Search Results (782)

Building users are key participants in demand response (DR) markets, providing significant flexible resources. Due to uncertainty in market clearing prices, various risk-based decision models have been developed to describe their bidding behavior, typically assuming constant risk preferences. However, empirical evidence indicates that users’ risk attitudes vary with the magnitude of load adjustments. To capture this feature, this paper introduces mental accounting theory to model the risk-aware bidding behavior of building users. Total response capacity is divided into three independent mental accounts based on air-conditioning setpoint adjustment magnitude, representing risk-averse, risk-neutral, and risk-seeking behaviors. This framework allows multiple risk preferences to be represented within a unified bidding model. For each account, response quantity and cost models are developed. Bidding strategies under uncertain market clearing prices are formulated by incorporating loss aversion. A multi-agent simulation framework, including building users, a load aggregator, and a grid operator, is established to simulate the market clearing process. A simulation study is conducted using 19 building clusters located in Zhuhai, China. The proposed model is compared with a single-bid model and a step-wise bidding model with constant risk preferences. The results show that it better captures building users’ multiple risk preferences under market clearing price uncertainty. Users tend to secure stable returns through responses with minimal comfort loss, while pursuing excess profits via higher bids for responses involving greater comfort sacrifices. Full article

This study analyzes the relationship between stock market returns and investment flows in investment funds in Spain. Through a quantitative analysis covering the period from December 2001 to June 2025, it examines not only the existence of a correlation but also its temporal structure, functional form, and heterogeneity across different geographical areas (U.S., Europe, Japan, and Spain). Using monthly data on net flows from INVERCO and market indices, the study employs Ordinary Least Squares (OLS) regression models, segmented regressions, and fixed-effects panel models to obtain robust estimates. The results confirm a positive and statistically significant relationship between past returns and subsequent investment flows, with a temporal lag ranging from one to three months. This delay varies notably by geographical region, suggesting the existence of different investor profiles and information channels. The study also finds evidence of a convex relationship, indicating that investors react asymmetrically, aggressively pursuing high returns more than penalizing low ones. These findings, interpreted through the lens of behavioral finance, point to pro-cyclical and reactive behavior of Spanish investors, driven by biases such as loss aversion, trend-following, and delays in information processing. The study contributes to the academic literature by providing updated and methodologically robust evidence on Spain, a market that has traditionally been underexplored, and offers practical implications for investors, fund managers, and regulators in terms of financial education and risk management. Full article

Background: Adolescents are at an increased risk for experiencing emotional reactions and interpersonal stressors, which can interfere with their access to gratitude. While gratitude is typically defined as an empathic or other-oriented emotion, personal distress is an aversive or self-oriented empathic reaction to others’ emotions or states, which can interfere with prosocial behavior. The goal of this study was to examine whether guilt proneness and gender moderate the prospective association between personal distress and later gratitude. Methods: The participants consisted of 111 early adolescents (61% females; M age = 12.74). Trait gratitude, personal distress (IRI—Personal Distress), and guilt proneness (TOSCA-A) were used as self-report measures. Using conditional process analysis (PROCESS Model 2), we tested whether Time 1 personal distress is associated with Time 2 gratitude, moderated by guilt and gender. Correlations showed that Time 2 gratitude was positively related to guilt but was not significantly related to personal distress. Results: The results indicated that personal distress was associated with lower Time 2 gratitude when guilt proneness was moderate to high, but not when guilt proneness was low. The association between personal distress and gratitude varied across levels of guilt proneness. Although conditional effects were examined separately for boys and girls, the interaction with gender was not significant and should be interpreted cautiously. The findings suggest that lower gratitude in adolescence may reflect distress–guilt dynamics rather than ingratitude itself. Conclusions: These findings highlight the importance of considering guilt proneness in future research on adolescents’ socioemotional development. Full article

Background/Objectives: Time-constrained consultations in high-volume settings can crowd out patient-centered communication, while AI-generated advice may face algorithm aversion when it lacks a humanistic dimension. This study examined whether a brief narrative-based prompt could improve coded patient-facing communication features in an LLM relative to both clinicians and an unprompted model on authentic patient queries. Methods: We conducted a three-condition comparative evaluation using a stratified sample of 1000 de-identified MedDialog-CN consultations (2016–2020). For each consultation, the same patient query was used to generate (i) a zero-shot GPT-o3-mini response and (ii) a narrative-prompted GPT-o3-mini response; the original physician reply served as the human baseline. Responses were annotated with a pre-specified schema operationalizing four communication dimensions—Storytelling, Empathy, Personalization, and Clarity—with expert adjudication. Frequency-based indicators were summarized as mean events per consultation, and binary indicators as proportions; secondary checks captured unwarranted certainty and risk-relevant language. Results: Narrative prompting shifted coded patient-facing communication from sparse and selectively deployed (clinicians and zero-shot AI) to more routine and standardized. Across the reported communication measures, the prompted model showed the most favorable overall pattern, with higher narrative-device use, empathic support, contextual tailoring, and terminology explanation, alongside more frequent consideration of patient preferences and markedly higher rates of emotion–symptom linkage and the presence of a patient-centered narrative framework. Conclusions: Narrative prompting may offer a lightweight and potentially scalable strategy for improving patient-facing communication in Chinese asynchronous, text-based online consultations. An important next step is calibration: humanistic cues should be delivered selectively and safely so that responses remain credible, locally feasible, and cognitively manageable. Full article

Distributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical distribution or a model. To further guard against finite, noisy data, model-based approaches admit Bayesian formulations that propagate uncertainty from the posterior to the decision-making problem. However, when the model is misspecified, the decision-maker must stretch the ambiguity set to contain the data-generating process (DGP), leading to overly conservative decisions. We address this challenge by introducing DRO with Robust ayesian Ambiguity Sets (DRO-RoBAS) to model misspecification. These are Maximum Mean Discrepancy ambiguity sets centred at a robust posterior predictive distribution that incorporates beliefs about the DGP. We show that the resulting optimisation problem obtains a dual formulation in the Reproducing Kernel Hilbert Space and we give probabilistic guarantees on the tolerance level of the ambiguity set. Our method outperforms other Bayesian and empirical DRO approaches in out-of-sample performance on the Newsvendor and Portfolio problems with various cases of model misspecification. Full article

Animal by-products (ABPs), comprising both edible and inedible components, offer significant nutritional, economic, and environmental value. However, their utilization differs markedly across global jurisdictions due to cultural preferences, regulatory frameworks, and technological capacities, which collectively shape consumption patterns and determine integration into food systems or diversion to industrial applications. While consumer reliance on offal remains high in the Global South, driven by tradition, affordability, and nutritional needs, its acceptance in the Global North is markedly lower, often limited by cultural aversion and perceived risks. Drawing from published evidence and primary survey data, this review examines regional consumption trends, industrial utilization pathways, and emerging valorization opportunities for ABPs. Globally, industrial use of ABPs is increasingly shifting toward advanced bioprocessing, integration within circular bioeconomy models, and high-value applications in nutraceutical, pharmaceutical, and bio-industrial sectors. An online cross-sectional survey (n = 358) conducted across Africa, North America, Europe, and Asia revealed strong regional disparities in offal consumption, with higher acceptance in parts of Africa and Asia and more selective use in Europe and North America. Respondents also indicated clear support for non-food valorization pathways, particularly animal feed, fertilizer, and energy production, alongside pharmaceutical and cosmetic applications. These findings align with the literature, where industrial valorization pathways such as collagen and gelatin extraction, rendering, and bioenergy production dominate. This review synthesized the jurisdictional disparities in consumption, regulation, technological capability, and industrial applications while highlighting emerging technological opportunities for high-value valorization. Recommendations emphasize consumer education, regulatory refinement, technological innovation, and sustainable practices to enhance the economic and environmental benefits of ABP utilization within a circular bioeconomy framework. Full article

While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. To address the collaborative governance dilemma, this study constructs a tripartite “platform-user-government” evolutionary game model based on prospect theory. It explores the evolutionarily stable strategies and stability conditions of each actor, supplemented by numerical simulations and practical case validation. The results indicate that: (1) under specific conditions, the system can converge to an ideal equilibrium {active platform governance, engaged user participation, stringent government supervision}; (2) the government’s reward–penalty mechanisms can drive the system towards this ideal equilibrium; (3) users’ digital literacy is a key variable influencing the system’s evolutionary path; (4) both the risk preference coefficient (β) and loss aversion coefficient (λ) from prospect theory have a significant moderating effect on the system’s evolution. Finally, targeted recommendations are proposed for the three aforementioned stakeholders to accelerate the improvement of China’s collaborative governance of the content ecosystem. Full article

This paper analyzes “prompt engineering” through the economic lens of self-insurance against the risk of errors from noisy AI systems. To formalize this approach, we model an agent under cognitive load, allocating effort between working unassisted and prompting an AI assistant. The theoretical model demonstrates that an agent’s optimal prompting effort is driven by the agent’s attitude toward risk. Specifically, the model proves that risk-averse agents rationally “over-invest” in prompting effort, while risk-seeking agents “under-invest” relative to the risk-neutral benchmark. This outcome stems from the covariance between the marginal utility of performance and the marginal product of prompting. This alignment is positive for risk-averse agents, effectively boosting the AI’s perceived productivity. The novel implication is that prompting effort is an economically meaningful behavior that can be informative about an individual’s underlying attitude toward downside AI risk. These results offer a new perspective for understanding heterogeneity in AI adoption and oversight. They also suggest that, under comparable task conditions and controlling for prompting ability, observed prompting effort may be informative about attitudes toward downside AI risk. The framework therefore provides a risk-management perspective for understanding heterogeneity in AI governance in high-stakes settings such as healthcare and finance. Full article

Suicide is the third-leading cause of death in the age group 15–29. In recent years, suicide completions as well as attempts have notably risen, with suicide being highlighted as a major mental health crisis. While risk factors associated with suicide are well-documented, our qualitative understanding of the deeper experiences of suicidality remains limited. One factor that has been found to relate to suicidality is family size, with research finding that having more older siblings is associated with higher suicide risk. To better understand the dynamics of suicidality in large families, eight participants from families of four or more children were interviewed, and interpretative phenomenological analysis was used. A variety of internal and environmental factors that contributed to suicidality in large families were found, including exposure to suicide, poverty, abuse, and a sense of worthlessness. The findings of this study support the need for additional research to validate the factors associated with an aversive environment and internal processes in suicidality. Although not all multi-sibling families experience suicidality, factors exist that can exacerbate risk among large families. Discussion regarding future implications is included. Full article

Electric vehicles (EVs) are promoted to decarbonise road transport, yet uptake remains slow in many emerging markets. This study examines consumer resistance to EV adoption in Sri Lanka by modelling status quo bias (SQB) using a Prospect Theory lens. An online survey of urban vehicle owners and near-term buyers yielded 157 responses; after screening and removing influential outliers, 151 cases were analysed using partial least squares structural equation modelling (PLS-SEM). The model tests five Prospect Theory-aligned antecedents, namely, loss aversion, reference dependence, risk perception, framing effects, and uncertainty aversion, and evaluates environmental concern as a moderator. Results indicate that loss aversion has a significant positive effect on SQB (β = 0.216, p = 0.005) and uncertainty aversion is the strongest predictor (β = 0.453, p < 0.001), while reference dependence, risk perception, and framing effects show positive but statistically non-significant direct effects. Moderation tests show that environmental concern significantly moderates the effects of reference dependence (β = 0.181, p = 0.039) and framing effects (β = 0.179, p = 0.037) on SQB, but does not significantly moderate the loss aversion, risk perception, or uncertainty aversion paths. Overall, perceived losses and—especially—ambiguity surrounding EV ownership appear to sustain reliance on internal combustion vehicles in this developing-country context, underscoring the need for interventions that reduce uncertainty (credible infrastructure signals, stable policy, service capability) and mitigate perceived losses (warranties, resale assurances) alongside carefully framed communications. Full article

International policy frameworks, including the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) and the WHO Quality Rights initiative, have established dignity as a foundational right in mental health care. However, a significant gap remains between these policy aspirations and the lived experience of service users, often due to risk-averse cultures that prioritize control over autonomy. This commentary employs an interpretive synthesis of international literature (2006–2025) and illustrative case examples, such as the Trieste model and Quality Rights implementation in low-resource settings, to examine the operationalization of dignity-centered care. I argue for a paradigm shift from control-based safety models to relational safety grounded in biographical literacy and positive risk-taking. Key findings highlight that dignity-centered approaches not only improve patient experiences of respect and agency but also mitigate moral injury and burnout among the nursing workforce. Furthermore, as digital mental health tools and AI-driven risk assessments emerge, systems must ensure these technologies enhance rather than automate paternalism. I conclude that realizing dignity-centered care requires a structural and cultural transformation, embedding dignity into clinical protocols, leadership practices, and environmental design to move beyond rhetorical commitments toward measurable, humane standards. Full article

The article examines the persistent stability of the weighted average cost of capital (WACC) disclosed by German DAX40 companies despite substantial declines in risk-free interest rates between 2004 and 2021. While theory suggests that WACC should reflect lower risk-free interest rates and decline as well with falling government bond yields, empirical evidence reveals minimal adjustment in reported WACC figures. Disclosed WACC of DAX40 companies remains between 7% and 8% as the yield of the ten-year German government bond fell from 4.1% to −0.2%. This study employs quantitative analyses to investigate whether systematic increases in risk exposure can explain this phenomenon. Using capital market data spanning from 2000 to 2023, we analyze five risk dimensions: systematic risk (beta factors), overall market volatility, risk aversion (lambda factors), earnings risk, and financial structure risk. Bootstrap analyses reveal a 41.5% reduction in beta factor variance, while volatility analyses demonstrate declining market risk exposure. The market price of risk analysis does not reveal definite findings. Earnings risk measures indicate improved financial stability, and debt ratios show modest declines. These findings suggest that observable risk parameters cannot explain persistent WACC levels, indicating a disconnect between theoretical WACC calculations and practitioner applications in investment project decision-making following value-based management principles. Full article

In practice, the emerging shared electric bicycles battery-swapping systems face weather disturbances and time-window lateness, which can reduce travel efficiency and degrade operational reliability. To facilitate the operation reliability and management robustness, this study builds a scenario-based location–routing optimization model that links station siting with replenishment routing under two weather scenarios, no rain and rain. The first stage selects sites and determines battery-swapping station construction decisions before scenario realization. The second stage reacts through scenario-dependent depot assignment and routing and scheduling decisions. The objective functions are to minimize average cost while restraining tail risk through an explicit worst-case term, yielding an adjustable efficiency–resilience balance. The modeling constraints impose a minimum service level, preserve route feasibility under scenario travel times, and prevent structural shortcuts. An improved genetic algorithm is proposed to solve the model. The algorithm adopts construction encoding and scenario-wise assignment encoding, applies feasibility repair before evaluation, and constructs executable routes during decoding with local improvement. Experiments demonstrate that the proposed method achieves better objective values than benchmark methods and exhibits stable convergence. Case study shows that rain increases transportation and lateness-related costs. The System Resilience Analysis shows that the robust penalty term reduces variable operating loss under rain by 5.33% and cuts the cost shock from no rain to rain by 32.82%, demonstrating improved resilience under adverse weather. Full article

The rapid integration of AI customer service in e-commerce raises an important managerial question: Can AI effectively reduce product-related information asymmetry and improve sales performance across different product types? While prior research highlights both the uncertainty-reducing benefits of information and the risks of algorithm aversion, little is known about how AI customer service performs under varying levels of product uncertainty and information asymmetry. Using a difference-in-differences design with fixed effects across time, products, shops, and categories, we examine the impact of replacing customer service with AI on sales outcomes, distinguishing between search and experience goods. We further test how the depth and breadth of product information moderate these effects. Our findings indicate that AI customer service reduces sales for experience goods but not for search goods, unless accompanied by sufficient informational depth and breadth. We argue that this effect arises because AI technically inherits and amplifies the information asymmetry inherent in experience products, while greater informational depth and breadth of product information can mitigate this amplified asymmetry. Additionally, we find that this mitigating effect is more pronounced among products with high return rate. These findings clarify when AI-generated information mitigates product uncertainty and when it exacerbates it. Our results provide actionable guidance for firms seeking to deploy AI strategically in digital commerce environments. Full article

Grey plastic is a representative traditional architectural decoration craft in the Lingnan region in China, carrying rich historical and cultural values as well as distinctive regional artistic characteristics. However, the grey plastic craft is currently facing problems such as inheritance gaps and a shortage of craftsmen, and its restoration projects impose extremely high professional requirements on contractors. Existing contractor selection methods are mostly applicable to ordinary construction projects and are difficult to adapt to its particularity, which may easily lead to risks such as substandard restoration quality. Therefore, this paper proposes a contractor selection method for grey plastic decoration projects of cultural relic buildings based on the BWM-TODIM method. Firstly, an evaluation system covering six core criteria is constructed; secondly, the BWM is adopted to determine the criteria weights; thirdly, the TODIM method is used to characterize the decision-makers’ loss aversion psychology and rank the candidate contractors; finally, an empirical analysis is conducted with a grey plastic restoration project in Lingnan as a case to verify the feasibility and effectiveness of the method. This study can provide decision support for the scientific selection of contractors for grey plastic decoration projects and contribute to the sustainable protection of cultural heritage. The scope of this study is limited to contractor selection for grey plaster decoration engineering of cultural relic buildings. Full article