apply multi-criteria decision-making

To address the challenge that conventional supervised decision support systems struggle to reconcile conflicting preferences among multiple stakeholders, this paper proposes a preference-driven participatory decision-making framework. Methodologically, it formalizes decision-making as a contextualized multi-objective optimization problem, introducing (i) a reward-function-based stakeholder representation mechanism, (ii) a model-agnostic and interpretable modular architecture, and (iii) a dynamic trade-off and consensus-generation mechanism integrating compromise functions with user-defined composite scoring. Experiments on two real-world scenarios demonstrate that the framework significantly outperforms pure predictive baselines in balancing competing objectives. Ablation studies confirm its robustness across diverse models, scales, and domains. The core contribution is the first general-purpose, multi-stakeholder decision support paradigm that simultaneously ensures interpretability, flexibility, and consensus orientation.

This study addresses the limitations of conventional energy system models, which often generate alternatives that overlook stakeholder concerns, and traditional multi-criteria decision analysis (MCDA), which relies on predefined options and inadequately explores the feasible solution space. To overcome these issues, the authors propose a value-focused alternative generation method (VF-MGA) that, for the first time, bidirectionally couples MCDA with modeling to generate alternatives (MGA). This approach embeds stakeholder values directly into the optimization process, automatically producing a diverse set of technically feasible and value-aligned alternatives. Preference-based evaluation of these alternatives supports strategic decision-making. Demonstrated in a university campus decarbonization case study, VF-MGA generated 691 relevant alternatives, effectively identifying preferred solutions and refining preference information, thereby validating its generality and practical utility.

This paper addresses three core challenges in multi-criteria decision-making (MCDM): difficulty in modeling group consensus, neglect of criterion interdependencies, and weak representation of decision-maker preference uncertainty (e.g., normal/triangular distributions, interval-valued preferences). To this end, we propose the first unified Bayesian probabilistic framework for MCDM. Methodologically, we integrate probabilistic graphical models with finite mixture models and introduce a novel hierarchical group identification algorithm to detect homogeneous subgroups, alongside a probabilistic mechanism for ranking criteria and alternatives. Our key contribution lies in establishing a holistic probabilistic modeling paradigm for MCDM, enabling flexible incorporation of diverse preference representations and rigorous handling of interval-valued probabilities. Empirical evaluations demonstrate that our approach significantly improves accuracy in group consensus identification and robustness in ranking outcomes, while outperforming conventional MCDM methods in uncertainty adaptability and statistical interpretability.

This study addresses a critical gap in algorithmic fairness research, which has predominantly focused on trade-offs between performance and fairness in prediction space while overlooking the real-world utilities of multiple stakeholders and welfare distribution across groups. The authors propose a novel multi-stakeholder framework grounded in welfare economics and distributive justice, formalizing fairness as the social planner’s utility and employing posterior multi-objective optimization to identify optimal trade-offs between decision-maker utility and societal fairness. For the first time, they characterize the fairness–performance Pareto frontier in utility space under both deterministic and randomized policies, theoretically demonstrating that randomization can yield strictly superior trade-offs under certain conditions. Empirical results confirm that simple randomized mechanisms leverage outcome uncertainty to enhance fairness–performance balance, offering a more transparent and equitable design paradigm for algorithmic decision systems.

This study addresses a key limitation in traditional strategic research, which often treats firms as unitary decision-makers and overlooks the heterogeneity and potential conflicts among individual preferences within organizations. The authors propose a formal mathematical framework that integrates random utility models with specific aggregation mechanisms—such as unanimity or pluralistic governance—to derive, for the first time, an operational organizational utility function. This function is not a simple average of individual utilities but is endogenously shaped by the chosen aggregation rule: unanimity amplifies risk aversion, whereas pluralistic governance fosters risk-seeking behavior. Through applications to Cournot competition and principal–agent models, the framework effectively explains and predicts organizational behavior in strategic interactions, revealing how governance structures fundamentally reshape organizational preferences and influence competitive and cooperative outcomes.

This study addresses how organizations adopting commercial AI decision-support systems often passively accept vendors’ embedded and non-negotiable value judgments, thereby constraining their own decision flexibility. The paper introduces the concept of the “behaviorally feasible set” to formally characterize the range of recommendations an AI system can generate under value-alignment constraints and identifies critical conditions under which organizational needs exceed the system’s adaptive capacity. Through controlled experiments comparing binary decisions and multi-stakeholder preference rankings, the research demonstrates that value alignment substantially shrinks the behaviorally feasible set, diminishing the system’s responsiveness to legitimate contextual variation. Commercial models exhibit heightened rigidity, and the alignment process systematically shifts—rather than neutralizes—stakeholder priorities, revealing that value alignment functions as a structural mechanism embedding vendor values and narrowing organizational negotiation space.

This study addresses a key limitation of traditional multi-attribute decision-making models, which assume full compensability and thus fail to account for human tendencies to outright reject alternatives that perform poorly on critical attributes. To resolve this, the authors propose a bounded trade-off screening mechanism that formalizes tolerance for attribute trade-offs as a controllable parameter, capturing decision makers’ non-compensatory judgments across varying contexts. Through computational modeling and simulation experiments, they develop a lightweight choice model that successfully reproduces preference patterns distinct from those predicted by classical utility theory. The approach elucidates the computational underpinnings of non-compensatory screening and context-dependent preferences, while generating testable behavioral predictions that offer a novel mechanistic account of human multi-attribute decision making.

Current evaluation benchmarks for large language models (LLMs) are typically established by a single entity, failing to reflect the diverse values of multiple stakeholders and lacking support for collaborative development and iterative refinement. This work introduces MultEval, the first system to integrate consensus-building mechanisms into the LLM evaluation standard formulation process. MultEval combines consensus theory, an interactive collaborative interface, example anchoring, and versioned standard management to enable disagreement diagnosis, transparent justification of judgments, and traceable evolution of evaluation criteria. In case studies with domain experts, MultEval effectively facilitated multi-stakeholder consensus and clearly captured the dynamic evolution of evaluation standards through collaborative negotiation.

This study addresses the challenge posed by heterogeneity in individual values, which often impedes consensus in value-based decision-making. To this end, the paper proposes a decentralized optimization framework that, for the first time, enables the generation of multiple value agreements that accommodate individual differences while reflecting societal diversity. The approach integrates participatory value elicitation with data from the European Values Survey and models individuals’ willingness to compromise to effectively aggregate diverse value systems. Experimental evaluations in two real-world scenarios demonstrate that the proposed method significantly enhances individual utility and outperforms existing aggregation techniques.

This work addresses the limitation of existing scenario theory, which is confined to single-criterion robustness evaluation and thus ill-suited for real-world decision problems involving multiple criteria that rely on distinct datasets. To overcome this, the paper proposes a general data-driven multi-criteria scenario framework that jointly models violation risks across all criteria, enabling precise quantification of overall robustness guarantees that simultaneously satisfy every criterion. By integrating scenario optimization, probabilistic robustness analysis, and joint risk modeling over multiple datasets, the approach substantially improves the accuracy of robustness certificates. This advancement transcends the constraints of traditional single-criterion methods and offers a theoretically rigorous, scalable, and more accurate framework for delivering joint robustness guarantees in a broad range of multi-criteria decision-making settings.