Name
Abstract | ||
|---|---|---|
Due to the extensive social influence in modem information communication environment, public opinion event has caused significant attention in emergency management by the government. The formation and evolution of public opinion event is a dynamically interactive process affected by the participant's complicated psychology and behaviors. Agent-based modeling method can provide an effective and powerful tool to investigate the emerging phenomena in a social simulation system. Although many methodologies have been presented to support the standardized modeling, but the transition from expertise or theory model to computing simulation model is still a hard work for the sociologist, who usually lack of the required technological skills. In order to step over this barrier, we propose a domain-expert oriented methodology to guide the modeling process in a specific social domain, and design a meta-modeling framework based on the paradigm of Agent-Event-Environment (AEE) which can be considered as a basis of developing a visual modeling environment or tools. By the application in the real case of a major public opinion event, the proposed method has been verified effectively and efficiently. Research work of this article provides a valuable framework and methodology to deal with the transition of expertise or theoretical knowledge into computing simulation for a specific social domain. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.2298/CSIS140927024F | COMPUTER SCIENCE AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
agent-based modeling and simulation,methodology,meta-modeling framework,public opinion | Data mining,Computer science,Visual modeling,Emergency management,Knowledge management,Social simulation,Social influence,Public opinion,Artificial intelligence,Machine learning,Government | Journal |
Volume | Issue | ISSN |
12 | 2 | 1820-0214 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongchen Fan | 1 | 3 | 0.86 |
| Xiaogang Qiu | 2 | 149 | 20.35 |
| Weihui Dai | 3 | 115 | 19.79 |
| Yuanzheng Ge | 4 | 21 | 3.53 |
| Liang Liu | 5 | 163 | 40.93 |
| Yunsuo Duan | 6 | 0 | 0.68 |