Active strategy learning-based consensus mechanism for group decision making with efficiency and fairness
Year: 2025
Type: article
Abstract: Efficiency and fairness constitute pivotal criteria for evaluating consensus mechanisms in group decision-making (GDM) systems, where both active strategy learning and dynamic weight allocation critically impact these metrics. Active strategy learning empowers decision makers (DMs/agents) to iteratively optimize consensus strategies through Q-learning-based environmental interactions, effectively balancing individual preferences with collective objectives. Concurrently, a contribution-weighted allocation mechanism ensures fairness while maintaining original opinion authenticity. To address existing research gaps in integrated learning frameworks, fairness quantification, and decision rationality verification, this study proposes an adaptive Q-learning consensus model enhancing efficiency through reward feedback mechanisms. A dual-factor reward function integrating contribution weights and opinion retention to improve fairness metrics. An algorithmic framework with complexity analysis is validated through multi-scenario simulations. Empirical results from case study and simulation analysis demonstrate the method’s superior performance in achieving efficient convergence, equitable resource distribution, and rational opinion evolution compared to conventional approaches.
Source: Information Sciences
Authors: Xia Liu, Yajing Shan, Ziyan Song, Yejun Xu, Xiulai Wang
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
OpenAlex ID: W4409259932