Search Results (647)

Recent advancements in lightweight electroencephalogram(EEG) signal classification have enabled real-time human–robot interaction, yet challenges persist in balancing computational efficiency and safety in dynamic path planning. This study proposes an EEG-based inverse reinforcement learning (EIRL) framework to simulate human navigation strategies by decoding neural decision preferences. The method integrates a pruned WNFG-SSCCNet-ADMM classifier for EEG signal mapping, apprenticeship learning for reward function extraction, and Q-learning for policy optimization. Experimental validation in an 8 × 8 FrozenLake-v1 environment demonstrates that EIRL reduces average path risk values by 50% compared with traditional reinforcement learning, achieving expert-level safety ($\Delta$ = 4) while maintaining optimal path lengths. The framework enhances adaptability in unknown environments by embedding human-like risk aversion into robotic planning, offering a robust solution for applications requiring minimal prior environmental knowledge. Results highlight the synergy between neural feedback and computational models, advancing inclusive human–robot collaboration in safety-critical scenarios. Full article

This study evaluates the utility derived by policyholders in a Danish pension company, from their life insurance coverages. We quantify the relative importance policyholders assign to their existing coverages versus a hypothetical complete coverage scenario, thereby measuring the implicit priority of their current coverage. By analyzing these implicit priorities based on individual attributes such as age, financial situation, and company agreement limitations, we gain a comprehensive understanding of policyholders’ evaluations of their current life insurance coverage. Utilizing a continuous-time life cycle model, we optimize consumption and life insurance decisions during the accumulation phase, applying well-established theoretical findings to actual data. Our analysis identifies trends, outliers, and insights that can inform potential improvements in life insurance coverage. This tool aims to assist policyholders in prioritizing their coverage according to their life situations and provides a foundation for advisory dialogues and product development. Full article

Sexual selection is a major driver of speciation and evolution, with mate choice being a key component. Individuals assess mate quality by integrating various mating cues. The Western mosquitofish (Gambusia affinis), a species exhibiting pronounced sexual dimorphism in body size and secondary sexual traits, serves as an ideal model for studying mate choice. This study examines the impact of mating cues on mate choice in different sexes of G. affinis through a combination of morphological parameter database construction, computer-simulated animations, and dichotomous association preference tests. The results showed that male gonopodium status significantly affects female mate choice. Females exhibited a preference for males with resting-phase gonopodia, suggesting their aversion to forced copulation and sexual harassment in coercive mating systems. Furthermore, males preferred younger females, with this preference being positively correlated with male body size. This suggests that males are sensitive to sperm competition intensity and may base their choice on social rank. Geometric morphometric analysis and simulation experiments showed that males preferred females with larger gravid spots, regardless of age, suggesting that gravid spot size reflects female fecundity. Male preference for younger females with streamlined bodies and smaller abdomens was significant, but body size did not affect mate choice in general. Our findings highlight that female and male G. affinis employ different mate choice strategies, with females prioritizing male harassment avoidance and males considering multiple mating cues, not solely one dominant characteristic, in their mate choice decisions. These findings demonstrate that mate choice in G. affinis involves balancing conflicting preferences for traits associated with reduced harassment risk (e.g., resting-phase gonopodium in males) and those linked to reproductive potential (e.g., large gravid spot in females), highlighting the nuanced decision-making processes in both sexes. Full article

Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. Full article

With Generative AI (GenAI) entering medicine, understanding its decision-making under uncertainty is important. It is well known that human subjective risk appetite influences medical decisions. This study investigated whether the risk appetite of GenAI can be evaluated and if established human risk assessment tools are applicable for this purpose in a medical context. Five GenAI systems (ChatGPT 4.5, Gemini 2.0, Qwen 2.5 MAX, DeepSeek-V3, and Perplexity) were evaluated using Rheumatoid Arthritis (RA) clinical scenarios. We employed two methods adapted from human risk assessment: the General Risk Propensity Scale (GRiPS) and the Time Trade-Off (TTO) technique. Queries involving RA cases with varying prognoses and hypothetical treatment choices were posed repeatedly to assess risk profiles and response consistency. All GenAIs consistently identified the same RA cases for the best and worst prognoses. However, the two risk assessment methodologies yielded varied results. The adapted GRiPS showed significant differences in general risk propensity among GenAIs (ChatGPT being the least risk-averse and Qwen/DeepSeek the most), though these differences diminished in specific prognostic contexts. Conversely, the TTO method indicated a strong general risk aversion (unwillingness to trade lifespan for pain relief) across systems yet revealed Perplexity as significantly more risk-tolerant than Gemini. The variability in risk profiles obtained using the GRiPS versus the TTO for the same AI systems raises questions about tool applicability. This discrepancy suggests that these human-centric instruments may not adequately or consistently capture the nuances of risk processing in Artificial Intelligence. The findings imply that current tools might be insufficient, highlighting the need for methodologies specifically tailored for evaluating AI decision-making under medical uncertainty. Full article

Background/Objectives: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation and incomplete response to conventional therapies. Although biologics have advanced UC management, many patients with moderate-to-severe disease experience treatment failure, relapse, or adverse effects. This review evaluates the pharmacology, efficacy, and safety of oral Janus kinase (JAK) inhibitors—tofacitinib, upadacitinib, and filgotinib—to guide their clinical use in UC. Methods: A comprehensive literature review was conducted using the PubMed, Embase, Cochrane, and Web of Science databases to identify relevant studies on JAK inhibitors in UC. The review included Phase 3 randomized controlled trials (RCTs), real-world observational studies, and recent network meta-analyses. We assessed pharmacologic profiles, clinical efficacy, and safety data for tofacitinib, upadacitinib, and filgotinib. Additionally, we reviewed emerging pipeline agents and future directions in oral immunomodulatory therapy for UC. Results: All three agents demonstrated efficacy in the induction and maintenance of remission. Upadacitinib showed superior performance, including rapid symptom control, high clinical remission rates, and favorable long-term outcomes in both biologic-naïve and -experienced patients. Tofacitinib offered strong efficacy, particularly in early response, but was associated with higher risks of herpes zoster and thromboembolic events. Filgotinib provided moderate efficacy with a favorable safety profile, making it suitable for risk-averse populations. Meta-analyses consistently ranked upadacitinib highest in clinical efficacy and onset of action. Conclusions: JAK inhibitors offer effective and convenient oral treatment options for moderate-to-severe UC. Upadacitinib emerges as a high-efficacy agent; tofacitinib and filgotinib remain valuable based on patient-specific risk profiles. Future studies are needed to clarify optimal sequencing, long-term safety, and the role of emerging agents or combination therapies. Full article

In today’s high-stakes arenas—from healthcare to defense—algorithms are advancing at an unprecedented pace, yet they still lack a crucial element of human decision-making: an instinctive caution that helps prevent harm. Inspired by both the protective reflexes seen in military robotics and the human amygdala’s role in threat detection, we introduce a novel idea: an integrated module that acts as an internal “caution system”. This module does not experience emotion in the human sense; rather, it serves as an embedded safeguard that continuously assesses uncertainty and triggers protective measures whenever potential dangers arise. Our proposed framework combines several established techniques. It uses Bayesian methods to continuously estimate the likelihood of adverse outcomes, applies reinforcement learning strategies with penalties for choices that might lead to harmful results, and incorporates layers of human oversight to review decisions when needed. The result is a system that mirrors the prudence and measured judgment of experienced clinicians—hesitating and recalibrating its actions when the data are ambiguous, much like a doctor would rely on both intuition and expertise to prevent errors. We call on computer scientists, healthcare professionals, and policymakers to collaborate in refining and testing this approach. Through joint research, pilot projects, and robust regulatory guidelines, we aim to ensure that advanced computational systems can combine speed and precision with an inherent predisposition toward protecting human life. Ultimately, by embedding this cautionary module, the framework is expected to significantly reduce AI-induced risks and enhance patient safety and trust in medical AI systems. It seems inevitable for future superintelligent AI systems in medicine to possess emotion-like processes. Full article

This paper proposes a novel robustness framework for studying the unit elasticity of intertemporal substitution (EIS) assumption based on the Perron-Frobenius sieve estimation model by Christensen, 2017. The sieve nonparametric decomposition is a central model that connects key strands of the long run risk literature and recovers the stochastic discount factor (SDF) under the unit EIS assumption. I generate various economies based on Epstein–Zin preferences to simulate scenarios where the EIS deviates from unity. Then, I study the main estimation mechanism of the decomposition as well as the time discount factor and the risk aversion parameter estimation surface. The results demonstrate the robustness of estimating the average yield, change of measure, and preference parameters but also reveal an “absorption effect” arising from the unit EIS assumption. The findings highlight that asset pricing models assuming a unit EIS produce distorted parameter estimates, caution researchers about the potential under- or over-estimation of risk aversion, and provide insight into trends of misestimation when interpreting the results. I also identify an additional source of failure from a consumption component, which demonstrates a more general limit of the consumption-based capital asset pricing model and the structure used to estimate relevant preference parameters. Full article

Risk perception and travel behavior under extreme weather have attracted increasing scholarly attention due to their implications for sustainable transport. This study investigates how perceived risks influence commuters’ travel mode choices during heavy rainfall in Qingdao, China, using data from a pilot survey and a stated choice experiment. A Range-varying Generalized Weberian Regret–Rejoice Model (RGWRRM) is developed to capture nonlinear perceptual sensitivities and decision-making under uncertainty. Results indicate that safety and reliability risks significantly shape travel behavior, with commuters showing heightened loss aversion and increased willingness to pay for safer and more dependable modes. The RGWRRM outperforms traditional utility- and regret-based models, offering deeper behavioral insights. By elucidating the mechanisms linking risk perception to mode shifts, this study contributes to the design of resilient and sustainable urban transport strategies in the face of climate-induced disruptions. Full article

We examine how institutional saving mechanisms influence retirement saving decisions under bounded rationality and income risk. Using a life-cycle experiment with habit formation and loss aversion, we test mandatory and voluntary binding savings under deterministic and stochastic income. Voluntary commitment improves saving performance only when income is predictable; under uncertainty, it fails to improve performance. Mandatory savings do not raise total saving, as participants reduce voluntary contributions. These results emphasize the role of income smoothing in enabling behavioral interventions to improve long-term financial outcomes. Full article

This paper examines the characteristics of banks and their lending behavior in relation to Paycheck Protection Program (PPP) loans and commercial and industrial (C&I) loans to small businesses during the COVID-19 pandemic. Our findings show that lenders facing greater risk tended to lend more PPP loans, consistent with the risk-aversion theory. Specifically, banks with a higher loan–deposit ratio, lower overall profitability, poorer loan quality, and higher exposure to risks in business (C&I) loans are characterized by higher PPP loans. C&I loans to all businesses are negatively related to the loan–deposit ratio and loan loss allowance ratio, but are positively linked with the capital ratio. However, we find important differences in C&I lending to small businesses versus large businesses. Furthermore, there is evidence regarding the success of targeting PPP loans towards more productive sectors of the US economy. Using FDIC-defined banks’ lending specializations, we show that banks focused on international lending had a limited role in PPP lending. Full article

This study investigates how the rise of commission-free FinTech platforms and the introduction of fractional trading (FT) have altered trading behavior and order book dynamics in the NASDAQ equity market. Leveraging high-frequency ITCH data from highly capitalized stocks—AAPL, AMZN, GOOG, and TSLA—we analyze market microstructure changes surrounding the implementation of FT. Our empirical findings show a statistically significant increase in price levels, average tick sizes, and price volatility in the post-FinTech-FT period, alongside elevated price impact factors (PIFs), indicating steeper and less liquid limit order books. These shifts reflect greater participation by non-professional investors with limited order placement precision, contributing to noisier price discovery and heightened intraday risk. The altered liquidity landscape and increased volatility raise important questions about the resilience and informational efficiency of modern equity markets under democratized access. Our findings contribute to the growing literature on retail trading and provide actionable insights for market regulators and exchanges evaluating the design and oversight of evolving trading mechanisms. Full article

The standard expected utility model applied by economists and decision scientists assumes both that decision makers (DMs) are rational and that their information retrieval behavior and choices are determined by the observed and potential values of the multiple characteristics defining the alternatives. In this regard, if DMs can formalize the information acquisition structures determined by the main postulates of expected utility theory, they should also be able to perform standard operations regarding the potential combinatorial outcomes that may be obtained when evaluating the alternatives. We define an information retrieval scenario where DMs account for the different combinatorial possibilities arising among the realizations of the characteristics defining the alternatives before evaluating them. We demonstrate the indifference that arises among risk-neutral DMs endowed with standard expected utilities within sequential information acquisition environments such as those defined by online search engines. We also illustrate the reticence of DMs to acquire information on new alternatives when increasing their aversion to risk or modifying the relative importance assigned to the different characteristics defining the alternatives. The main strategic consequences that follow from the enhanced information retrieval scenario proposed are also analyzed. Full article

Background: Overparenting describes a developmentally inappropriate and excessive parental involvement in a child’s life. It is predominantly measured in contemporary research by using emerging adults’ reports. Objective: The current study briefly reports on the adaptation and validation process of the overparenting short-form scale (OP-SF) with parents of early adolescents. The scale is among the sole instruments for evaluating overparenting within a general setting from the parental perspective. However, to date, its structure and psychometric properties remain untested among parents of pre-adults. Method: A cohort of 316 parents, including 159 mothers and 157 fathers (Mage = 44.07, SD = 5.08), provided online assessments regarding their overparenting behaviours, alongside a battery of related parental instruments used for validation. Results: The results showed that the OP-SF with three dimensions (i.e., Anticipatory Problem Solving, Affect Management, and Risk Aversion) fitted the data well, demonstrated measurement invariance across parental gender, had an acceptable internal consistency, and exhibited good convergent validity with several related constructs. Conclusions: Taken together, the 9-item OP-SF is a sound instrument for assessing a unidimensional construct of overparenting when used with parents of early adolescents. The implications for psychiatric and family practices involving parents and adolescents are discussed in depth. Full article

The low-carbon development (LCD) of energy markets not only serves as a critical enabler in combating global climate change and advancing the green economy but also enhances global industrial competitiveness. Grounded in prospect theory, this study develops a tripartite evolutionary game model involving three core energy market stakeholders, i.e., energy enterprises, the public, and the government, to investigate the determinant factors and decision-making mechanisms underlying the LCD of energy enterprises, with subsequent simulation analyses conducted through MATLAB R2024a. The research findings indicate that loss aversion serves as the primary driver for energy enterprises’ adoption of LCD strategies. Public supervision demonstrates optimal effectiveness only under conditions of low risk and low loss, while risk sensitivity remains the dominant factor influencing the government’s strategic choices. Notably, government incentives combined with public supervision demonstrate significant synergistic effects in accelerating the corporate transition toward LCD. Accordingly, the government should actively promote LCD strategies to mitigate transformation risks for energy enterprises while concurrently optimizing regulatory frameworks to reduce public supervision costs and amplify incentive benefits, thereby fostering active public participation in LCD. Full article