Maximum-Return-Driven Consensus Framework With Internal-External Compensation in Undirected Collaboration Network.

Area of Science:

• Decision Sciences
• Game Theory
• Environmental Governance

Background:

• Group decision-making (GDM) with heterogeneous interests, like in transboundary watershed governance, faces challenges in consensus formation.
• Existing models often focus on moderator costs rather than individual returns.

Purpose of the Study:

• Develop a directionally asymmetric maximum-return consensus model (MRCM) with nonnegative return constraints.
• Shift the perspective from moderator costs to individual returns for consensus formation.
• Propose a feasibility diagnosis and adaptive compensation framework for GDM.

Main Methods:

• Developed a directionally asymmetric maximum-return consensus model (MRCM).
• Integrated minimum-slack feasibility checking model (MSFCM) for feasibility diagnosis.
• Proposed a twofold adaptive consensus framework with internal and external compensation mechanisms.

Main Results:

• The proposed feasibility diagnosis approach effectively determines MRCM feasibility and identifies deficit origins.
• The adaptive consensus framework successfully selects compensation strategies based on diagnosis results.
• Demonstrated enhanced consensus feasibility and stability in a numerical study of the Dongjiang River Basin.

Conclusions:

• The novel return-driven consensus mechanism, governed by feasibility diagnosis, refines return formulation and compensation schemes.
• The framework adaptively enhances consensus feasibility and stability in complex GDM scenarios.
• The individual-return perspective is crucial for effective consensus in heterogeneous group decision-making.