Search Results (36)

Graph pseudo-labeling is an effective semi-supervised learning (SSL) approach to improve graph neural networks (GNNs) by leveraging unlabeled data. However, its success heavily depends on the reliability of pseudo-labels, which can often result in confirmation bias and training instability. To address these challenges, we propose a dual-layer consistency semi-supervised framework (DiPat), which integrates an internal differentiating pattern consistency mechanism and an external multimodal knowledge verification mechanism. In the internal layer, DiPat extracts multiple differentiating patterns from a single information source and enforces their consistency to improve the reliability of intrinsic decisions. During the supervised training phase, the model learns to extract and separate these patterns. In the semi-supervised learning phase, the model progressively selects highly consistent samples and ranks pseudo-labels based on the minimum margin principle, mitigating the overconfidence problem common in confidence-based or ensemble-based methods. In the external layer, DiPat also integrates large multimodal language models (MLLMs) as auxiliary information sources. These models provide latent textual knowledge to cross-validate internal decisions and introduce a responsibility scoring mechanism to filter out inconsistent or unreliable external judgments. Extensive experiments on multiple benchmark datasets show that DiPat demonstrates superior robustness and generalization in low-label settings, consistently outperforming strong baseline methods. Full article

This paper asks whether debt among affluent consumers reflects rational leverage, comparable to firms, or the influence of cognitive biases. Using survey data on Brazilian bank clients, we combine logistic regressions with a finite-mixture-inspired, rule-based classification and a test based on a ten-business-day overdraft grace period to identify heterogeneity in borrowing behavior. In the high-income subsample, Cognitive Reflection Test scores are unrelated to debt incidence, diverging from prior evidence in mixed-income populations. Among indebted affluent respondents, most borrowing is cost-sensitive and consistent with deliberate leverage (about 80 percent), while a minority displays patterns consistent with optimism bias and overconfidence (about 20 percent). The institutional feature of a temporary grace period lowers the effective cost of short-term credit and is associated with a marked reduction in overdraft use, reinforcing the leverage interpretation. Overall, consumer debt is heterogeneous; for the affluent, it largely aligns with leverage, though behavioral biases persist at the margins. Policy for high-income borrowers should prioritize targeted measures that address optimism bias and overconfidence while preserving deliberate leverage management through clear disclosures and monitoring of sensitivity to short-term credit costs. Full article

Cognitive biases have been proved to have a systematic influence on decision-making at both individual and social levels. This study investigated two forms of overconfidence—specifically, overestimation and overplacement—among young individuals participating in a social prediction task using an experimental design. The experimental study was conducted with 414 undergraduate students during an in-class written exam. Participants were drawn from different courses taught by the same instructor and varied in terms of their level of prior interaction with the instructor (having attended one, two, or three semesters). Participants predicted the instructor’s favourite songs and subsequently evaluated the accuracy of their own predictions and those of their classmates. This experimental design allowed the researchers to look at both how accurately participants judged themselves and how they compared themselves to their peers. The analysis primarily focused on discrepancies between self-evaluated and actual performance. Results revealed a consistent pattern of overestimation and overplacement. Participants rated themselves as more successful than they actually were and positioned themselves above the average of their peer group. Moreover, the lack of direct feedback and limited contextual cues in the guessing task created a psychological ambiguity, which may have contributed to an unjustified sense of certainty among the participants. These findings offer empirical insight into the functioning of cognitive biases and contribute to a nuanced understanding of overconfidence in young decision-makers. Full article

Our paper investigates how emotions and cognitive biases shape small investors’ decisions in a bearish market or are perceived as such. Using semi-structured interviews and a focus group, we analyze the behavior of eight management science students engaged in a three-day trading simulation with virtual portfolios. Our findings show that emotions are active forces influencing judgment. Fear, often escalating into anxiety, was pervasive in response to losses and uncertainty, while frustration and powerlessness frequently led to decision paralysis. Early successes sometimes generated happiness and pride but also resulted in overconfidence and excessive risk-taking. These emotional dynamics contributed to the emergence of cognitive biases such as loss aversion, anchoring, confirmation bias, overconfidence, familiarity bias and herd behavior. Emotions often acted as precursors to biases, which then translated into specific decisions—such as holding losing positions, impulsive “revenge” trades or persisting with unsuitable financial strategies. In some cases, strong emotions bypassed cognitive biases and directly drove behavior. Social comparison through portfolio rankings also moderated responses, offering both comfort and additional pressure. By applying a qualitative perspective—not commonly used in behavioral finance—our study highlights the dynamic chain of emotions → biases → decisions and the role of social context. While limited by sample size and the short simulation period, this research provides empirical insights into how psychological mechanisms shape investment behavior under stress, offering avenues for future quantitative studies. Full article

Career decisions are among the most consequential choices individuals make, profoundly shaping their long-term stability and overall life satisfaction. The literature suggests that behavioral biases, specifically overconfidence, herd mentality, social comparison, status quo bias, and optimism bias, can exert considerable influence on these decisions, thereby shaping students’ future career trajectories. This study adopts a behavioral perspective to examine how these biases influence career choices within a collectivist social context. A survey of 360 undergraduate and graduate business students was conducted. The collected data were analyzed using an integrated approach that combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN), enabling the use of both linear and non-linear methods to analyze the relationship between cognitive biases and career choices. Our findings reveal that while all five biases have a measurable impact, status quo bias and social comparison are the dominant factors influencing students’ career decisions. These results underscore the need for interventions that foster self-awareness, independent decision-making, and critical thinking. Such insights can guide educators, career counselors, and policymakers in designing programs to mitigate the negative effects of cognitive biases on career decision-making, ultimately enhancing career satisfaction and workforce efficiency. Full article

Overconfidence is a well-established behavioral bias that involves the overestimation of one’s own capabilities. We introduce a model in which managers and agents exert effort in a joint production, after the manager decides on the allocation of the tasks. A rational manager tends to reduce their own effort by delegating the critical task to the agent more often than in an efficient task allocation. In contrast, an overconfident manager engages in responsibility hoarding, i.e., is likely to delegate a critical task less often to the agent than a rational manager. In fact, a manager with a sufficiently high ability and a moderate degree of overconfidence increases the total welfare by refusing to delegate critical tasks and by exerting more effort than a rational manager. Finally, we derive the conditions under which the responsibility hoarding can persist in an organization, showing that the bias survives as long as the overconfident manager can rationalize the observed output by underestimating the ability of the agent. Full article

The development of stock markets is pivotal for economic growth, particularly through the mobilization of idle resources into productive investments. Despite recent reforms to enhance Uzbekistan’s capital market, public engagement remains limited. This study examines the behavioral determinants of stock market investment intention and participation among university students, employing the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model investigates the influence of digital literacy, financial literacy, social interaction, herding behavior, overconfidence bias, risk tolerance, and financial well-being on investment intention and behavior. A survey of 369 university students was conducted to assess the proposed relationships. The results reveal that risk tolerance, overconfidence bias, and herding behavior significantly and positively affect investment intention, while digital literacy demonstrates a notable negative effect, suggesting caution in assuming technology readiness automatically translates to investment readiness. Investment intention, in turn, strongly predicts actual participation and mediates several of these effects. Conversely, financial literacy, financial well-being, and social interaction showed no significant direct or mediating influence. Additionally, differences according to gender and academic background were observed in how intention translates into behavior. The findings underscore the need for integrated financial and behavioral education to enhance market participation and contribute to policy discourse on youth financial engagement in emerging economies. Full article

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior. Full article

Gender recognition from pedestrian imagery is acknowledged by many as a quasi-solved problem, yet most existing approaches evaluate performance in a within-domain setting, i.e., when the test and training data, though disjoint, closely resemble each other. This work provides the first exhaustive cross-domain assessment of six architectures considered to represent the state of the art: ALM, VAC, Rethinking, LML, YinYang-Net, and MAMBA, across three widely known benchmarks: PA-100K, PETA, and RAP. All train/test combinations between datasets were evaluated, yielding 54 comparable experiments. The results revealed a performance split: median in-domain F1 approached 90% in most models, while the average drop under domain shift was up to 16.4 percentage points, with the most recent approaches degrading the most. The adaptive-masking ALM achieved an F1 above 80% in most transfer scenarios, particularly those involving high-resolution or pose-stable domains, highlighting the importance of strong inductive biases over architectural novelty alone. Further, to characterize robustness quantitatively, we introduced the Unified Robustness Metric (URM), which integrates the average cross-domain degradation performance into a single score. A qualitative saliency analysis also corroborated the numerical findings by exposing over-confidence and contextual bias in misclassifications. Overall, this study suggests that challenges in gender recognition are much more evident in cross-domain settings than under the commonly reported within-domain context. Finally, we formalize an open evaluation protocol that can serve as a baseline for future works of this kind. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

The core characteristic of the bullwhip effect is that upstream companies overproduce or hoard inventory due to information distortion, leading to resource waste and increased carbon emissions, which severely affects the economic, environmental, and social efficiency of sustainable supply chains. This paper investigates the impact of retailers’ cognitive bias, namely, overconfidence, on the bullwhip effect in the sustainable supply chain. It characterizes retailers’ overconfidence from two aspects: overprecision and overestimation. This study finds that retailers’ overestimation biases distort demand forecasts, causing product orders and inventory decisions to significantly deviate from the rational optimal level, exacerbating the bullwhip effect in sustainable supply chains. In contrast, retailers’ overprecision bias reduces the forecast error, which has a mitigating effect on the bullwhip effect on inventory; however, this effect weakens as the level of overestimation increases. Furthermore, order lead time and the autocorrelation coefficient of demand moderate the bullwhip effect. Finally, through numerical simulation analysis, the interactive effects of overconfidence bias and operational parameters are effectively captured, providing strong validation for the theoretical results and research propositions. The conclusions of this study offer valuable managerial insights for mitigating the bullwhip effect of sustainable supply chain caused by irrational factors. It also provides policy recommendations for promoting the theoretical research and practice of sustainable supply chains. Full article

This paper investigates a supply chain setting in which a retailer exhibiting overconfidence invests in demand forecasting. Specifically, the retailer overestimates both the precision of the forecasting signal and the productivity of the investment. We analytically characterize the retailer’s investment behavior and show that overconfidence can lead to overinvestment in forecast accuracy. Beyond the investment decision itself, we examine how overconfidence influences the performance of both supply chain members and the system as a whole. When the retailer withholds forecast information from the supplier, overconfidence tends to harm both the retailer and the overall supply chain. However, under information-sharing arrangements, overconfidence can become beneficial—improving outcomes for the supplier and the system. Notably, when the retailer shares forecast with a sophisticated (strategically responsive) supplier, overconfidence may lead to a win–win outcome, where both parties gain from the retailer’s elevated investment in demand forecasting. These findings offer valuable insights into the conditions under which overconfidence shifts from being a liability to a strategic advantage, enriching our understanding of behavioral factors in supply chain decision-making. Full article

This study aimed to develop the Five Cognitive Biases in Risk-Taking Scale (5 CBR-S) to measure five cognitive biases associated with risk-taking: overconfidence, illusion of control, belief in the law of small numbers, escalation of commitment, and optimism. Firefighters completed a series of five questionnaires: cognitive biases related to risk-taking, emotional intelligence, self-regulation behaviors, personality traits, and mental toughness. Data were collected from two distinct samples, each consisting of 202 firefighters. A series of exploratory and confirmatory factor analyses conducted on an initial version of the 5 CBR-S with 50 items provided structural evidence supporting a 5-factor, 19-item solution. Evidence of validity and reliability for the 5 CBR-S scores was provided by examining correlations with emotional intelligence, personality traits, and mental toughness. Overall, despite certain limitations, the 5 CBR-S constitutes a robust measure, offering the advantage of highlighting the five main cognitive biases related to risk-taking. It can be used both among firefighters and in other professional contexts involving high-intensity emergency decision-making. Full article

This study examines the impact of cognitive biases on soccer player performance in penalty shootouts, focusing on the fairness of two different formats: the current ABAB sequence and the alternative ABBA sequence, modeled after the tennis tiebreak system. We consider the context of a real-world penalty shootout scenario, where each team takes five shots. The study brings attention to a previously overlooked aspect of the fairness debate in soccer, emphasizing the significant impact of cognitive biases on outcomes. Using Monte Carlo simulations, we modeled 10,000 penalty shootouts for each format, incorporating psychological biases such as overconfidence, loss aversion, and social comparison to estimate the likelihood of success for each shot. Our findings indicate that while the ABBA format reduces the first-mover advantage observed in the ABAB format, a slight bias in favor of the first team still persists in the ABBA format. Statistical analyses, including two-sample t-tests and chi-square tests, confirmed that the differences in winning probabilities between the two formats are statistically significant. The study suggests that although the ABBA format offers a more balanced approach, cognitive biases continue to play a critical role in influencing outcomes. These results help players stay focused, manage pressure, and improve performance during high-stakes penalty shootouts, leading to better team outcomes. It also allows coaches to act as decision observers by using a checklist to identify cognitive biases in specific decision-making situations. Full article

The interplay between land management and real estate market dynamics is critical for sustainable development. This study employs behavioral finance theory to explore how irrational behaviors among key market participants, including developers, consumers, and brokers, influence housing prices in China. By examining decision aggregation processes and sociocultural influences, we identify significant behavioral factors such as overconfidence, herding behavior, and availability bias that contribute to real estate price fluctuations. Our empirical analysis, based on data from 2001 to 2018, reveals how these behaviors impact market outcomes and provides insights for improving land administration systems. The findings offer valuable perspectives for policy and strategy development aimed at stabilizing housing markets, promoting sustainable real estate practices, and supporting the achievement of sustainable development goals (SDGs). This research underscores the importance of integrating behavioral finance into land management to enhance the efficiency and security of land tenure systems. Full article