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Fairness-aware large-scale collective opinion generation paradigm: A case study of evaluating blockchain adoption barriers in medical supply chain by Zhen-Song Chen; Zhengze Zhu; Zhu-Jun Wang; Yungpo Tsang is a Computer Science article available to read on EtoBox.
What is Fairness-aware large-scale collective opinion generation paradigm: A case study of evaluating blockchain adoption barriers in medical supply chain about?
Generating collective opinion based on probability distribution function aggregation occupies crucial roles in accomplishing probabilistic risk analysis, probabilistic-forecast-based prediction, and uncertain assessment tasks. However, rare efforts have been paid to exploring collective opinion generation based on massive quantified judgments from large-scale experts. In this study, we establish a novel large-scale collective opinion generation paradigm based on probability distribution function aggregation to accomplish complicated assessment and evaluation tasks in decision analysis. To this end, we reformulate the existing bi-objective optimization model with the exclusion of the consensus dimension and inclusion of fairness concern among subject matter experts. The formulation of collective fairness utility uses the notion of aggregation functions and contributes to the establishment of large-scale collective opinion generation paradigm with capabilities of modeling distinct fairness distributions among the subject matter experts. The established fairness-aware large-scale collective opinion generation model advocates an adjusted bi-objective optimization model that maximizes t
Who reads Fairness-aware large-scale collective opinion generation paradigm: A case study of evaluating blockchain adoption barriers in medical supply chain?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Zhen-Song Chen; Zhengze Zhu; Zhu-Jun Wang; Yungpo Tsang
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)