Search Results (1,108)

This paper develops a dynamic game-theoretic model to evaluate market competitiveness in industries characterized by price competition and adjustment stickiness. We extend the dynamic oligopoly framework for estimating market competitiveness in the literature from a quantity-setting to a price-setting context with differentiated goods. By deriving the subgame perfect equilibrium in a linear-quadratic structure, we utilize an index analogous to the price conjectural variation to measure market competitiveness with differentiated goods. The model is applied to the Chinese retail oil market, and we find that the Chinese retail oil market, particularly dominated by two state firms, exhibits characteristics close to a collusive benchmark within the maintained model. The dynamic game model provides a tractable analytical tool for antitrust authorities to monitor strategic coordination in dynamic environments where price transparency or regulation may facilitate tacit coordination of pricing behavior to a high degree. Full article

Shore-side electricity (SSE) is an effective approach to reduce the emissions of greenhouse gas (GHG) and air pollutants from at-berth ships. Governments are using subsidies to incentivize ports and vessels to use SSE. However, its utilization remains limited. This paper targets the problem of government subsidy optimization considering games between the government and ports. A two-stage tripartite game model and four subsidy scenarios are proposed to investigate the interactions between the government and two ports. The results show that the choice of subsidy recipients does not affect the overall effectiveness of the subsidies. The optimal unit subsidy should be linked to the environmental benefits of GHG reduction, electricity prices, and fuel oil prices. Port competition can further enhance SSE utilization and environmental performance. Thus, policies should encourage such competition. Furthermore, analysis indicates that the cost of enhancing SSE quality is a key factor affecting SSE performance. Based on the analytical findings, this study offers policy recommendations for designing effective subsidy schemes. Full article

This study examines how managerial overconfidence affects corporate cybersecurity investment and whether a breach-contingent regulatory penalty can mitigate behaviorally induced underinvestment. This study develops a behavioral game-theoretic model in which a firm chooses preventive cybersecurity investment and remedial cybersecurity investment, whereas a strategic attacker chooses attack effort, under three scenarios: rational decision making, managerial overconfidence, and managerial overconfidence with market competition. The results show that managerial overconfidence reduces cybersecurity investment by distorting perceptions of breach probability and breach losses. Specifically, breach-probability overconfidence mainly reduces preventive cybersecurity investment and increases attack effort, whereas underestimation of breach losses reduces both preventive cybersecurity investment and remedial cybersecurity investment. In addition, market competition has a conditional effect: it can strengthen preventive cybersecurity investment when managerial bias is mild but weaken it when managerial bias is strong. This study contributes by distinguishing two channels of managerial bias, identifying a conditional competition paradox, and clarifying the bounded corrective role of the breach-contingent regulatory penalty. Full article

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions. Full article

Carbon emission regulations and customers’ green preferences require ports and shipping companies to develop green services, but green investments entail significant costs. Vertical alliance cooperation between ports and shipping companies through sharing costs can address this issue. Most studies use non-cooperative game to analyze the competitive relationship between ports and shipping companies. Although such research can capture price competition, they struggle to address the distribution of cooperative benefits within an alliance. They also fail to simultaneously reflect the coexistence of competition and cooperation. So, we constructed a non-cooperative–cooperative biform game to analyze green investment under vertical alliance. In the non-cooperative stage, the model captures vertical price competition between ports and shipping companies, as well as horizontal competition among supply chains. In the cooperative stage, the Shapley value is used to allocate the coalition profits from green investment cooperation. The results indicate that alliance cooperation can promote the green development of shipping. Moderate green competition can promote the green development of shipping. Route substitution competition will increase service prices and green investment level and reduce the cost-sharing ratio for shipping companies. Port congestion prompts ports to increase green investment level. These findings offer references for the green collaborative development of ports and shipping companies across different countries, thereby enriching the research framework for global sustainable development in shipping. Full article

This study examined whether different body fat percentages (BF%) may influence performance, physiological responses, and fatigue in professional soccer players during a simulated soccer game protocol on a treadmill. Twenty professional male soccer players were categorized in higher (HBF%) and lower (LBF%) body fat percentage groups [HBF% > 11.5%; n = 11, BF% = 14.2 ± 2, LBM = 65.3 ± 8 kg, age = 22.7 ± 4 years, height = 177 ± 7 cm, weight = 76 ± 9 kg, $\dot{V}$O₂max = 60.1 ± 4.5]; [LBF% < 11.5%, n = 9; BF% = 8.1 ± 1, LBM = 65.9 ± 5 kg, age = 20.1 ± 3 years, height = 179 ± 4 cm, weight = 72 ± 5 kg, $\dot{V}$O₂max = 61.6 ± 4). Players underwent a simulated soccer game protocol on a treadmill. Cardiometabolic and hormonal responses, and fuel oxidation and performance, were evaluated. At baseline, apart from the BF% variable (p < 0.0001), the groups did not differ in any other physiological or physical characteristic (p > 0.05). There were no differences between the groups in any performance or biological parameters evaluated (p > 0.05), except for plasma glucose, which was higher in the HBF% group at rest and during the soccer game protocol (p < 0.05). In conclusion, the theory of a uniform ideal (~10 ± 2%) of BF% in elite soccer is not supported by the present study. This study suggests that when muscle mass and fitness levels of the soccer players are maintained at high levels during the competitive period, BF% represents a highly individualized characteristic rather than a uniform target across players. However, a higher BF% increased resting and exercising blood glucose concentrations, even in highly trained professional soccer players, without concomitant effects on metabolism or fuel oxidation during match play. Full article

To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and temporal constraints. To tackle the resulting high-dimensional, nonconvex problem, we introduce a multi-strategy improved stellar oscillation optimizer (MISOO), establishing a closed-loop synergistic system through three coupled stages: (i) evolutionary game-theoretic strategy competition via replicator dynamics for adaptive exploration–exploitation balance; (ii) intuitionistic fuzzy entropy (IFE)-driven dimension-wise parameter control, where IFE calibrates global exploration intensity while dimension-specific crossover probabilities accommodate heterogeneous convergence; and (iii) memory-driven differential escape mechanisms modulated by historical memory parameters to evade local optima. Cross-stage coupling through IFE ensures state information flows across the “strategy selection-refined search-dynamic escape” pipeline. Coupled with a dual-layer encoding scheme, this framework ensures efficient feasible search. Ablation studies validate each mechanism’s contribution. Evaluations on CEC2017 benchmarks demonstrate MISOO’s superior convergence against six metaheuristics. Large-scale earthquake rescue simulations confirm that EC-HUTA/MISOO strictly adheres to nonlinear energy constraints while enhancing task completion and temporal compliance. These results validate the framework’s efficacy for time-critical emergency resource allocation. Full article

This study aimed to identify structural differences in shot characteristics between world-class badminton players and regional collegiate players using a hierarchical comparative framework. Match data were collected from top-ranking players in the Badminton World Federation (BWF) World Series and from regional university league players. All shots were recorded using a custom VBA-based notational analysis system, including player identity, court position, shot type, and rally outcome. Statistical analyses were conducted using chi-square tests with residual analysis and logistic regression modeling incorporating competitive level and tactical patterns. The results revealed that BWF players exhibited significantly lower error rates and higher proportions of building shots, indicating superior rally stability and tactical consistency. In contrast, collegiate players demonstrated higher variability in performance, including both higher ace rates and error rates. These findings suggest that world-class performance is characterized by the ability to sustain rallies while minimizing errors, rather than relying solely on offensive success. Although effect sizes were relatively small and the predictive performance of the regression model was modest (AUC = 0.53), the analysis successfully captured structural differences in tactical patterns between competitive levels. This supports the value of the model as a tool for understanding game dynamics rather than prediction. From a theoretical perspective, the findings align with the view of sport performance as a dynamic, self-organizing system, where outcomes emerge from interactions between players. Practically, the results suggest that improving defensive stability, reducing errors, and maintaining rally continuity are critical for achieving higher competitive performance. This study demonstrates the usefulness of a hierarchical comparative approach for bridging the gap between domestic and international performance standards and provides a foundation for future data-driven research in badminton. Full article

To address the challenges posed by multiple uncertainties in modern power systems to the market bidding of Virtual Power Plants (VPPs), this paper proposes an adaptive bidding strategy based on Deep Reinforcement Learning (DRL). First, a heterogeneous VPP aggregation model integrating dedicated energy storage, Vehicle-to-Grid (V2G), and flexible loads is constructed, incorporating complex physical and operational constraints. Second, to overcome the “myopic” local optimality problem of traditional DRL in temporal arbitrage tasks, a potential-based reward shaping mechanism linked to future price trends is designed to guide the agent toward long-term optimal strategies. Finally, multi-dimensional comparative experiments and mechanism analyses are conducted in a simulated day-ahead electricity market. Simulation results demonstrate the following: (1) The proposed algorithm exhibits robust convergence stability and effectively handles stochastic noise in market prices and renewable generation. (2) Economically, the strategy significantly outperforms the rule-based strategy and remains highly competitive with the deterministic-optimization benchmark under perfect-information assumptions. (3) Mechanism analysis further reveals that the DRL agent breaks through the rigid logic of fixed thresholds, learning a non-linear dynamic game mechanism based on “Price-SOC” states, thereby achieving full-depth utilization of energy storage resources. This work provides an interpretable data-driven paradigm for intelligent VPP decision-making in uncertain environments. Full article

This paper investigates the problem of contract coordination in a two-tier multi-unit auction supply chain consisting of a seller and an auction house. We theoretically show that the conventional commission-based mechanism distorts the transmission of demand information from the demand side to the supply side, thereby preventing effective supply chain coordination. In contrast, guaranteed auction contracts can achieve coordination under both cooperative and non-cooperative game frameworks. Under the cooperative game setting, profits are allocated according to a Nash bargaining solution, in which each party receives its disagreement payoff and a bargaining-power-weighted share of the surplus, with risks and returns being allocated symmetrically. Under the non-cooperative game setting, the supply chain leader can appropriate a larger share of the total profit while bearing relatively lower risk. These results indicate that, as the supply chain leader, the auction house can select different cooperation modes under guaranteed auction contracts according to its bargaining position, but profit allocation should be benchmarked against the cooperative game outcome in order to enhance the long-term competitiveness and stability of the supply chain. Full article

Mental fatigue (MF) has been hypothesized to contribute to injury risk in athletes, but observational studies have not directly investigated this relationship. Therefore, the current study evaluates potential relationships between mental fatigue and subsequent injury occurrence in basketball. Using an observational design, we monitored fourteen male semi-professional basketball players (age: 22 ± 4 years; stature: 192.6 ± 8.8 cm; body mass: 85.5 ± 9.1 kg; Tier 3) from a single team for 21 weeks throughout the competitive season. Each week, the players participated in 5 team-based training sessions, 2–4 individual training sessions, and 1–2 official games. Subjective MF ratings were collected using 100 mm visual analogue scales twice a week (the day before and after the official game) and then averaged. Time-loss injuries were registered, noting the body location, mechanism, and context (training and games). Generalized logistic mixed models were employed to evaluate whether MF levels were associated with injury occurrence in the subsequent 1, 3, and 5 days and 1, 2, 3, and 4 weeks of basketball activity. A total of 11 injuries were registered during the study (7.40 per 1000 h of basketball activity), with an average time loss of 12 ± 19 days. There were no associations between MF and injury occurrence in the following 1, 3, 5 days nor 1, 2, 3, 4 weeks (all p > 0.05, odds ratios: 1.00–1.28). In male semi-professional basketball settings, preliminary evidence indicates that MF might not be associated with injury occurrence. However, due to the dearth of injury events, the statistical power of this study is insufficient to detect potential small–medium effects. Therefore, the current results should be considered exploratory as opposed to a definitive rejection of the hypothesis. Future studies should evaluate the relationship between MF and injury risk in larger samples and among professional athletes. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

Eigenvector-based centrality captures recursive notions of importance in networks. While the direct problem computes centrality from given edge weights, the inverse eigenvector centrality problem seeks edge weights that reproduce a prescribed centrality profile; for directed multigraphs, this inverse task is typically non-unique and depends on the admissible arc structure. We study the direct and inverse problems on directed multigraphs and derive an explicit linear characterization of the set of admissible edge-weight vectors that are compatible with a given centrality target. On this feasible set, we formulate a generalized Nash equilibrium problem with shared centrality constraints, in which multiple agents select edge weights to maximize economically interpretable payoffs that incorporate arc-level competition effects. We provide conditions under which the induced game admits a concave potential function, yielding equilibrium existence and, under standard strict concavity assumptions, uniqueness. Finally, we illustrate the model on an airport network where nodes represent airports and parallel arcs represent airline-specific routes, showing that equilibrium selection produces a feasible and interpretable weight configuration that preserves the prescribed centrality. Full article

This article reexamines Paul’s use of athletic imagery in 1 Corinthians 9:24–27 within the broader argument of chapters 8–10. Against readings that treat the passage as a call to individual moral striving or competition for salvation, this study situates Paul’s metaphor within the honor–shame dynamics of Greco-Roman Corinth and his own defense of apostolic self-restraint. Paul’s “race” and “imperishable wreath” do not exhort believers to outperform one another but dramatize the paradox of freedom expressed through voluntary limitation. Drawing on insights from social-scientific and rhetorical criticism, the essay demonstrates that Paul’s imagery functions as the rhetorical climax of the section, translating his ethical argument into the moral grammar of the agon. By reconfiguring the contest from rivalry to service, Paul transforms the competitive ethos of Corinth into a vision of communal flourishing in which believers “compete” for the good of others. The passage thus offers a distinctly Pauline theology of self-control as the discipline of love, turning the agonistic spirit of the games into an image of the gospel itself. Full article

Competitive eSports impose substantial cognitive workload, yet performance evaluation still emphasizes post-match statistics without considering players’ cognitive states. We reviewed 30 papers that recorded physiological signals using sensors and utilized machine learning (ML) for predicting cognitive states and/or game performance. Findings showed that cardiovascular monitoring (heart rate variability/HRV) was the most prevalent modality (20/30 studies), followed by oculometry (10), electrodermal activity/EDA (9), and electroencephalogram/EEG (5); however, no standardized protocols (device/pre-processing/feature subset) were observed across HRV studies despite it being the most common measure. The best outcomes per construct (measure, accuracy) were: mental workload (pupillometry, ~82%), stress/arousal (EDA, p < 0.001), cognitive fatigue (pupil diameter/EEG, ~88%), expertise (EEG, ~92%), and tilt (EDA/HRV/eye-tracking, ~82–87%). Notably, current studies used small samples and were gender-imbalanced, while ML studies often lacked cross-validation. Only 2 of 30 studies examined flow state—a mental state of optimal performance characterized by total immersion and effortless execution—and interestingly, HRV showed decreases during stress/workload but increases during flow, suggesting context-dependent autonomic regulation. To address this gap, a new framework for flow detection is presented. This review will be of interest to game developers, eSports players, and coaches, and the reported findings may help towards improving player experience and game performance. Full article