Search Results (2,811)

The efficiency of scientific and technological achievement transformation is constrained by supply–demand matching challenges. Concurrently, Artificial Intelligence (AI) offers novel pathways for digital-intelligence service platforms to mitigate this challenge. To resolve AI investment decision problems of such platforms, this study constructs a bilateral matching model involving high-quality/low-quality technology providers and high-capability/low-capability technology seekers. Based on expected value theory and Stackelberg games, it derives optimal AI investment strategies for the Commercial Platform (platform’s expected revenue maximisation objective) and the Public Welfare Platform (social expected revenue maximisation objective). Findings indicate that higher AI investment contributes to a rise in the matching probability between high-quality providers and high-capability demanders. Owing to incomplete benefit internalization, platforms of different types show divergent willingness for AI investment. The AI investment level of the Commercial Platform is lower than that of the Public Welfare Platform, which results in losses of expected matching value. Furthermore, declines in AI technology costs and reduced external selection value of suppliers will drive platforms to raise their AI investment intensity. This research provides theoretical foundations for optimising AI strategies in online technology transfer service platforms and informing targeted government interventions. Full article

Digital game consumption increasingly unfolds across video platforms, comment sections, and community discussions, where platform visibility, creator-mediated information, interaction metrics, and commercialization signals shape users’ expectations. In platform-mediated digital commerce, visible interaction may indicate information use, cultural resonance, payment concern, or consumption-related complaint rather than uniformly positive engagement. Using self-determination theory as a motivational lens within a platform-mediated consumer-behavior framework, this study examines whether platform content cues, public comment responses, and user perceptions provide convergent evidence on differentiated engagement meanings. The empirical setting is Bilibili content related to the Chinese wuxia role-playing game Where Winds Meet. The analysis combines 1164 public videos, 19,919 hot comments, and a content-exposure-anchored survey of 564 valid respondents. The results show differentiated patterns: functional information cues correspond to saving-oriented engagement and useful responses; cultural-aesthetic cues correspond to supportive interaction and cultural responses; and payment-mechanism and experience-problem cues correspond to payment concerns and complaints. The survey further shows that perceived information value, cultural/experiential connection, perceived monetization fairness, consumer autonomy in spending decisions, and perceived monetization risk are associated with continued engagement intention. These findings suggest that engagement quality should be interpreted through platform-mediated consumer relationships rather than interaction volume alone, while recognizing that hot-comment evidence reflects a platform-visible layer of user response rather than the full distribution of comments or player attitudes. Full article

To address the issues of unclear risk-coupling mechanisms and the subjective-objective imbalance in evaluation weights for underground gas storage, this paper proposes an assessment method integrating network analysis with game-theory-based fusion weighting. A comprehensive geology–wellbore–surface–auxiliary whole-system risk inventory is first established. Meanwhile, the cross-system risk conduction network is analyzed based on the identification of material, energy, and information flows among subsystems. Subsequently, fault tree analysis (FTA) and expert risk scoring (ERS) are integrated to form a coupling network-guided game theory-based weighting model (CN-GT). This mechanism introduces a game-theoretic deviation-minimization model to reconcile conflicts between subjective and objective information sources and explicitly incorporates risk conduction paths into the weight-aggregation process to quantitatively correct cross-system coupling effects. A case study is conducted at the Xiangguosi gas storage facility. Results from ablation experiments and benchmark method comparisons demonstrate that the cross-system coupling effect is significant; the weight of the risk factor “systemic risk caused by improper compressor operation” ranks first after integration, a contribution severely underestimated by traditional methods. Furthermore, the risk prioritization clearly identifies wellbore integrity and critical equipment reliability as the primary control points. This study provides a quantifiable and decision-support tool for the systematic risk management and control of gas storage. Full article

This paper addresses the critical challenge of identifying cost-effective coalitions of social media influencers to promote environmental sustainability (ES) messages under budget constraints. Traditional influencer marketing often relies on heuristics that ignore relational dependencies and heterogeneous agent costs, leading to redundant coverage and suboptimal resource allocation. To overcome these limitations, we introduce a novel Relational Cooperative Game (RG) framework that formalizes pre-determined dependencies among influencers and followers using closure operators, enabling a portfolio of polynomial-time identification algorithms for Minimal Winning Coalitions (MWCs). We further extend this model to the Weighted Cost-Relational Game (WCRG) to optimize campaigns with heterogeneous influencer costs. We prove that finding a Minimum-Cost Winning Coalition (MCWC) is NP-hard via reduction from weighted set cover and propose two complementary algorithms: (1) a Greedy Cost–Benefit (GCB) algorithm that operates in polynomial time and empirically achieves optimal solutions across all tested instances; a logarithmic approximation guarantee is established for the restricted single-antecedent model; and (2) an Integer Linear Programming (ILP) formulation enhanced with Strongly Connected Component (SCC) preprocessing to handle cyclic dependencies and yield exact optimal solutions for moderate instances. Extensive empirical validation, ranging from a representative six-agent cyclic scenario to large-scale synthetic networks (up to 300 agents), confirms the framework’s robustness and scalability. Results demonstrate that GCB consistently achieves optimal solutions (approximation ratio = 1.000×) with subsecond runtime (<0.2 s) and minimal memory overhead (<50 MB), while ILP-SCC leverages graph condensation for rapid exact solving. Compared to size-based baselines, WCRG achieves up to 95.2% cost savings by systematically leveraging cost-efficient micro-influencers, empirically validating that minimizing coalition size does not guarantee cost efficiency. These findings establish WCRG as a scalable, budget-aware optimization toolkit for maximizing the impact of sustainability campaigns through relational coalition design. Full article

Open-vocabulary object detection (OVD) fails at attribute-level discrimination: when instances share a class label yet differ in color, material, or texture, category names provide no appearance-specific cues. Prior attempts to enrich text inputs with LLM-generated descriptions are limited by single-model distribution bias, producing coverage gaps and unstable attribute quality. We propose a Concept Expander framework built on cooperative multi-LLM game theory. Three heterogeneous LLMs generate candidate attributes in parallel; a cooperative Nash equilibrium then selects the final subset by maximizing each model’s minimum utility gain, jointly enforcing semantic quality and cross-source diversity without amplifying any single model’s bias. The resulting Concept Repository contains approximately 5000 discriminative visual priors. A lightweight retrieval module injects the top-k matched attributes into region-level visual features via residual fusion, preserving CLIP’s pretrained alignment while enriching instance representations with fine-grained semantic priors. A semantic consistency loss anchors enhanced features to ground-truth class semantics throughout training. On LVIS, rare-category APr rises from 22.2 to 28.5; on RefCOCO, attribute-conditioned localization accuracy reaches 54.8, confirming that structured multi-LLM semantic priors improve discrimination across long-tail and high-confusion benchmarks. Full article

Recidivism prediction is increasingly embedded in criminal justice decision-making, yet most deployed systems remain opaque and have been shown to exhibit discriminatory behavior against certain demographic groups. This paper presents a fairness-aware interpretable framework for recidivism prediction applied to three real-world datasets from Bulgaria, Greece, and Portugal. The classification core relies on a 1-Dimensional Convolutional Neural Network (1D-CNN), trained by a custom objective function that embeds the Equalized Odds fairness criterion as an L1-regularized penalty reflecting on gender-based disparities in false positive and false negative rates. Model-level interpretability is provided through Kernel SHAP, which decomposes individual predictions into additive feature attributions grounded in cooperative game theory. Experiments across prediction tasks, each evaluated over randomized runs, demonstrate that the baseline model exhibits statistically significant bias against the female group in all datasets. The fairness-constrained model substantially reduces these disparities across all tasks at a moderate and expected cost to classification accuracy. Kernel SHAP analysis reveals the relative contribution of static and dynamic offenders’ attributes to individual risk scores, supporting auditability and contestability. The proposed framework advances the integration of predictive performance, algorithmic fairness, and structural interpretability in criminal justice analytics. Full article

In online retail, consumers cannot experience product quality before purchase. With the adoption of artificial intelligence (AI), platforms can certify product quality information. However, stronger platform certification may reduce sellers’ incentives to disclose and limit personalized information such as product fit. This study examines the conditions under which a platform should adopt AI-assisted platform certification (AIPC). We develop a game-theoretic model with one platform and two competing sellers. We compare the case of not adopting AIPC with adopting AIPC, and examine how AIPC affects seller disclosure, pricing, and profits. Sellers decide whether to disclose product information and set prices. Consumers update their quality beliefs based on seller disclosure and platform labels. Our results show that AIPC is not always the preferred strategy. When product-fit information spillovers between competing sellers are strong, the platform may be better off not adopting AIPC. When information spillovers are weak, AIPC adoption depends on consumers’ prior belief regarding product quality. Specifically, when consumers have a low prior belief that an uncertified or undisclosed product is of high quality, AIPC benefits the platform and sellers but reduces consumer surplus. When this prior belief is sufficiently high, AIPC creates a win–win–win outcome for the platform, sellers, and consumers. Full article

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

Inverters are widely applied in aviation, distributed power grids, and vehicles, where their health status directly impacts the stable operation of entire systems. Existing health assessment methods suffer from poor real-time performance, require additional measurement circuits, and are prone to misjudgment, while failing to adequately address slow degradation behaviors during inverter operation. To address these challenges, this study proposes an inverter health assessment method based on an improved D-S evidence theory. First, based on the practical requirements of subway auxiliary inverters, 13 key evaluation indicators were selected. Subjective weights were obtained using the Analytic Hierarchy Process (AHP), while objective weights were derived through the Critic method, credibility, and falsity weighting. These were then fused using game theory to obtain composite weights. Next, after data normalization, a ridge-type membership function was employed to describe health state uncertainty. Finally, the improved D-S evidence theory integrates multi-source information to achieve online health status assessment. Experimental validation demonstrates that this method effectively evaluates the impact of IGBT failures, sensor malfunctions, and capacitor–inductor degradation on the inverter. It exhibits strong robustness under DC voltage fluctuations and load variations, enabling real-time output of health scores and grades to provide a reliable basis for maintenance decisions. Full article

Scientific theories survive on institutional fitness, not empirical merit alone. Under Soviet Stalinism, Vygotsky and Luria’s cultural-historical psychology was suppressed while Leontiev’s Activity Theory flourished because it aligned with Marxist-Pavlovian materialism. A game-theoretic framework formalizes this dynamic through three coupled mechanisms: a researcher utility function (U_r = αT + βR − γC), a state utility function (U_s(e) = δI(e) − εD(e) − κ(e)), and a replicator dynamic for institutional selection. Under sufficiently high punishment coefficients, the unique Nash equilibrium is aligned with the ideologically safe theory regardless of empirical truth, and the replicator dynamics drive empirically stronger theories to extinction in the institutional population. Classical findings on conformity and obedience from Sherif, Asch, Festinger, Schachter, and Milgram supply the foundations for the model’s parameters. This pattern—termed here as epistemological selection pressure—explains the Vygotsky case. Because the model assumes severe punishment, active enforcement, complete information, and a binary choice, it applies most directly to authoritarian science; contemporary liberal institutions correspond to the low-punishment regime in which the same model predicts that empirical merit can prevail, so the mechanism is expected to recur only in attenuated form within specific high-pressure domains where scientific truth and institutional power remain entangled. Full article

Energy communities (ECs) are becoming a key institutional instrument for decentralizing the European energy transition, yet their implementation remains constrained by fragmented legal interpretation, uneven national transposition, and unresolved socio-technical coordination problems. This review synthesizes the peer-reviewed literature, EU primary legal texts, and national legislation to clarify the distinction between Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs), alongside the amendment relationship between the RED II and RED III directives. The analysis demonstrates that the scalability of these initiatives depends less on theoretical legal recognition and more on aligning operational frameworks, including metering, settlement, cybersecurity, and equitable allocation rules. The Romanian case illustrates this challenge clearly: rapid prosumer growth creates valuable distributed generation but also exposes physical grid constraints, asymmetric socio-economic participation capacity, and weak experience with cooperative energy governance. To address these vulnerabilities, this paper contributes a focused analytical framework linking energy justice, peer-to-peer game-theoretic modeling, and the strategic integration of “anchor-prosumers.” The study argues that larger renewable self-consumers can act as stabilizing community anchors when internal energy prices are designed between wholesale export values and retail import prices, thereby improving both producer incentives and consumer affordability. Future research developments, including targeted surveys and longitudinal empirical validations, will sustain this claim and optimize the socio-economic resilience of decentralized energy markets. Full article

Numerous construction enterprises have insufficient efficiency in resource utilization for construction and demolition waste (CDW), restricting global circular economic development. How to improve resource utilization has become an urgent problem. While existing studies have extensively explored operational decisions in CDW resource supply chains, insufficient attention has been given to construction enterprises’ information misreporting and its interaction with on-site conversion efficiency. This paper aims to elucidate the mechanism of action of misreporting and systematically analyzes its effects on the pricing decisions of the CDW supply chain. Drawing on information misreporting theory, this study constructs a Stackelberg game model involving construction firms and recycled building materials manufacturers, and compares supply chain decision-making behaviors under two scenarios: information misreporting and honest disclosure. The main conclusions are as follows: (1) misreporting alters recycled building material pricing and profit distribution by affecting manufacturers’ supply capacity expectations; (2) higher on-site conversion efficiency enhances CDW treatment ability and affects stakeholders’ profits; and (3) misreporting is related to on-site conversion efficiency and onsite conversion costs—enterprises prefer misreporting for short-term gains under low on-site conversion efficiency or high costs, while higher on-site conversion efficiency makes truthful disclosure conducive to long-term stable returns. This paper reveals the CDW supply chain decision-making mechanism from enterprises’ perspective, providing a new theoretical basis and practical value for CDW utilization and supply chain optimization. Full article

Background: Standard consumer theory treats preferences as fixed primitives and demand as the solution to an individual optimisation problem; we instead model consumption styles as heritable strategies whose prevalence is shaped by selection and experimentation, and ask when status competition produces an over-consumption trap. Methods: We embed a reference-dependent payoff—private utility concave in own consumption, a positional benefit proportional to consumption relative to the social mean, a financial-fragility cost, and a loss-averse relative-deprivation term—into replicator–mutator dynamics over three strategies (frugal, balanced, conspicuous). Results: Status concern induces strategic complementarity, so that a rising consumption norm penalises moderate consumers and makes imitation self-reinforcing. For intermediate status weight, the system is bistable: an efficient balanced equilibrium and a Pareto-inferior conspicuous trap are separated by a tipping threshold, and the width of the bistable window equals the deprivation weight, producing hysteresis in the consumption norm. The trap persists even though the positional benefit nets to zero in any monomorphic state. Mutation—behavioural experimentation—shrinks the bistable window and can dissolve the lock-in. Conclusions: Reference-dependent demand is better captured by evolutionary dynamics than by static equilibrium, and positional externalities can lock a population into self-defeating over-consumption that interventions on the deprivation or fragility channel may unlock. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article