Search Results (115)

In an era of rapid technological advancement—particularly with the accelerated development of artificial intelligence and digital technologies—entrepreneurship enables firms to dynamically adjust their strategies in response to environmental uncertainty and helps them maintain sustainable competitive advantages over time. As a key concept in entrepreneurship research, entrepreneurial orientation (EO) has long attracted scholarly attention. However, existing studies on EO have primarily focused on its specific outcomes, while insufficient attention has been paid to its antecedents from the perspective of internal threats. Under the threat of performance shortfalls, firms’ strategic choices are influenced not only by resource constraints but also by managerial cognitive biases. Drawing on Behavioral Theory of the Firm, we explore the moderating roles of managerial overconfidence and myopia in the relationship between performance shortfalls and EO. This study aims to uncover the cognitive “black box” behind why some firms are more likely to trigger entrepreneurial behavior in adverse situations. Based on panel data from 2822 A-share listed companies in China spanning the period from 2009 to 2020, and using a fixed-effects regression model, our findings indicate that both historical and social performance shortfalls have significant positive effects on EO. Further analysis reveals that the positive impact of performance shortfalls on EO is attenuated under conditions of heightened managerial overconfidence and myopia. By enriching the boundary conditions of EO from a cognitive perspective, this study provides a theoretical explanation for how firms can engage in entrepreneurial behavior under threat by reducing cognitive biases, thereby offering both theoretical and managerial insights into how firms can maintain sustainable development under crisis conditions. Full article

Traditional neuroticism assessments primarily rely on self-report questionnaires, which can be difficult to implement in highly confrontational scenarios and are susceptible to subjective biases. To overcome these limitations, this study develops a machine learning-based approach using behavioral data to predict an opponent’s neuroticism in competitive environments. We analyzed behavioral records from 167 participants on the MiaoSuan Wargame platform. After data cleaning and feature selection, key behavioral features associated with neuroticism were identified, and predictive models were developed. Neuroticism was assessed using the 8-item neuroticism subscale of the Big Five Inventory. Results indicate that this method can effectively infer an individual’s neuroticism level. The best-performing model was LinearSVR, which balances interpretability, robustness to noise, and the ability to capture moderate nonlinear relationships—making it suitable for behavior-based psychological inference tasks. The correlation between predicted scores and self-reported questionnaire scores was 0.606, the R-squared value was 0.354, and the test–retest reliability was 0.516. These behavioral features provide valuable insights into neuroticism prediction and have practical applications in psychological assessment, particularly in competitive environments where conventional methods are impractical. This study demonstrates the feasibility of behavior-based neuroticism assessment and suggests future research directions, including refining feature selection techniques and expanding the application scenarios. Full article

Climate change risk has significant impacts on corporate financial activities. Using firm-level data from A-share listed companies in China from 2010 to 2022, we examine how climate risk affects corporate debt financing capacity. We find that climate change risk significantly weakens firms’ ability to raise debt, leading to lower leverage and higher financing costs. These results remain robust across various checks for endogeneity and alternative specifications. We also show that reducing corporate carbon emission intensity can mitigate the negative impact of climate risk on debt financing, suggesting that supply-side credit policies are more effective than demand-side capital structure choices. Furthermore, we identify three channels through which climate risk impairs debt capacity: reduced competitiveness, increased default risk, and diminished resilience. Our heterogeneity analysis reveals that these adverse effects are more pronounced for non-state-owned firms, firms with weaker internal controls, and companies in highly financialized regions, and during periods of heightened environmental uncertainty. We also apply textual analysis and machine learning to the measurement of climate change risks, partially mitigating the geographic biases and single-dimensional shortcomings inherent in macro-level indicators, thus enriching the quantitative research on climate change risks. These findings provide valuable insights for policymakers and financial institutions in promoting corporate green transition, guiding capital allocation, and supporting sustainable development. Full article

As mainstream solvers for black-box optimization problems, evolutionary computation (EC) methods struggle with finding desired optima of lower attractiveness. Researchers have designed benchmark problems for simulating this scenario and proposed a large number of niching methods for solving those problems. However, factors causing the difference in attractiveness between local optima are often coupled in existing benchmark problems, which makes it hard to clarify the primary contributors. In addition, niching methods are carried out using a combination of several niching techniques and reproduction operators, which enhances the difficulty of identifying the essential effects of different niching techniques. To obtain an in-depth understanding of the above issue, thus offering actionable insights for optimization tasks challenged by the multimodality, this paper uses continuous optimization as an entry point and focuses on analyzing differential behaviors of EC methods across different basins of attraction. Specifically, we quantitatively investigate the independent impacts of three features of basins of attraction via corresponding benchmark scenarios generated by Free Peaks. The results show that the convergence biases induced by the difference in distribution only occur in EC methods with less uniform reproduction operators. On the other hand, convergence biases induced by differences in size and average fitness, both of which equate to the difference in size of superior region, pose a challenge to any EC method driven by objective functions. As niching methods limit survivor selection to specified neighborhoods to mitigate the latter biases, we abstract five niching techniques from these methods by their definitions of neighborhood for restricted competition, thus identifying key parameters that govern their efficacy. Experiments confirm these parameters’ critical roles in reducing convergence biases. Full article

This paper examines strategies aimed at improving search procedures in multimodal, low-dimensional domains. Here, low-dimensional domains refers to a maximum of five dimensions. The present analysis assembles strategies to form an algorithm named S-EPSO, which, at its core, locates and maintains multiple optima without relying on external niching parameters, instead adapting this functionality internally. The first proposed strategy assigns socio-emotional personalities to the particles forming the swarm. The analysis also introduces a technique to help them visit secluded zones. It allocates the particles of the initial distribution to subdomains based on biased decisions. The biases reflect the subdomain’s potential to contain optima. This potential is established from a balanced combination of the jaggedness and the mean-average interval descriptors developed in the study. The study compares the performance of S-EPSO to that of state-of-the-art algorithms over seventeen functions of the CEC benchmark, and S-EPSO is revealed to be highly competitive. It outperformed the reference algorithms 14 times, whereas the best of the latter outperformed the other two 10 times out of 30 relevant evaluations. S-EPSO performed best with the most challenging 5D functions of the benchmark. These results clearly illustrate the potential of S-EPSO when it comes to dealing with practical engineering optimization problems limited to five dimensions. Full article

Recently, interest in optimizing judging schemes for large-scale innovation competitions has grown as the complexities in evaluation processes continue to escalate. Although numerous methods have been developed to improve scoring fairness and precision, challenges such as evaluator subjectivity, workload imbalance, and the inherent uncertainty of scoring systems remain inadequately addressed. This study introduces a novel framework that integrates a genetic algorithm-based work cross-distribution model, advanced Z-score adjustment methods, and a BP neural network-enhanced score correction approach to tackle these issues. First, we propose a work crossover distribution model based on the concept of information entropy. The model employs a genetic algorithm to maximize the overlap between experts while ensuring a balanced distribution of evaluation tasks, thus reducing the entropy generated by imbalances in the process. By optimizing the distribution of submissions across experts, our model significantly mitigates inconsistencies arising from diverse scoring tendencies. Second, we developed modified Z-score and Z-score Pro scoring adjustment models aimed at eliminating the scoring discrepancies between judges, thereby enhancing the overall reliability of the normalization process and evaluation results. Additionally, evaluation metrics were proposed based on information theory. Finally, we incorporate a BP neural network-based score adjustment technique to further refine the assessment accuracy by capturing latent biases and uncertainties inherent in large-scale evaluations. Experimental results conducted on datasets from national-scale innovation competitions demonstrate that the proposed methods not only improve the fairness and robustness of the evaluation process but also contribute to a more scientific and objective assessment framework. This research advances the state of the art by providing a comprehensive and scalable solution for addressing the unique challenges of large-scale innovative competition judging. Full article

This study provides a comprehensive survey of the top-performing research papers in the field of time series prediction, offering insights into the most effective machine learning techniques, including tree-based, deep learning, and hybrid methods. It explores key factors influencing the model performance, such as the type of time series task, dataset size, and the time interval of historical data. Additionally, this study investigates potential biases in model development and weighs the trade-offs between the computational costs and performance. A detailed analysis of the most used error metrics and hyperparameter tuning methods in the reviewed papers is included. Furthermore, this study evaluates the results from prominent forecasting competitions, such as M5 and M6, to enrich the analysis. The findings of this paper highlight that tree-based methods like LightGBM 4.6.0 and deep learning methods like recurrent neural networks deliver the best performance in time series forecasting, with tree-based methods offering a significant advantage in terms of their computational efficiency. This paper concludes with practical recommendations for approaching time series forecasting tasks, offering valuable insights and actionable strategies that can enhance the accuracy and reliability of predictions derived from time series data. Full article

Link prediction in bipartite networks is a challenging task due to their distinct structural characteristics, where edges only exist between nodes of different types. Most existing methods are based on structural similarity, assigning similarity scores to node pairs under the assumption that a higher similarity corresponds to a higher likelihood of connection. Local structural methods, in particular, are widely favored for their simplicity, interpretability, and computational efficiency. However, real-world bipartite networks often exhibit highly heterogeneous node degree distributions, which introduce biases and undermine the effectiveness of traditional local structure-based methods. To address this issue, we propose a novel link prediction framework that explicitly adjusts for the degree heterogeneity of intermediate nodes between unconnected node pairs and incorporates their influence within local connection patterns formed around these pairs. Furthermore, our framework differentiates between the roles of same-type and cross-type nodes by leveraging quadrangle graphs between unconnected nodes. This approach allows for a more nuanced capture of unique properties of bipartite networks and effectively mitigates the inherent degree bias commonly observed in such networks, resulting in considerable improvements in prediction accuracy. Experimental results on ten diverse bipartite networks demonstrate that our framework achieves competitive and robust performance compared to nineteen state-of-the-art link prediction methods. Full article

Background: Early-maturing players tend to have physical advantages during formative stages, but it remains unclear whether these advantages translate into long-term professional success. This study examines how biological maturation influences participation and career trajectories in youth football. Methods: Anthropometric and competitive data were collected from 47 players (13.53 ± 1.08 years) in a top-tier academy during the 2010–2011 season. The maturation status was assessed using the Tanner–Whitehouse II RUS method, and the career outcomes were tracked in 2024–2025. Results: Early-maturing players showed higher anthropometric values and greater participation. However, late maturers were more likely to reach professional football (p = 0.003), with all players competing in the top five European leagues belonging to the late-maturing group. Conclusions: Early maturation does not guarantee professional success. Strategies such as bio-banding and personalized training can reduce biases and support talent development, highlighting the need for a more holistic approach to player evaluation. Full article

Stable Q-value estimation is critical for effective policy learning in deep reinforcement learning (DRL), especially continuous control tasks. Traditional algorithms like Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic (TD3) policy gradients rely on Mean Squared Error (MSE) loss for Q-value approximation, which may cause instability due to misestimation and overestimation biases. Although distributional reinforcement learning (RL) algorithms like C51 have improved robustness in discrete action spaces, their application to continuous control remains computationally expensive owing to distribution projection needs. To address this, we propose a classification-based Q-value learning method that reformulates Q-value estimation as a classification problem rather than a regression task. Replacing MSE loss with cross-entropy (CE) and Kullback–Leibler (KL) divergence losses, the proposed method improves learning stability and mitigates overestimation errors. Our statistical analysis across 30 independent runs shows that the approach achieves an approximately 10% lower Q-value estimation error in the pendulum environment and a 40–60% reduced training time compared to SAC and Continuous Twin Delayed Distributed Deep Deterministic (CTD4) Policy Gradient. Experimental results on OpenAI Gym benchmark environments demonstrate that our approach, with up to 77% fewer parameters, outperforms the SAC and CTD4 policy gradients regarding training stability and convergence speed, while maintaining a competitive final policy performance. Full article

This paper studies biased-manager hiring in a market with network externalities and product compatibility. We show that the aggressivity of a biased manager has a non-linear relationship with product compatibility; however, since both owners want to hire aggressive managers, product compatibility is irrelevant to the type of manager the owner hires. In Cournot competition, product compatibility is crucial in alleviating the “prisoner’s dilemma” due to the net network effect of network externalities with product compatibility. In Bertrand competition, the “prisoner’s dilemma” is resolved when the augmented net network effect of product compatibility is large. Full article

This paper proposes new ANOVA-based approximations of functions and emulators of high-dimensional models using either available derivatives or local stochastic evaluations of such models. Our approach makes use of sensitivity indices to design adequate structures of emulators. For high-dimensional models with available derivatives, our derivative-based emulators reach dimension-free mean squared errors (MSEs) and a parametric rate of convergence (i.e., $\mathsf{O}\left(N^{-1}\right)$). This approach is extended to cope with every model (without available derivatives) by deriving global emulators that account for the local properties of models or simulators. Such generic emulators enjoy dimension-free biases, parametric rates of convergence, and MSEs that depend on the dimensionality. Dimension-free MSEs are obtained for high-dimensional models with particular distributions from the input. Our emulators are also competitive in dealing with different distributions of the input variables and selecting inputs and interactions. Simulations show the efficiency of our approach. Full article

Reproductive interactions between species could have negative effects on the fitness of the species involved, which can have important ecological and evolutionary consequences, such as population declines (including local extinction) or character divergence. Here, we report the courtship and attempted mating between two congeneric species of fireflies endemic to Mexico. The interactions involved males of the synchronous firefly Photinus palaciosi and females of the much larger, non-synchronous P. extensus. In the study site, the population density of P. palaciosi is much higher than that of P. extensus. Observations of marked P. extensus females throughout most of the mating season showed that 37.8% of their interactions with males were with P. palaciosi males. Although interspecific interactions were usually of shorter length, they frequently consumed a significant portion of the nightly mate-locating/courting period. These interspecific interactions are probably facilitated by the similarities in the mate location and courtship behavior of both species, which also share female brachyptery (elytra and wing reduction that makes females unable to fly). The simplest hypothesis to explain our behavioral observations is that P. palaciosi males mistakenly courted P. extensus females. The available evidence suggests that the operational sex ratio (OSR) of P. palaciosi is male-biased, as it seems to be the case in all synchronous fireflies studied to date. We hypothesize that the intense male competition for mates resulting from a male-biased OSR explains, at least in part, the “indiscriminate” sexual responses of P. palaciosi males. Another still not studied factor that could contribute to the frequent interspecific sexual interactions observed is the degree of similitude of the mating signals. The relatively high frequency of interspecific interactions and the significant amount of time invested in many of them (relative to the duration of the nightly mating period) indicate that the study of the potential fitness costs (and benefits?) of these interactions is a promising line of research. Full article

Investigating causal interactions between entities is a crucial task across various scientific domains. The traditional causal discovery methods often assume a predetermined causal direction, which is problematic when prior knowledge is insufficient. Identifying causal directions from observational data remains a key challenge. Causal discovery typically relies on two priors: the uniform prior and the Solomonoff prior. The Solomonoff prior theoretically outperforms the uniform prior in determining causal directions in bivariate scenarios by using the causal independence mechanism assumption. However, this approach has two main issues: it assumes that no unobserved variables affect the outcome, leading to method failure if violated, and it relies on the uncomputable Kolmogorov complexity (KC). In addition, we employ Kolmogorov’s structure function to analyze the use of the minimum description length (MDL) as an approximation for KC, which shows that the function class used for computing the MDL introduces prior biases, increasing the risk of misclassification. Inspired by the insufficient but necessary part of an unnecessary but sufficient condition (INUS condition), we propose an asymmetry where the expected complexity change in the cause, due to changes in the effect, is greater than the reverse. This criterion supplements the causal independence mechanism when its restrictive conditions are not met under the Solomonoff prior. To mitigate prior bias and reduce misclassification risk, we introduce a multilayer perceptron based on the universal approximation theorem as the backbone network, enhancing method stability. Our approach demonstrates a competitive performance against the SOTA methods on the TCEP real dataset. Additionally, the results on synthetic datasets show that our method maintains stability across various data generation mechanisms and noise distributions. This work advances causal direction determination research by addressing the limitations of the existing methods and offering a more robust and stable approach. Full article

This study uses Q methodology to examine the perception types of humanities majors during their employment preparation process. With the rapid advancement of artificial intelligence (AI) and automation technologies, traditional career paths for humanities majors are shrinking, leading to intensified job mismatches, psychological anxiety, and social bias. The study identified four perception types: (1) Social Support for Career Challenges, which emphasises the need for emotional and institutional support to overcome career-related anxiety and biases, (2) Building Practical Career Skills, which focuses on enhancing employability through practical job experience and technical skill development, (3) Graduation-related Career Constraints, which highlights the limitations caused by academic graduation requirements, calling for structural reforms and expanded certification support, and (4) Proactive Job Preparation, which reflects active efforts to adapt to technological advancements and competitive job market demands by emphasising digital skill acquisition and practical education. We analyse each type’s characteristics and support needs, offering valuable insights into how to address these challenges. The findings provide policy implications for career guidance and employment support, aiming to improve the employment success rates and job stability of humanities graduates. By offering empirical evidence for tailored support programmes, this study contributes practical recommendations to prepare humanities majors for the evolving job market. Full article