Search Results (73)

Multi-criteria group decision making (MCGDM) under linguistic uncertainty remains a fundamental challenge in applied mathematics, where decision makers seldom assign crisp numerical evaluations and frequently exhibit heterogeneous risk attitudes shaped by behavioural factors. An integrated mathematical framework, hereafter PLR-3WBC (Probabilistic Linguistic Regret-driven Three-Way Bayesian Consensus), is developed to systematically integrate four methodological components that have each been individually validated in the MCGDM literature: representation of decision information with explicit probability mass on linguistic terms; quantification of decision-maker regret and rejoice psychology under linguistic uncertainty; classification of alternatives into three actionable decision regions rather than a single-valued ranking; and group consensus reaching with credal weight aggregation. Each component has demonstrated its effectiveness in its respective domain; the present framework capitalises on their complementary strengths by embedding them within a single pipeline equipped with formal guarantees, an integration that has not been previously reported. The framework integrates five methodological components: probabilistic linguistic term sets (PLTS) for information representation; the Bayesian best–worst method (BBWM) for credal criterion weighting; a regret–rejoice value function adapted to the linguistic domain for behavioural evaluation; three-way decision (3WD) thresholds derived from a loss-function model for actionable classification; and a distance-based consensus reaching process with feedback mechanism for group convergence. A case study on age-friendliness evaluation of twelve aging urban residential communities under an indicator system of five dimensions and eighteen criteria, with four expert decision makers, demonstrates that PLR-3WBC delivers an actionable three-way classification, recovers a transparent group consensus, and produces rankings broadly consistent with classical TOPSIS, VIKOR, PROMETHEE-II, and BWM-TOPSIS (Spearman rank correlation exceeding 0.97), thereby confirming that the integrated framework preserves the ordinal reliability of these established methods, while additionally delivering three outputs that arise from the methodological integration: an actionable three-way classification enabling discrete budget-aligned decisions, credal weight intervals quantifying the depth of expert agreement on criterion importance, and a behavioural reordering of borderline non-dominated alternatives that reflects the loss-averse psychology of the decision panel and would remain hidden under single-method deployment. Sensitivity analyses with respect to the regret aversion coefficient, the loss function parameters, and the consensus threshold confirm that the qualitative classification is stable across a wide parameter envelope, supporting the practical deployment of PLR-3WBC in age-friendly community renewal programmes. Full article

The precise selection of key flight parameters is fundamental to enhancing aircraft condition monitoring and risk warning capabilities. However, existing methods typically rely on a single source of information, i.e., either solely expert judgments or solely objective flight data, and lack effective mechanisms to reconcile conflicts between subjective opinions and objective data characteristics, which limits their applicability in complex aviation safety scenarios. To address this issue, a flight parameter selection method based on dual-domain rough sets and three-way decision theory is proposed in this paper. First, regret theory is introduced to quantify experts’ psychological preferences, and a subjective evaluation model integrating both psychological and absolute agreement is constructed. Second, a subjective–objective conflict information system is established within a dual-domain framework. Based on this system, bidirectional decision rules are designed to simultaneously consider positive-domain and negative-domain conditional probabilities, through which candidate sets of key flight parameters are generated. Finally, a new Bayesian minimum loss criterion is designed to determine the optimal parameter set. Experimental results demonstrate that the accuracy and robustness of flight parameter selection are improved by the proposed method while interpretability is maintained, offering reliable decision support for aviation safety analysis. Full article

A novel sustainable supplier selection (SSS) method is proposed to address the interrelation among attributes and the psychological state and risk attitude of decision-makers (DMs). The method integrates proportional interval type-2 hesitant fuzzy sets (PIT2HFSs), a generalized Shapley-based aggregation operator, and a modified regret theory combined with a normalized bidirectional projection (NBP) measure. The aggregation operators handle the correlations among attributes, while the NBP and regret theory reflect DMs’ risk preferences by considering both the best and worst alternatives. An application case study in a manufacturing enterprise, along with sensitivity and comparative analyses, demonstrates the effectiveness and robustness of the proposed approach. The results indicate that the method outperforms existing approaches in handling attribute interdependencies, decision uncertainty, and human risk behavior, providing a comprehensive and practical framework for sustainable supplier selection in the manufacturing industry. Full article

Zero-shot generalization to out-of-distribution (OOD) teammates and opponents in multi-agent systems (MASs) remains a fundamental challenge for general-purpose AI, especially in open-ended interaction scenarios. Existing multi-agent reinforcement learning (MARL) paradigms, such as self-play and population-based training, often collapse to a limited subset of Nash equilibria, leaving agents brittle when faced with semantically diverse, unseen behaviors. Recent approaches that invoke Large Language Models (LLMs) at run time can improve adaptability but introduce substantial latency and can become less reliable as task horizons grow; in contrast, LLM-assisted reward-shaping methods remain constrained by the inefficiency of the inner reinforcement-learning loop. To address these limitations, we propose LLM-TOC (LLM-Driven Theory-of-Mind Adversarial Curriculum), which casts generalization as a bi-level Stackelberg game: in the inner loop, a MARL agent (the follower) minimizes regret against a fixed population, while in the outer loop, an LLM serves as a semantic oracle that generates executable adversarial or cooperative strategies in a Turing-complete code space to maximize the agent’s regret. To cope with the absence of gradients in discrete code generation, we introduce Gradient Saliency Feedback, which transforms pixel-level value fluctuations into semantically meaningful causal cues to steer the LLM toward targeted strategy synthesis. We further provide motivating theoretical analysis via the PAC-Bayes framework, showing that LLM-TOC converges at rate $O(1/\sqrt{K})$ and yields a tighter generalization error bound than parameter-space exploration under reasonable preconditions. Experiments on the Melting Pot benchmark demonstrate that, with expected cumulative collective return as the core zero-shot generalization metric, LLM-TOC consistently outperforms self-play baselines (IPPO and MAPPO) and the LLM-inference method Hypothetical Minds across all held-out test scenarios, reaching 75% to 85% of the upper-bound performance of Oracle PPO. Meanwhile, with the number of RL environment interaction steps to reach the target relative performance as the core efficiency metric, our framework reduces the total training computational cost by more than 60% compared with mainstream baselines. Full article

“Cashback for positive reviews” is a common form of e-commerce manipulation that may undermine consumer trust and distort the market evaluation system. However, there is a lack of systematic research on how it influences consumers’ willingness to provide evaluations through psychological mechanisms. This study, based on the Stimulus–Organism–Response (S-O-R) framework, integrates the theories of psychological contract and cognitive dissonance. An empirical analysis based on 460 valid questionnaire responses was performed using SPSS and AMOS, yielding the following findings. (1) Negative emotions, including disappointment, anger, and regret, significantly triggered psychological contract breach, both transactional and relational. (2) Psychological contract breach reduced consumers’ willingness to provide positive reviews and lowered their store evaluation behavior, fully mediating the relationship between negative emotions and evaluation behavior. (3) Cognitive dissonance partially moderated the pathway from negative emotions through psychological contract breach to review behavior. This study elucidated the influence mechanism of negative emotions in “cashback for positive review” scenarios on consumers’ evaluation behavior, established a “merchant-user” online review relationship model, and provided practical and managerial implications for fostering mutually beneficial outcomes among platforms, merchants, and consumers. Full article

As people spend more time indoors, the impact of the built environment on psychological health has attracted growing attention. Yet existing studies often have difficulty capturing decision-makers’ reference dependence and loss aversion under uncertainty. To bridge this gap, we propose an evaluation framework comprising three first-level criteria—Outdoor Environment, Physical Comfort (including thermal, lighting, and color environments), and Acoustic Comfort—and determine combined weights by integrating subjective analytic hierarchy process (AHP) judgments with objective entropy weighting based on triangular fuzzy numbers (TFNs). We further incorporate prospect–regret theory to represent loss aversion, expectation-based reference points, and counterfactual regret/rejoicing, and couple it with the VIKOR compromise ranking method, forming an integrated “TFN + Prospect–Regret + VIKOR” approach. The proposed method is applied to four retrofit alternatives for an open-plan office floor (approximately 1200 m²), each emphasizing outdoor environment, physical comfort, acoustic comfort, or no single priority. Experts assessed the schemes using fuzzy linguistic variables. The results show that lighting conditions, thermal comfort, color scheme, and internal noise control receive the highest comprehensive weights. Extensive sensitivity analyses across value/weighting functions and regret-aversion parameters indicate that the ranking of alternatives remains stable while exhibiting clearer separation. Comparative analyses further suggest that, although the overall ordering is consistent with baseline methods, the proposed model increases score dispersion and improves discriminative power. Overall, by explicitly accounting for decision-makers’ psychological behavior and information uncertainty, the framework enables robust and interpretable selection of retrofit schemes for existing office spaces. Full article

Artificial underground compressed air energy storage (CAES) caverns have the advantages of large capacity and flexible location. However, the location selection of CAES in conditions of hard shallowly buried rock requires comprehensive consideration of multi-field coupling effects and engineering constraints, and the decision-making process involves multiple criteria and strong uncertainty. Aimed at addressing the problems of the evaluation index system not being detailed enough and the weight determination being biased to a single subjective or objective method in the existing research, this paper constructs a multi-criteria site selection evaluation method for an artificial underground CAES chamber in hard shallowly buried rock. Firstly, starting from the four criteria layers of ground environment, construction convenience, regional geological characteristics, and basic geological characteristics, combined with literature research and expert investigation, an evaluation index system containing 13 indicators was established. Secondly, the analytic hierarchy process (AHP) and entropy weight method (EWM) were introduced, the combination of subjective weight and objective weight realized through game theory, and the comprehensive weight of each index obtained. Then, the VIKOR method was used to rank the four candidate sites—A, B, C, and D—and the results were compared with those of the weighted TOPSIS method and the weighted gray relational analysis method. The engineering example shows that site B has advantages in group utility value, individual regret value, and compromise index. It is judged the optimal scheme by the three methods, and the ranking is stable under different decision-making mechanism coefficients, which verifies the robustness and applicability of the AHP–EWM–VIKOR model. The results show that the proposed method can distinguish different site selection schemes more clearly, effectively and comprehensively reflect suitability under complex geological and engineering conditions, and provide quantitative decision support for engineering site selection of artificial underground CAES caverns. Full article

Electronic word of mouth (eWOM) is a powerful form of online communication that strongly influences consumer purchasing behaviour. However, what remains less clear is the combined influence of situational factors versus personality traits when assessed simultaneously. The aim of this paper is to address this gap by developing an integrative conceptual model to assess the comparative relevance of situational factors and personality traits in driving eWOM generation in the Romanian accommodation sector. To implement the research scope, an empirical, quantitative, questionnaire-based investigation was pursued, data being collected from 291 tourists who had previous experience with online accommodation platforms such as booking.com, Airbnb, Trivago, etc. Based on the proposed conceptual model, data were analysed by means of structural equation modelling via SmartPLS 4.0. The research extends previous knowledge based on the Theory of Reasoned Action (TRA) and the Theory of Cognitive Dissonance (TCD), showing the combined multiple effects of situational factors and personality traits on consumers’ behaviour in generating eWOM in the accommodation sector. The results show that acquisition regret strongly drives eWOM generation intention, this regret being significantly increased by the unpleasantness, unacceptability, and importance of the consumer’s situation. Consumer expressivity predicts eWOM generation and is positively influenced by perceived social support, a relationship newly validated in the literature. Full article

In recent years, information propagation on social networks has attracted extensive attention, with psychological characteristics of individuals exerting a significant influence on the diffusion process. Our study investigates the role of regret psychology and its impact on information spreading and behavioral adoption. We categorize individuals into regretful and non-regretful groups and introduce regret intensity together with the proportion of regretful individuals as dynamic variables. Based on this, we construct a two-layer interactive model consisting of a psychological layer and a behavioral layer. Then we establish the behavioral adoption model for the heterogeneous population and study the propagation characteristics of the regretful individuals on social networks. Furthermore, we derive the propagation dynamics using edge-based compartmental theory to examine the transmission mechanism. Numerical simulations, which coincide nicely with our theoretical analyses, reveal the crossover phenomena in phase transitions: as the regret threshold increases, adoption dynamics shift from second-order continuous to first-order discontinuous transitions. More importantly, for a given propagation probability, there exists an optimal regret threshold that maximizes the final adoption size. These findings highlight the crucial role of regret psychology in reshaping the propagation mechanism and provide a new theoretical perspective for understanding symmetry transformations and group heterogeneity in social contagion dynamics. Full article

Decision-making theory has developed over many decades at the intersection of economics, mathematics, psychology, and engineering. Its classical foundations include Bernoulli’s expected utility theory, von Neumann and Morgenstern’s rational choice theory, and the criteria proposed by Savage, Wald, Hurwicz, and others. However, in real-world contexts, decisions are made under uncertainty, incompleteness, and unreliability of information, which classical approaches do not adequately address. To overcome these limitations, modern multi-criteria decision-making methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (Compromise solution approach) (VIKOR), and ELimination Et Choix Traduisant la REalité (Elimination and Choice Expressing Reality) (ELECTRE), as well as their fuzzy and Z-number extensions, are widely applied to the modeling and evaluation of complex systems. These Z-number extensions are based on the concept of Z-numbers introduced by Lotfi Zadeh in 2011 to formalize higher-order uncertainty. This study introduces the Z-Regret principle, which extends Savage’s regret criterion through the use of Z-numbers. Supported by Rafik Aliev’s mathematical justifications concerning arithmetic operations on Z-numbers, the model evaluates regret not only as a loss relative to the best alternative but also by incorporating the degree of confidence and reliability of this evaluation. Calculations for the selection of digital advertising platforms in terms of performance assessment under various scenarios demonstrate that the Z-Regret principle enables more stable and well-founded decision-making under uncertainty. Full article

Against the backdrop of incomplete evaluation information prevalent in real-world decision-making scenarios and the limited application of Fermatean fuzzy numbers (FFNs) in the domain of two-sided matching (TSM) models, this paper proposes a Fermatean fuzzy two-sided matching model that integrates the regret aversion psychological behavior of agents and their matching willingness. Firstly, the TSM problem characterized by incomplete Fermatean fuzzy preference is described. Based on the incomplete Fermatean fuzzy evaluation information provided by bilateral agents, satisfaction matrices are constructed, and a fairness-aware matching willingness matrix is quantified. Moreover, a multi-objective TSM optimization model is established to maximize the satisfaction of agents on both sides, and the optimal TSM scheme is obtained by solving the model. Finally, the feasibility, effectiveness, and innovation of the proposed approach are validated by an example analysis of TSM on a data trading platform. Full article

This study investigates the influence of FOMO, loss aversion, and herd behavior on gold investment decisions in the Vietnamese market. Employing data collected from 727 investors and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, the analysis results confirm the pivotal role of FOMO, with both direct and indirect impacts on gold investment decisions. Notably, both loss aversion and herd behavior positively influence FOMO, thereby indirectly encouraging relatively hasty and inadequately considered investment decisions. The study also finds that FOMO has a negative relationship with anticipated regret but is positively correlated with subjective expected pleasure. Furthermore, as determined through Multi-Group Analysis (MGA), psychological messages featuring “self-decision” or “risk warning” demonstrate a significant moderating role, potentially reducing or enhancing the influence of FOMO on investment decisions. These findings contribute to enriching behavioral finance theory and provide an empirical basis for developing effective risk management policies and gold market regulation aimed at mitigating the negative impacts of FOMO. Full article

Recycled water is commonly considered an environmentally friendly alternative water source for urban water systems, which can not only serve as a solution for water scarcity, but also reduce wastewater discharge from sewage systems. However, owing to the high degree of energy consumption during recycled water production, the utilization of recycled water may be detrimental to greenhouse gas emission reduction. In this work, we conduct a detailed investigation into greenhouse gas emissions from different sources in a typical multisource urban water system in China. Furthermore, we develop an optimization model for water resource allocation based on the rime optimization algorithm and regret theory. The results show that although greenhouse gas emissions from recycled water exceed those from other sources, their impact can be eliminated through rational water resource allocation. Specifically, compared with the original water resource allocation, the optimal results effectively reduce pollutant emissions by 7.6~11.1% without excessively increasing water resource shortages and greenhouse gas emissions. Additionally, both subjective preferences and recycled water utilization conditions have significant impacts on the optimization results, which should be carefully selected according to practical situations and technologies. Overall, the methods developed in this study provide a new general framework for the water resource allocation of multisource urban water systems in the context of greenhouse gas emission reduction and recycled water utilization, which can be employed in other areas. Full article

This paper introduces a novel framework for Distributionally Robust Bayesian Optimization (DRBO) with continuous context that integrates optimal transport theory and entropic regularization. We propose the sampling from the Wasserstein Barycenter Bayesian Optimization (SWBBO) method to deal with uncertainty about the context; that is, the unknown stochastic component affecting the observations of the black-box objective function. This approach captures the geometric structure of the underlying distributional uncertainty and enables robust acquisition strategies without incurring excessive computational costs. The method incorporates adaptive robustness scheduling, Lipschitz regularization, and efficient barycenter construction to balance exploration and exploitation. Theoretical analysis establishes convergence guarantees for the robust Bayesian Optimization acquisition function. Empirical evaluations on standard global optimization problems and real-life inspired benchmarks demonstrate that SWBBO consistently achieves faster convergence, good final regret, and greater stability than other recently proposed methods for DRBO with continuous context. Indeed, SWBBO outperforms all of them in terms of both optimization performance and robustness under repeated evaluations. Full article

We propose a novel contextual multi-armed bandit (CMAB) framework that integrates copula-based context generation with Gaussian Process (GP) regression for reward modeling, addressing complex dependency structures and uncertainty in sequential decision-making. Context vectors are generated using Gaussian and vine copulas to capture nonlinear dependencies, while arm-specific reward functions are modeled via GP regression with Beta-distributed targets. We evaluate three widely used bandit policies—Thompson Sampling (TS), $\epsilon$-Greedy, and Upper Confidence Bound (UCB)—on simulated environments informed by real-world datasets, including Boston Housing and Wine Quality. The Boston Housing dataset exemplifies heterogeneous decision boundaries relevant to housing-related marketing, while the Wine Quality dataset introduces sensory feature-based arm differentiation. Our empirical results indicate that the $\epsilon$-Greedy policy consistently achieves the highest cumulative reward and lowest regret across multiple runs, outperforming both GP-based TS and UCB in high-dimensional, copula-structured contexts. These findings suggest that combining copula theory with GP modeling provides a robust and flexible foundation for data-driven sequential experimentation in domains characterized by complex contextual dependencies. Full article