven credit enhancement in SCF for SMLEs. The results show that regulatory governance improves system performance through cost reduction, trust enhancement, and incentive alignment, thereby relaxing the conditions required for the system to evolve toward the Pareto-optimal state of credit granting, strict supervision, and non-default. The strategic choices of the three actors are mainly influenced by data acquisition costs, incentive intensity, and penalties. Numerical simulations further show that government incentives must exceed certain thresholds to promote cooperation, while penalty mechanisms play a critical role in constraining opportunistic behavior and accelerating convergence to the desirable equilibrium. These findings provide theoretical support and practical insights for improving data-driven credit enhancement in SCF for SMLEs.

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage.

Purpose This study aims to examine the interplay between infrastructure development and environmental protection, focusing on how governmental regulations shape contractor behavior and exploring policy mechanisms that reconcile economic and ecological objectives. Design/methodology/approach An integrated framework combining evolutionary game theory and system dynamics simulation models the interactions between contractors and regulatory agencies. Computational experiments assess the effects of penalties and public reporting systems on behavioral stability. Findings Analysis reveals three significant outcomes: (1) Pure strategy solutions fail to achieve evolutionary stability regardless of parameter configurations; (2) Mixed strategy equilibria demonstrate conditional stability, particularly when public reporting systems are operational; (3) While both financial penalties and public disclosure influence short-term behavior modification, only robust public monitoring systems generate sustainable compliance patterns over extended periods. Research limitations/implications The findings deepen understanding of how government–contractor interactions shape sustainable infrastructure development, emphasizing the pivotal role of regulatory design and behavioral adaptation in achieving enduring environmental and economic balance. Practical implications Policymakers should prioritize public reporting and strengthen monitoring systems to align contractor incentives with ecological goals. Combining penalty mechanisms with transparent oversight offers a coherent and lasting approach to advancing green construction and maintaining regulatory compliance. Originality/value This study proposes a novel framework integrating evolutionary game theory with system dynamics, rarely applied in infrastructure governance. It captures dynamic feedback between contractors and regulators and shows that transparency mechanisms outperform punitive measures in sustaining compliance, offering clear guidance for policy optimization.

To address the demographic challenges posed by accelerated aging and shrinking labor force, it is crucial to develop human resources and encourage labor participation among older adults to achieve active aging. This study constructs a three-party evolutionary game model involving government departments, local enterprises, and older workers based on evolutionary game theory. It analyses the strategic choices of each party during the gaming process and their evolutionary strategies under different conditions, with numerical simulations conducted to examine the impact of parameter adjustments on these evolutionary dynamics. The findings indicate that: the effectiveness of digital government construction serves as a critical determinant for governmental support of older adults’ labor participation; the probability of enterprises actively employing older workers correlates with both the outcomes of corporate digital transformation and labor costs associated with older adults’ employment; older individuals’ likelihood of labor participation relates to employment income and age discrimination, while digital technology empowerment facilitates strategic shifts from negative to positive engagement for both government and enterprises. Based on these conclusions, policy recommendations including strengthening digital government development, accelerating enterprise digital transformation, and fostering age-friendly employment environments are proposed, thereby providing theoretical foundations for implementing national strategies addressing the labor shortage challenges due to population aging.