Ecolinguistic analysis of the blame game in Los Angeles wildfire reporting

Blockchain-enabled maritime financial ecosystems: Evolutionary game analysis of financing efficiency and trust mechanisms

Breaking the coordination deadlock of hydrogen application in inland shipping: a four-stakeholder evolutionary game analysis

ABSTRACT Drawing on visual studies, memory politics and game analysis, this article examines how the video game Gris stages an interactive aesthetics of mourning. Through close analysis of its colour palette, sound design and spatial composition, I argue that Gris transforms the five stages of grief proposed by author Elisabeth Kübler-Ross (denial, anger, bargaining, depression and acceptance) into a symbolic reckoning with Spain’s unresolved historical trauma. By rejecting monumentalism and embracing vulnerability, the game reconfigures the aesthetics of post-Franco cultural memory, offering an alternative form of commemoration rooted in silence, care and emotional resilience.

Evolutionary game and simulation analysis of collaborative governance of sports public opinion.

Introduction Food safety supervision involves dynamic strategic interactions between enterprises and government regulators. Traditional static models fail to capture the co-evolution of behaviors under varying policy incentives. This study develops a coupled evolutionary game-system dynamics (EG-SD) model to investigate how cost-benefit configurations and policy parameters shape long-term strategic outcomes in food safety governance, with the goal of identifying conditions that balance enterprise-government relationships and promote sustainable food systems. Methods An integrated EG-SD framework was constructed to model the two-player (enterprise and government) evolutionary game. Equilibrium stability conditions were derived analytically under different cost-benefit scenarios. Stability analysis was performed to identify evolutionary stable strategies (ESS). A numerical simulation was conducted to replicate four distinct case configurations, tracking strategy evolution over time under varying parameter sets. Results The stability analysis revealed that equilibrium outcomes depend critically on the relative magnitudes of supervision costs, penalty levels, and compliance benefits. Numerical simulations demonstrated the absence of any stable pure or mixed strategy in Case 1. In all simulated scenarios, enterprises consistently converged to a “not pay attention” strategy regardless of government actions. Government strategy was scenario-dependent: it fully adopted a “supervise” stance in some cases, but switched to a “not supervise” stance in Cases 3 and 4. No parameter configuration induced enterprise proactive compliance as a stable outcome. Discussion The government's scenario-dependent behavior indicates that supervision is effective only under specific cost-benefit thresholds. These findings underscore the necessity of redesigning regulatory dynamics to align economic incentives with long-term environmental and social health goals. Effective supervision requires not only enforcement but also mechanisms that make proactive compliance economically attractive for enterprises. The model provides a tool for testing policy interventions before implementation.

Purpose The promotion of “Zero-Waste Construction Sites” (ZWCS) is caught in a dilemma between insufficient effectiveness of unilateral government governance and a lack of initiative from enterprises to participate. Accordingly, this study aims to explore how to enhance construction enterprises' enthusiasm for building ZWCS through government incentive strategies and the involvement of industry associations, as well as how to effectively integrate the government's guiding role with the coordinating role of industry associations. Design/methodology/approach This study constructs an evolutionary game model and conducts simulation experiments to systematically analyze the strategic interactions among multiple stakeholders in the construction of ZWCS. It specifically examines the decision-making behaviors and evolutionary dynamics of the three core interest entities: the government, industry associations, and construction enterprises. Findings The study shows that the government plays a leading role in the incubation stage of ZWCS, and appropriate subsidies and penalties can dynamically adapt to the development of ZWCS at each stage, while high incremental costs inhibit the motivation of construction enterprises to build ZWCS. The participation of industry associations can transfer the regulatory functions of the government, save the cost of ZWCS, and help to stimulate the participation of all parties. Originality/value This study develops an effective governance framework for ZWCS, centered on government guidance, association coordination, and enterprise participation, by deciphering the underlying collaborative mechanisms among these three stakeholders. This approach offers a promising pathway to support the “Zero Waste City” initiative and contribute to high-quality development within the construction industry.

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.

Background: Students’ prior mathematical knowledge plays a critical role in their success in university mathematics, yet it is often treated diagnostically rather than instructionally. Aim: This study examines students’ prior mathematical knowledge at the start of a university calculus course, with the purpose of identifying conceptual and procedural strengths and weaknesses that are relevant for subsequent learning and for a mathematical modelling intervention. Setting: The study was conducted in South Africa and involved three cohorts of first-year science and engineering students (1874 participants) across the years 2020, 2021 and 2022. Methods: A mixed-methods sequential explanatory design was employed. Quantitative data were collected using a diagnostic test. Welch’s one-way analyses of variance (ANOVAs) and Games–Howell post hoc tests were used to compare cohort performance across content areas. Qualitative analyses of written solutions from a stratified random sample of 90 students were conducted to examine errors and misconceptions. Results: Although overall test performance showed modest improvement across cohorts, students’ results remained low across most content areas. Algebra emerged as a relative strength, indicating procedural fluency, whilst weaknesses were evident in Analytical geometry, Functions and Modelling. Qualitative analyses revealed persistent conceptual and representational difficulties, in interpreting inequalities, graphing functions and constructing and using linear models. Conclusion: The findings suggest that apparent improvements in preparedness may cover structural weaknesses. The study highlights the importance of using diagnostic evidence not merely to identify at-risk students, but to inform responsive teaching that addresses conceptual gaps. Contribution: By integrating large-scale diagnostic data with qualitative error analysis, students’ prior knowledge can be leveraged as an instructional resource to support modelling-oriented teaching in university mathematics.

A wave of large-scale retirement of power batteries is gradually approaching, and the patent licensing conditions for remanufacturing retired power batteries present opportunities for third-party recycling manufacturers to emerge. Considering both the carbon-emission benefits of power battery recycling and the intellectual property disputes, this paper establishes an evolutionary game model with third-party recyclers and battery manufacturers as players. It examines the costs and utilities of stakeholders involved in the reverse logistics process of power battery recycling under carbon quotas, accounting for patent licensing, and analyzes key parameters and participant strategy choices. The research indicates: (1) when the volume of waste power batteries is significant, third-party recycling manufacturers tend to choose direct battery disassembly; (2) at higher carbon prices within the carbon market, third-party recycling manufacturers are more likely to adopt remanufacturing strategies; (3) lower patent licensing fees combined with higher patent maintenance costs help battery manufacturers secure greater profits and encourage third-party recycling manufacturers to engage in battery remanufacturing activities.

Currently, the cognitive synergy between artificial intelligence and human editors will become an important direction to promote the development of scientific and technical journals. The purpose of this paper is to illustrate the evolutionary game dynamics between multiple stakeholders in the intelligent transformation of the publishing industry, reveal the behavioural rationality and strategic decisions of each participant, and explore the optimal coping strategies under the interaction between AI technology and human resources. In order to deeply study the dynamic interaction between technology and human resources under the framework of co-construction of AI and science and technology journals, we divided the relationship between each stakeholder. We established a game relationship model, taking the government and ethical regulators, the editorial boards of scientific and technical journals, and research groups as game participants. Then, we addressed the stabilisation strategy problem and examined the strategic choice dilemmas faced by these three parties. We identified four stabilisation points and studied the evolutionary game through four stages of technology, early stage, development phase, surge phase and maturity phase respectively. Based on the results of the game analysis, the coping strategies of gradient adaptation of technology embedding and business process, capacity cultivation of human capital and organisational development, value reconstruction of academic ecology and scientific research culture, precise insight of user needs and cognitive behaviours, and globalisation and regional differences are proposed from the perspectives of governmental and ethical regulators, editorial boards of science and technology journals, and scientific research groups, respectively. The human-machine collaborative editing model, by integrating human creativity and the efficient processing capability of AI, will achieve a double rise in quality and efficiency in the fields of content review, intelligent proofreading, editing and processing, precise pushing, and knowledge dissemination.

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.

In recent years, understanding player behavior and emotional states during gameplay has become an important research area in game analytics and human computer interaction. Emotional state detection enables game designers and researchers to analyse player engagement, improve user experience, and develop adaptive gaming environments. Traditionally, emotional behavior analysis in games relied on manual approaches such as questionnaires, player observation, and basic statistical analysis. These methods were time-consuming, less accurate, and unable to provide real-time insights into player emotions and behavior during gameplay. To address these limitations, this research proposes a Hybrid Fuzzy Boosting Architecture for Accurate Emotional State Detection in Real-Time Gameplay. The system is implemented as a web-based application using the Django web framework. Machine Learning (ML) techniques are used to analyse gameplay behavior data and predict emotional states efficiently. The research utilizes several ML algorithms including K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machine (SVM), and a proposed hybrid model Fuzzy Neuro Boosting (FNB) model that combines Fuzzy Neural Network with Histogram Gradient Boosting (FNN-HGB). These algorithms are trained and evaluated using both classification and regression tree (CART) techniques to classify player behaviour (play_behavior) and predict engagement intensity (activity_level). Among the evaluated models, the proposed hybrid FNB model achieves higher prediction accuracy compared to traditional ML classifiers, demonstrating improved performance in handling complex and noisy gameplay data. The system also includes modules such as user authentication, exploratory data analysis, model training, performance comparison, and real-time prediction. By integrating ML models with a Django-based web interface, the developed system provides an efficient platform for analysing gameplay behaviour and predicting emotional states, thereby supporting better decision-making and improved player experience in modern gaming environments.

While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. To address the collaborative governance dilemma, this study constructs a tripartite “platform-user-government” evolutionary game model based on prospect theory. It explores the evolutionarily stable strategies and stability conditions of each actor, supplemented by numerical simulations and practical case validation. The results indicate that: (1) under specific conditions, the system can converge to an ideal equilibrium {active platform governance, engaged user participation, stringent government supervision}; (2) the government’s reward–penalty mechanisms can drive the system towards this ideal equilibrium; (3) users’ digital literacy is a key variable influencing the system’s evolutionary path; (4) both the risk preference coefficient (β) and loss aversion coefficient (λ) from prospect theory have a significant moderating effect on the system’s evolution. Finally, targeted recommendations are proposed for the three aforementioned stakeholders to accelerate the improvement of China’s collaborative governance of the content ecosystem.

Roblox is one of the most popular mobile gaming platforms; however, its rapid growth has led to an increasing number of user complaints related to technical stability, which significantly affects user experience. Common issues such as bugs, asset loading problems, and unstable connections are frequently expressed in user reviews, making sentiment analysis a valuable approach for identifying critical technical problems in mobile games. This study aims to analyze user sentiment toward these technical aspects and to compare the performance of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) in aspect-based sentiment classification. A total of 12,809 Indonesian-language reviews were collected from the Google Play Store during October 2025. The research methodology included data scraping, text preprocessing (cleansing, tokenization, normalization, stopword removal, and stemming), lexicon-based sentiment labeling, and data balancing using the Synthetic Minority Over-sampling Technique (SMOTE). TF-IDF was used for feature extraction in the SVM model, while word embeddings were applied for the CNN model. The results show that the Bug aspect is the most dominant issue (63.91%), followed by Connection Stability (34.41%) and Asset Loading (1.68%). In terms of classification performance, SVM outperformed CNN, achieving 96% accuracy, precision, recall, and F1-score, whereas CNN obtained an accuracy of 80.63% with an F1-score of 0.81. These findings indicate that SVM combined with TF-IDF features is more effective than CNN for classifying short and informal mobile game reviews and provides useful insights for developers in prioritizing technical improvements.

While the terms “cozy” and “eco” have become common in recent game culture, the combined term “cozy ecogame” is rarely used and has not been fully defined as a concept. The study identifies how cozy ecogames engage players with environmental issues through a game analysis of two cozy and two noncozy ecogames, using a DiGAP-inspired protocol. Further included is a content analysis of 400 player reviews for two cozy ecogames and two noncozy ecogames. The findings reveal how cozy ecogames avoid highlighting harmful effects or dystopian futures, to instead utilize cozy design elements to construct hopeful climate communication, creating an experience where players feel relaxed and safe to participate in climate action, leading to noticeable betterments of in-game environments. Players of noncozy ecogames are also identified as having different motivations for playing than those of the cozy ecogames.

With the global construction industry undergoing a green transformation, stimulating the collaborative innovation (CI) of green building generic technology (GBGT) among stakeholders through the coordinated configuration of multiple policy instruments has become a pressing challenge. Because the existing literature on the CI of GBGT is relatively limited, the primary objective of this study is to investigate the evolutionary process of influencing stakeholders’ CI behavior in the GBGT field by policy instrument combinations. This study establishes an evolutionary game model that includes the government, construction enterprises, and universities and research institutions to analyze their collaborative innovation behaviors in scenarios where the government’s initial strategic probabilities are low, medium, and high. Based on this model, numerical simulations will parameterize supply-side, demand-side, and environmental policy instruments to quantify their systemic effects on the evolution of multiagent strategies. The research findings indicate that: (1) Six potential evolutionary stable strategies (ESSs) exist within the system. The ideal ESS identified in this three-party evolutionary game model is characterized by basic motivation, active collaboration, and active collaboration. (2) A higher initial probability of the government’s active motivation accelerates the alignment of enterprises with universities and research institutions toward active strategies. (3) Increased financial subsidies significantly enhance the willingness of government, construction enterprises, and universities and research institutions to adopt active strategies. However, procurement intensity does not significantly influence their strategic activation. Conversely, intensifying reward and punishment measures prolongs the duration required for the system to align toward stable equilibria. The results of this study reveal the mechanism by which policy instrument combinations promote CI in GBGT, thereby providing beneficial references for its sustainable development.

An evolutionary game analysis of a dynamically switching zero-determinant strategy

Carbon emission reduction strategy in shipping industry: A stochastic evolutionary game analysis

Abstract The dual constraints of environmental protection and resource reserves have accelerated the substitution of fossil energy sources, such as coal and oil, with renewable energy. This study used a scenario based on renewable energy substitution to model a fossil energy resource mining market that comprised one leading firm and multiple following firms. A market game analysis investigated the problem of multi-subject optimal mineral resource mining and examined the dynamic behavior of leading and following firms. The results revealed that the availability of renewable energy substitutes reduced the profits of mineral mining firms. This meant that these firms were motivated to actively reduce mining costs and optimize mining structures in an attempt to maximize profits. At the stable equilibrium point, the profits of leading firms and the average profits of following firms first increased and then decreased as the number of following firms grew. The optimal market structure maximized the profits of the mineral resources industry. As the number of following firms increased, the equilibrium price decreased, leading to an increase in firm incomes, and the total mining volume of the entire industry rose.