A Multi-Agent Platform to Explore Strategies for Age-Friendly Community Projects in Urban China

Gerontologist. 2022 Apr 20;62(4):530-542. doi: 10.1093/geront/gnab150.

Abstract

Background and objectives: Although numerous efforts have been made to promote age-friendly communities (AFCs) in urban China, challenges such as the engagement and management of stakeholders, budget constraints, and policy issues remain. This article describes the work of designing a multi-agent platform (MAP) for the briefing stage of AFC projects.

Research design and methods: The process to design the MAP is first described, and the components and variables are identified. Then, a case study of a stakeholder consensus formation process is conducted using an agent-based simulation. Next, according to the simulation results, strategies to handle the conflicts arising among the stakeholders of AFC projects are proposed.

Results: According to the agent-based simulation conducted, both the initial approval rate and the outside connection rate will affect the stakeholder consensus formation process. Although a higher initial approval rate and a lower outside connection rate may reduce the average convergence time, the results show that 3-5 rounds of information exchange are still needed before a consensus or dissent is formed.

Discussion and implications: Investors are suggested to communicate with residents and alleviate their concerns regarding AFC projects to facilitate the consensus formation process during the briefing stage of AFC projects; they can also organize activities for residents to exchange information and ideas. The simulation conducted, together with the MAP built in this research, will serve as a reference to help researchers and practitioners further understand the briefing stage and explore efficient strategies for the successful implementation of AFC projects in urban China.

Keywords: Agent-based model; Briefing; Consensus formation; Multiple stakeholders.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Humans
  • Policy*
  • Research Personnel*