Search Results (572)

Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology. Full article

This study systematically explores the underlying mechanisms of collaborative innovation driving the green transformation of traditional energy enterprises. Existing research primarily focuses on enterprise scale and overall competitiveness, rarely delving into these specific collaborative pathways. Furthermore, studies employing evolutionary game theory to analyze the tripartite relationship among the government, traditional energy, and emerging technology enterprises remain fragmented, failing to fully capture the dynamic mechanisms of multi-stakeholder strategic choices. To bridge these gaps, this paper constructs a tripartite evolutionary game model incorporating coordination costs and the benefit distribution ratio to explore their influence mechanisms. Replicator dynamics equations are employed to identify stable cooperation conditions, overcoming traditional two-party framework constraints. Additionally, MATLAB R2024b numerical simulations validate the theoretical findings. The results reveal two evolutionarily stable equilibrium points. First, higher initial willingness among participants accelerates the system’s evolution toward a stable cooperative state. Second, coordination costs induced by information asymmetry act as a core bottleneck that deters participation and risks collaborative collapse. Third, targeted government incentives and a rational benefit distribution ratio directly determine cooperation willingness; notably, enterprises adopt collaborative strategies only when this ratio falls between 0.27 and 0.69. Fourth, fair and transparent supervision is crucial for mitigating trust deficits and distribution disputes. Ultimately, scientifically designing incentives, optimizing benefit structures, promoting information sharing, and establishing robust supervision effectively facilitate a sustainable tripartite collaborative innovation pattern. Full article

Photovoltaic desert control (PVDC), an innovative model integrating clean energy development and desertification control, faces complex coordination challenges among local governments, local communities, and photovoltaic enterprises. This study constructs a tripartite evolutionary game model to identify the conditions that drive PVDC toward coordinated governance. The model defines a three-dimensional strategy space: government regulatory intensity (Strong vs. Lax), community willingness to cooperate (Active Cooperation vs. Passive Resistance), and enterprise ecological integration (Active Ecological Integration vs. Passive Land Occupation). Replicator dynamic equations are derived to characterize nonlinear interactions, and the stability conditions of eight pure-strategy equilibrium points are identified through Jacobian matrix eigenvalue analysis. Numerical simulations are conducted using a baseline parameter set that satisfies the Evolutionary Stable Strategy conditions for the ideal equilibrium E8, namely Strong Regulation, Active Cooperation, and Active Ecological Integration. The results show that the system can converge to E8 when higher-level rewards cover government regulation, subsidy, and community-support costs; when community cooperation benefits exceed livelihood opportunity costs and compensation incentives from resistance; and when enterprises’ effective ecological integration costs are lower than the combined benefits of subsidies, avoided fines, and long-term returns. Sensitivity analysis further indicates that government subsidies, fines, community support, cooperation income, and enterprise long-term benefits are key drivers of system evolution, while excessive regulation costs, high opportunity costs, and high ecological integration costs may hinder coordination. Qualitative evidence from four PVDC-related cases in Xinjiang provides practical illustrations broadly consistent with the model mechanisms. This study offers a dynamic analytical framework for designing incentive-compatible governance mechanisms in PVDC and similar multi-stakeholder ecological restoration projects. Full article

Ride-hailing dispatch systems face significant challenges under fluctuating demand and dynamic traffic conditions, where efficient coordination is essential for both platform performance and driver income among large-scale ride-hailing vehicles. This paper constructs a grid-based ride-hailing vehicle dispatch decision model (GRV-DDM), which provides a structured and quantifiable representation of vehicles and orders, effectively capturing spatio-temporal heterogeneity in dynamic traffic environments. Based on this model, a Bi-Level Optimization Multi-Directional Dispatch Decision Algorithm (BO-MDDA) is proposed. At the macro level, evolutionary game theory is employed to adaptively guide collective vehicle strategies toward supply–demand equilibrium, while at the micro level, deep reinforcement learning optimizes individual drivers’ real-time dispatch decisions to maximize long-term profits. A bidirectional feedback mechanism is further designed to integrate macro-level collective intelligence with micro-level individual decision-making. Experimental results across diverse traffic scenarios demonstrate that the proposed approach outperforms classical dispatch algorithms in terms of efficiency and robustness. Full article

Traditional static policy frameworks struggle to effectively respond to dynamic changes in enterprise behavior, thereby undermining the sustainability of policy constraints; therefore, promoting enterprise green technology innovation (GTI) requires adaptive governance, while consumer green preferences play a non-negligible role in this process. This study constructs an evolutionary game model to examine the strategic interactions between governments and enterprises under a dynamic subsidy and penalty mechanism, incorporating consumer green preferences into the analysis. The results show that static subsidy and penalty mechanisms are insufficient to sustain incentives for enterprise GTI; in contrast, dynamic subsidy and penalty mechanisms are more effective in promoting enterprise GTI. Further analysis reveals that the mechanism combining dynamic subsidies and static penalties exhibits superior governance effectiveness, with a “low-subsidy, high-penalty” strategy combination demonstrating a stronger incentive effect in promoting enterprise GTI. Consumer green preferences significantly influence the strategic choices of both governments and enterprises, and their enhancement drives enterprises to engage in GTI. Overall, promoting GTI requires a shift from rigid static policies to adaptive governance, with full considerations on the impact of consumer green preferences on stakeholder behavior. Full article

To address the “greenwashing” trust crisis induced by information asymmetry in sustainable supply chains, this study develops a comprehensive game-theoretic model integrating Stackelberg and evolutionary game theories (EGT). We quantitatively investigate the dynamic interactions among multi-dimensional consumer preferences, blockchain implementation costs, and boundedly rational government interventions. Our analysis yields three core contributions. First, we analytically reveal the “double-edged sword effect” of blockchain adoption. While structural transparency unlocks a trust dividend, exorbitant technological costs trigger a “budget crowding-out effect.” Quantitative results demonstrate that breaching the absolute Feasibility Threshold completely cannibalizes the environmental budget, driving substantive green investments strictly to zero. Second, EGT analysis proves that isolated punitive carbon taxes trap supply chains in a suboptimal “shallow greening” equilibrium. A composite tax-subsidy policy is structurally required to expand the feasible cost space and hedge against technological risks. Finally, we formulate a dynamic policy exit mechanism. As blockchain infrastructure matures and the endogenous green premium effectively offsets implementation costs, regulators must systematically phase out subsidies and converge toward a single-taxation regime to prevent corporate policy arbitrage and alleviate long-term public financial burdens. Full article

This paper investigates the evolution of public cooperation within a four-strategy public goods game that incorporates both consistently and inconsistently moralistic exclusion mechanisms. Using replicator dynamics in an infinite well-mixed population, we demonstrate that the presence of Inconsistent Moralists (IMs), i.e., non-contributors who hypocritically exclude other defectors, fundamentally reshapes the dynamical structure of the multi-player social dilemma game. While the system admits no interior fixed point and the IM strategy itself is evolutionarily unstable, IM acts as a critical catalyst by destabilizing pure defection and redirecting evolutionary trajectories toward exclusion-based cooperation. Ultimately, these findings reveal that diverse enforcement strategies can qualitatively alter evolutionary outcomes by providing a previously overlooked indirect pathway for cooperation to emerge and persist in social dilemmas. Full article

Technology enterprises are leveraging artificial intelligence (AI) to foster disruptive innovation, aiming to seize first-mover advantages in technological catch-up and strategic transformation. Most existing studies adopt static research methods such as empirical analysis to explore corporate disruptive innovation from the dimensions of technology, market, organization and value creation. However, few scholars dynamically investigate the impacts of multi-stakeholder interactions on the disruptive innovation of AI enterprises from the perspective of innovation ecosystem by employing evolutionary game theory. Against this backdrop, this paper adopts the evolutionary game approach to explore how the bounded rational strategic interactions among AI enterprises, incumbent enterprises and governments in the innovation ecosystem affect the evolutionary dynamics of AI enterprises’ disruptive innovation behaviors. It also examines under what conditions of benefits, costs, risks and policies the system can evolve toward a stable strategic equilibrium. The findings reveal that the sustainable advancement of disruptive innovation by AI enterprises is not merely driven by the unilateral willingness of individual firms. Instead, it is jointly shaped by the innovation investment of AI enterprises, cooperative responses of incumbent enterprises, and regulatory and supportive policies of governments, as well as comprehensively influenced by base benefits, R&D investment pressure, technology spillover effects and niche competition risks. This research provides theoretical references for improving the innovation governance and policy support system of the AI industry. Future research can further analyze the influence of strategic interactions among more heterogeneous stakeholders on the evolutionary process of disruptive innovation of AI enterprises. Full article

Recycling end-of-life (EoL) photovoltaic (PV) modules is essential for resource recovery and pollution mitigation, yet weak incentives and non-standardized treatment continue to hinder the development of formal recycling systems. This paper develops a tripartite evolutionary game model involving the government, PV power generators, and third-party recyclers under a reward–penalty policy mechanism. Replicator dynamic equations, Jacobian stability analysis, and MATLAB R2023b (MathWorks, Natick, MA, USA) simulations are used to examine strategic interactions and evolutionary paths. The results show that: (1) under the baseline parameter setting, the system converges to a unique evolutionary stable strategy, (0, 1, 1), namely no government regulation, generator recycling, and recycler green technology innovation; (2) variations in initial strategy probabilities affect convergence speed but do not change the final equilibrium; (3) under the same total reward expenditure, increasing rewards to generators drives the system toward the desirable equilibrium faster than allocating the same amount mainly to recyclers; and (4) penalty policies also promote compliance, but their marginal effect is weaker than that of reward-based incentives. These findings suggest that appropriately designed incentives can accelerate generator recycling and recycler green innovation, while the government’s role may gradually shift from direct intervention to supervision and coordination. Full article

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news regions, to be detected and targeted where strategic shifts are most likely to occur. Importantly, fact-checking is modeled as an external intervention that may induce a probabilistic lasting correction on agents’ latent opinions after removal, capturing more realistic post-intervention behavior. Unlike static strategies that assume fixed fact-checker distributions, the proposed approach continuously reallocates interventions toward structurally critical regions, while adaptively adjusting resource intensity based on misinformation prevalence. Extensive simulations on small-world, scale-free, and random networks show that the adaptive model consistently outperforms static baselines, reducing the final fake-news prevalence by over 90%, accelerating suppression, and improving overall system efficiency. Statistical tests confirm the significance of these improvements ($p<0.001$), while sensitivity analyses demonstrate robustness across parameter settings and intervention assumptions. Full article

Optimizing high-speed railway (HSR) timetables requires coordinated decisions on train stopping patterns and local feasibility constraints, such as headway separation and service ordering, particularly at stations where consecutive trains share infrastructure. As network size and service density increase, station-level interactions can induce strategic behavior among trains competing for overlapping passenger markets. This study introduces a delayed-feedback evolutionary game framework to model interactions between two trains under incomplete information. Here, “delayed feedback” represents the lag in information transmission and periodic strategy updates, rather than operational train delays. We first formulate replicator dynamics for the non-delayed scenario, deriving equilibrium points and local stability conditions. The model is then extended to include delayed feedback in payoff evaluation, and the impact of delay parameters on stability and convergence is analyzed. To account for operational heterogeneity, two station layouts are considered: (i) two tracks per direction, representing small stations with limited overtaking and stop–pass combinations, and (ii) four tracks per direction, representing large stations that allow simultaneous stopping and richer operational patterns. Numerical simulations examine convergence, oscillatory behavior, and parameter sensitivity. Results indicate that delayed feedback significantly influences system dynamics: small delays maintain convergence to evolutionarily stable strategies, whereas larger delays induce persistent oscillations and complex transient trajectories. Station layout further affects stability regions and long-term strategy profiles. This study is based on analytical derivation and numerical simulation rather than sample-based statistical inference; therefore, non-parametric hypothesis testing is not applicable in the present framework. This framework provides a game-theoretic and stability-oriented tool for station-level timetable analysis, offering methodological guidance for timetable design under delayed decision feedback in HSR operations. Full article

The optimization of regulatory strategies for live-streaming e-commerce is essential for tackling misleading marketing behaviors (MMBs) and protecting stakeholders’ rights. This is fundamental to building a healthy and sustainable live-streaming e-commerce ecosystem. To address governance challenges and regulatory inefficiencies, this paper adopts a behavioral perspective and constructs a tripartite evolutionary game model involving platforms, live streamers, and consumers. It unveils the interactive mechanism between dual herd mentality and overconfidence in shaping regulatory strategy evolution, with numerical simulations validating the dynamic regulatory pathway. The findings indicate: (1) The severity of platform penalties is the linchpin of collaborative governance. Under low penalties, herd mentality may spur consumers to report live streamers who choose the MMB strategy, but the absence of deterrence traps the market in a “more reports, more MMBs” vicious circle. Moderate-to-high penalties align herd behavior with non-MMBs by live streamers, but risk unleashing irrational herd conduct among consumers. A dynamic matching mechanism that adapts penalty intensity to prevailing herd levels is therefore essential. Once a critical threshold is crossed, it enables synergistic benefits through joint supervision by consumers and platforms. (2) Overconfidence on the live streamer’s side magnifies the illusion of inflated returns. At low levels, the herd mentality from consumers can correct this psychological bias, but once overconfidence becomes pronounced, only large-scale supervising can outweigh the expected gains from MMBs. (3) These two behavioral traits jointly shape the equilibrium of the live-streaming e-commerce system and should therefore be treated as key considerations when designing dynamic regulatory strategies. Full article

Freshwater scarcity is emerging as a critical constraint on industrial clusters, production networks, and urban service systems, where water functions simultaneously as an essential production input and a shared regional resource. This study investigates how post-allocation water-quota imbalances in large inter-basin diversion systems can be addressed through adaptive secondary water rights trading. Focusing on China’s South-to-North Water Diversion Project (SNWDP), the research aims to explain under what institutional and efficiency conditions water rights trading can enhance corporate social responsibility, environmental management, and sustainable supply chain resilience. The study’s main innovation lies in the development of a corporate-oriented evolutionary game model that links water governance with corporate production, urban–industrial demand, and responsible supply chain management. Unlike conventional models, it incorporates bounded rationality, heterogeneous water-use efficiency, information asymmetry, transaction costs, primary allocation water pricing, and the risk of unrecovered basic water fees. Using a case inspired by the Zhengzhou–Nanyang transaction along the Middle Route of the SNWDP, the model simulates the strategic interaction between a water-rich node with surplus quota and a water-scarce node facing deficit demand. The findings show that a socially desirable Trade–Trade equilibrium emerges only when efficiency expectations and institutional conditions are favorable. Lower transaction costs and basic water prices, higher sunk-fee risk, and clearer efficiency differentials significantly increase trading willingness. The study demonstrates the practical value of transparent secondary water markets in improving allocative flexibility, reducing governance rigidity, and promoting more responsible and environmentally efficient regional water management. Full article

Background: The convergence of Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) has established the field of Intelligent Evolutionary Games (IEGs). While IEG applications have flourished in general systems and social sciences, their operationalization within healthcare (IEG Health) remains significantly underdeveloped. This study identifies a “knowledge void” in the literature, where the bottleneck is not a lack of clinical data but a scarcity of frameworks that integrate intelligent strategic modelling into clinical practice. Methods: We employ the Synthetic Near-Empty Review (SNER) framework, utilizing Synthetic Knowledge Synthesis (SKS) and bibliometric triangulation via VOSviewer. Three distinct corpora—IEG Health, EG Health, and IEG All (IEG)—were harvested from Scopus and mapped to identify thematic clusters and translation pathways. Results: The analysis reveals that IEG Health is a nascent domain currently focused on service regulation in elderly care and chronic disease management. We demonstrate a “Translation Framework” to bridge the research void, mapping concepts like Social Trust and Reputation Management from the broader IEG literature into clinical-specific models, such as Doctor-AI Adoption and Adaptive Coordination Games. Conclusions: By shifting from static Replicator Dynamics to Adaptive Learning Strategies (e.g., MARL and Bayesian updating), IEG Health can address critical challenges like algorithm aversion and clinical deskilling. Furthermore, transitioning these models into clinical environments requires the incorporation of structured ethical guidelines, such as ALTAI, to ensure algorithmic accountability. This study provides a structured foundation for future research to transition from theoretical modelling to AI-augmented clinical decision-making. Full article

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms. Full article