1.西安财经大学信息学院,陕西 西安 710100
2.智财协同可信计算陕西省高等学校重点实验室,陕西 西安 710100
3.山东大学软件学院,山东 济南 250101
4.天津大学智能与计算学部,天津 300072
5.西安文理学院信息工程学院,陕西 西安 710065
[ "李芳(1986- ),女,西安财经大学信息学院讲师,主要研究方向为云计算、计算实验、区块链。" ]
[ "周德雨(1995- ),女,山东大学软件学院博士生,主要研究方向为服务计算、计算实验。" ]
[ "王刚(1974- ),男,西安财经大学信息学院教授,主要研究方向为大数据、云计算、计算实验、信任管理、数字经济。" ]
[ "薛霄(1979- ),男,天津大学软件学院教授,主要研究方向为服务计算、计算实验和群体智能。" ]
[ "刘光军(1980- ),男,西安文理学院信息工程学院教授,主要研究方向为大数据安全、网络安全、人工智能安全。" ]
[ "胡淇(2006- ),男,西安文理学院信息工程学院本科生,主要研究方向为大数据应用。" ]
收稿:2025-09-30,
修回:2025-12-28,
录用:2025-12-30,
纸质出版:2025-12-15
移动端阅览
李芳,周德雨,王刚等.社会网络与个体行为驱动的传染病演化计算模型:跨层交互机制与数字治理策略[J].智能科学与技术学报,2025,07(04):533-542.
LI Fang,ZHOU Deyu,WANG Gang,et al.A computational model for the evolution of infectious diseases driven by social networks and individual behavior: cross layer interaction mechanism and digital governance strategy[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):533-542.
李芳,周德雨,王刚等.社会网络与个体行为驱动的传染病演化计算模型:跨层交互机制与数字治理策略[J].智能科学与技术学报,2025,07(04):533-542. DOI: 10.11959/j.issn.2096-6652.202544.
LI Fang,ZHOU Deyu,WANG Gang,et al.A computational model for the evolution of infectious diseases driven by social networks and individual behavior: cross layer interaction mechanism and digital governance strategy[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):533-542. DOI: 10.11959/j.issn.2096-6652.202544.
个体行为的随机性、社会关系的复杂性、防控策略的滞后性等因素,使传染病的传播过程呈现出高动态性与强不确定性。这对传染病的演化分析提出新的挑战。现有研究通过多层网络建模和个体行为分析,在疾病传播、行为演化和政策干预等方面取得了重要进展,但在跨层闭环机制的系统化建模方面仍不足,难以充分解释个体行为、社会网络结构、防控策略之间的耦合关系。基于此,从整体上刻画“政策、行为、传播和反馈”的动态交互过程,提出一种基于社会网络与个体行为的传染病演化计算模型,包含个体层、组织层和社会层3个互联层次。最后,通过分析计算实验在不同策略下的各类型Agent存活数量和最终达到的稳定状态这两个指标,验证此模型的有效性。
The randomness of individual behavior
the complexity of social relationships
and the lag in prevention and control strategies make the transmission process of infectious diseases highly dynamic and uncertain. This poses new challenges to the analysis of the evolution of infectious diseases. Existing research has made significant progress in disease transmission
behavioral evolution
and policy intervention through multi-layer network modeling and individual behavior analysis. However
there is still insufficient systematic modeling of cross-layer closed-loop mechanisms
which makes it difficult to fully explain the coupling relationship between individual behavior
social network structure
and prevention and control strategies. This paper describes the dynamic interaction process of “policy
behavior
dissemination
and feedback” as a whole
and proposes a computational model for the evolution of infectious diseases based on social networks and individual behavior
which includes three interconnected levels: individual level
organizational level
and social level. Finally
the effectiveness of this model was verified by analyzing and calculating the number of surviving agents of various types under different strategies and the final stable state achieved in the experiment.
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