Search Results (41)

This study examines the characteristics of Group Multi-Attribute Decision Making (GMADM), including highly ambiguous information, divergent expert opinions, and bounded rationality among decision-makers. From the perspective of symmetry modeling and bias control, we propose an adaptive decision-making framework based on Spherical Fuzzy Sets (SFS). First, a spherical fuzzy quantification system for online reviews is constructed to map multi-source asymmetric information within reviews to Spherical Fuzzy Numbers. Second, an adaptive expert weighting mechanism is developed that integrates individual expert performance with the level of group consensus, dynamically adjusting weights to suppress the asymmetric interference of outlier opinions. Subsequently, we design the Credibility-based Spherical Weighted Arithmetic Mean (CSWAM) to preserve the dominance of expert judgments in a nonlinear manner and construct the Spherical Fuzzy Score function with Adaptive Hesitation Penalty (HP-SC) to ensure robustness and non-negativity in the defuzzification process. Furthermore, we extend DEMATEL and TODIM to the SFS environment, constructing a comprehensive evaluation model that captures causal relationships among attributes and asymmetric information, such as decision-makers’ loss aversion. Finally, empirical results from online movie review usefulness rankings demonstrate that this model can accurately identify and mitigate asymmetric information biases while maintaining decision symmetry equilibrium and exhibiting higher ranking stability. Full article

This article presents a contextual entropic model for understanding, explaining, and resolving cognitive biases associated with thinking under uncertainty. This article applies a unique performance evaluation of teamwork method, enabling a qualitative and quantitative assessment of symptom-based context and ensuring a clear and rational interpretation of decision making in ambiguous and risky situations. This human reliability assessment method also contributes to a deeper understanding of the iterative and complementary nature of cognition and decision making. The main idea is the quantum-like understanding of the dual image of a symptom as a wave and a bit of information in thought processes. Thus, each context alternative is identified by a unique combination of three-valued states of the socio-technical system at discrete points in time—recognized, unrecognized, and unrecognizable. Judgment is a wave-like process driven by the interfering sum of the amplitudes of symptom recognition shifts. By modeling stepwise cognitive processes by adding and subtracting symptoms or stimuli, we can estimate and compare the likelihood ratios between biased judgments. This article presents three canonical filters used to improve cognitive theories and decision-making methods—Ellsberg’s two-color, three-color, and four-color paradoxes—to demonstrate the power of the symptom-based context model. Full article

This study develops a production-based asset pricing model that incorporates time-varying disaster risk together with model uncertainty. Within an extended relative-entropy framework, agents’ distorted beliefs and ambiguity aversion are characterized, and the corresponding Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation is derived under a stochastic robust-control setting. The framework implies that the equity premium can be decomposed into three components: diffusion and jump risk premiums associated with conventional risk aversion and an additional rare-event premium generated by ambiguity aversion. Numerical experiments show that ambiguity aversion reduces the equilibrium risk-free rate, whereas aversion to rare disasters significantly raises compensation for bearing risk, helping reconcile both the equity premium puzzle and the risk-free rate puzzle. In addition, equity return volatility increases with the probability of disaster events, but at a diminishing rate. Overall, the results underscore the importance of model uncertainty and time-varying disaster risk in the determination of asset prices and risk premia. 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

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

Behavioral deviations from subjective expected utility theory, most famously captured by the Allais paradox and the Ellsberg paradox, have inspired extensive theoretical and experimental research into risk and ambiguity preferences. While the existing analyze these paradoxes independently, little work explores how such heterogeneously biased agents interact in networked strategic environments. Our paper fills this gap by modeling a convergent Markov–network game between Allais-type and Ellsberg-type players, each endowed with fully enriched loss matrices that reflect their distinct probabilistic and ambiguity attitudes. We define convergent priors as those inducing a spectral radius of <1 in iterated enriched matrices, ensuring iterative convergence under a matrix-based update rule. Players minimize their losses under these priors in each iteration, converging to an equilibrium where no further updates are feasible. We analyze this convergence under three learning regimes—homophily, heterophily, and type-neutral randomness—each defined via distinct neighborhood learning dynamics. To validate the equilibrium, we construct a risk-neutral measure by transforming losses into payoffs and derive a riskless rate of return representing players’ subjective indifference to risk. This applies risk-neutral pricing logic to behavioral matrices, which is novel. This framework unifies paradox-type decision makers within a networked Markovian environment (stochastic adjacency matrix), extending models of dynamic learning and providing a novel equilibrium characterization for heterogeneous, ambiguity-averse agents in structured interactions. Full article

Background/Objectives: University students are particularly vulnerable to unhealthy eating patterns and body image dissatisfaction. The association between lifestyle factors and eating disorders (EDs) can be ambiguous as healthier lifestyle choices may paradoxically be related to ED risk. In this study, we aimed to analyze physical activity (PA) and dietary patterns—specifically food type and diet quality—as lifestyle indicators in university students with and without ED risk. Motivations for engaging in PA and the association between PA levels and diet quality were also examined. Methods: A descriptive cross-sectional study was conducted on a convenience sample of 1982 undergraduate students aged 18–30 years from the University of the Balearic Islands. Dietary intake, diet quality, PA levels, and motivations were self-reported using a questionnaire. Results: Students at risk of EDs reported higher diet quality, including greater adherence to the Mediterranean diet (p < 0.001) and more adequate consumption of fruits (p < 0.001), vegetables (p < 0.001), and red and processed meat (p < 0.001). Regarding PA, participants with ED risk engaged in more weekly PA sessions (p < 0.001) and accumulated a longer total weekly duration (p = 0.019), with physical appearance being the main motivation. In participants without ED risk, PA levels were positively associated with adherence to the Mediterranean diet (p < 0.001); however, no such association was observed in participants with ED risk (p = 0.538). Conclusions: Students at risk for EDs exhibited comparatively healthier diet and PA patterns, seemingly driven by concerns related to body image and an aversion to energy-dense foods. Therefore, apparent health behaviors should not be used to rule out ED risk. Full article

To address the need for robust investment strategies in an increasingly uncertain global market, this study focuses on an ambiguity-averse insurer facing exchange rate uncertainty while investing in a foreign stock market. The insurer’s surplus is modeled via a classical compound Poisson process, and exchange rate dynamics are captured using an Ornstein–Uhlenbeck process for the drift component. Within the framework of maximizing expected exponential utility of terminal wealth, we derive and solve the Hamilton–Jacobi–Bellman equation to characterize the optimal investment strategy and the associated value function. Finally, a numerical example illustrates how varying model parameters influences the insurer’s optimal investment behavior. Full article

The nature of contemporary careers has shifted and is characterized by precarity, emphasizing the need for young people to possess adequate career resources in their pursuit of decent work. Grounded in the dual-process model and the conservation of resources (COR) theory, this study examines the loss impact caused by ambiguity aversion and low resilience on young people’s responses to career ambiguity, specifically, their coping strategies (i.e., avoidance and approach) and career anxiety. In this cross-sectional study, we collected data using online surveys from young adults aged 18–35 (N = 156) in Norway, Indonesia, and Bangladesh. Serial mediation analyses were conducted using IBM-SPSS Statistics. Our findings found that ambiguity aversion had significant positive relations with career anxiety. Furthermore, resilience and avoidance coping were found to play mediating roles in the ambiguity aversion–career anxiety association. The results of the exploratory analyses also revealed significant differences in variable levels between the three countries examined. Our results have both theoretical and practical implications that contribute to the knowledge and practices in helping young people navigate the risks of precarity by developing adaptive career resources. We acknowledge the limitations regarding sample size and research design. Full article

This paper proposes a coalition-based framework for the coordinated operation of multi-regional power systems subject to extreme uncertainty in demand surges, renewable variability, and resource mobilization delays. Methodologically, we integrate Bayesian learning with distributionally robust optimization (DRO), embedding dynamically updated scenario posteriors into a Wasserstein ambiguity set. This construction captures both stochastic variability from renewable and load realizations and epistemic uncertainty from incomplete knowledge of probability distributions. To align individual incentives with system-level efficiency, we design a risk-adjusted utility mechanism that combines VCG transfers, Shapley allocations, and nucleolus refinements. These mechanisms explicitly consider agent heterogeneity, risk aversion, and coalition stability, ensuring that cooperation remains both efficient and sustainable. The optimization model maximizes expected social welfare while incorporating constraints on transmission corridor capacities, mobilization logistics, demand–response rebound effects, and mobile energy storage operations. A hierarchical decomposition algorithm integrates the Bayesian-DRO dispatch layer with cooperative game-theoretic allocations to maintain tractability and robustness at large scale. A case study on a six-province interconnected system with 14–26 GW peak demand, 10.2 GW solar, 8.6 GW wind, 14 GW peaking units, and 6.8 GW mobile storage demonstrates the effectiveness of the approach. Results indicate that the proposed framework raises expected welfare by nearly 10% relative to a non-cooperative baseline, reduces the probability of unserved energy exceeding 1.5% from almost 2% to negligible levels, and narrows payment disparities across provinces to strengthen coalition stability. Demand response peaks at 250–300 MW with rebound averaging 25%, while mobile BESS units cycle frequently to enhance local reliability. Overall, the findings highlight a robust and incentive-compatible pathway for resilient inter-provincial operation, providing both methodological advances and policy-relevant insights for multi-regional energy governance. Full article

This study investigates the factors influencing the perceived credibility of advertising content on e-commerce platforms, drawing on behavioral economics and communication theories. Through a quasi-experimental design involving 156 participants, we analyzed how message features, product importance, and socio-demographic variables affect user perceptions. The results reveal that users assign higher credibility to simple, essential content and that gender plays a significant role, with women perceiving paramedical and technical content as more credible. Age, however, showed no significant influence. The discussion highlights the psychological mechanisms behind these behaviors, such as risk and ambiguity aversion, and proposes implications for digital marketing strategies and future research. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Today’s linear nutrient flows are rooted in a long history of agronomic and wastewater engineering strategies that have created cascading environmental, social, and economic side effects, signaling the need for more holistic and circular approaches. Our examination of the regulatory pathways that enable and constrain urine recycling—an underutilized approach to repurposing human waste as fertilizer—addresses a persistent research gap related to the mainstreaming of transformative technologies. Framed around policy process theories—Street Level Bureaucracy and Multiple Streams Theory—our methods include a review and mapping of 54 regulatory documents; action research where we reflect on our own efforts to expand urine recycling; and interviews with 16 practitioners and regulators in four states which, to our knowledge, are the only places in the US with efforts to scale up urine recycling in community settings. Given its circular nature, a key challenge we find is a lack of clarity around which sectors, or what scales of government, “own” the decision to allow the collection and use of urine as a fertilizer. Working around these challenges, we show how practitioners use many practical strategies to simplify the approval process and reduce the risk aversion regulators face when confronted with ambiguous rulemaking. Full article

We investigate a class of quadratic backward stochastic differential equations (BSDEs) with generators that are singular in y. First, we establish the existence of solutions and a comparison theorem, thereby extending the existing results in the literature. Furthermore, we analyze the stability properties, derive the Feynman–Kac formula, and prove the uniqueness of viscosity solutions for the corresponding singular semi-linear partial differential equations (PDEs). Finally, we demonstrate applications in the context of robust control linked to stochastic differential utility and the certainty equivalent based on g-expectation. In these applications, the quadratic coefficients in the generators, respectively, quantify ambiguity aversion and absolute risk aversion. 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