期刊文献+

文化认同及文化版图演化现象的社会计算模型 被引量:6

Computational Modeling of a Social Phenomenon:Evolution of Cultural Identity and Cultural Territory
下载PDF
导出
摘要 文化认同以及由于文化认同感的演变结果与地理边界的不一致性在文化版图上形成的文化侵蚀现象、文化融合现象等文化安全问题是文化领域的核心问题之一.在社会计算方法引入之前,该问题是典型的不能假设、无法进行计算实验的社会科学问题.将社会计算方法引入这一核心问题,使对该问题的实验研究进入到一个无风险的实验空间;运用基于多智能体的人工社会建模技术,确定了若干可刻画跨文化交流或文化内部环境稳定性的计算因子、文化抗消解惯性因子、文化身份状态因子、异族群的智能体间地理距离因子、"安土重迁"心理特征因子及"安土重迁"行为特征因子等;在此基础上,给出了刻画文化认同及其版图边界演变过程中文化侵蚀、文化融合与文化保护等现象的社会计算模型,并进行了大量的社会计算实验.实验结果表明:所选取的各种计算因子都是影响文化认同及其版图演化过程的重要因素,但作用各不相同;文化认同感演化的诸多规律也由该模型得出.该计算模型今后可在各类文化保护方案设计和方案效果评估等实际社会工作中发挥作用. Cultural identity is one of the core problems in cultural field. Cultural identity problem refers to the phenomenon of cultural erosion and cultural integration appearing on the cultural territory inconsistent with geological boundaries. This phenomenon is caused by the evolution of the sense of cultural identity. Before the social computing methods are introduced in, this problem is a typically social science problem which can't be applied to computing experiments. In this paper, we apply social computing methods to this problem. The experimental research of it is brought into a risk-free space. Using the artificial social modeling techniques based on the multi-agents, we have identified several computing factors. These computing factors can characterize the cross cultural communication and the internal stability of the cultural environment. The factors such as cultural resist-digestion inertia factor, cultural identity status factor, geological distance factor among different populations and the psychological and behavior characteristics factors of "hate-to-leave" are included.
出处 《计算机研究与发展》 EI CSCD 北大核心 2013年第12期2590-2602,共13页 Journal of Computer Research and Development
基金 国家教育部人文社会科学研究基金项目(12YJCZH263) 国家教育全国教育科学规划课题(ECA080288) 大连市金州新区软科学研究计划项目(20110405/225004)
关键词 社会计算 文化安全 文化认同 文化版图 演化 计算实验 人工社会 social computing cultural security cultural identity cultural territory evolution computational experiment artificial society
  • 相关文献

参考文献3

二级参考文献51

共引文献67

同被引文献46

  • 1王飞跃.人工社会、计算实验、平行系统——关于复杂社会经济系统计算研究的讨论[J].复杂系统与复杂性科学,2004,1(4):25-35. 被引量:231
  • 2Tsai C F, Hung C. Cluster collaborative filt Soft Computing, Xue G R, Lin ering recommend 2012,12(4) :1417.
  • 3C, Yang Q, collaborative filtering using ensembles in ation. Applied -1425. et al. cluster Scalable based smoothing. In: The 28th Annual International ACM SIGIR Conference on Research andDevelopment in Information Retrieval. New York : ACM Press, 2005 ~ 114 - 121.
  • 4Shinde S K,Kulkarni U. Hybrid personalized rec- ommender system using centering-bunching based clustering algorithm. Expert Systems with Applications, 2012,39 (1) : 1381- 1387.
  • 5Gong S J. A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software,2010,5 (7) : 745- 752.
  • 6Srebro N, Rennie J, Jaakola T. Maximum-margin matrix factorization. Advances in Neural Information Processing Systems, 2005, 17: 1329-1336.
  • 7Kolda T G, Bader B W. Tensor decompositions and applications. Society for Industrial and Applied Mathematics, 2009,51 (3) : 455 - 500.
  • 8Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recom- mendation. In.. The 3ra ACM International Conference on Web Search and Data Mining. New York:ACM Press,2010 ..81 -90.
  • 9Zhu Y X, Huang J,Zhang Z K,et al. Geography and similarity of regional euisi~ms in China. PLOS ONE,2013,8(11) ~1-8.
  • 10Steffen R. Factorization machines. In: The 10th IEEE International Conference on Data Mining. New York:IEEE Press , 2010 =14 17.

引证文献6

二级引证文献7

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部