Accession Number : AD1050179


Title :   Social Dynamics Modeling and Inference


Descriptive Note : Technical Report,14 May 2014,13 May 2017


Corporate Author : NATIONAL TAIWAN UNIVERSITY Tokyo Japan


Personal Author(s) : Chen, Kwang-Cheng


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1050179.pdf


Report Date : 29 Mar 2018


Pagination or Media Count : 7


Abstract : The fundamental goal of this research project is to comprehend complex collective behavior in human society, to set up the foundation of future possible inference and even control of social collective behavior. Two primary technological directions have been fruitfully developed to accomplish such goal: Innovative data analytics to effectively extract critical features from the data, to rapidly recognize the abrupt changes of data, and to identify the network relationship of the data. In addition to synthesized data, the main results have been verified from real Internet data. A new social network model associated with rate rather than traditional graphical properties has been developed to make inference possible, which has been verified by different Internet datasets. In-depth comprehension of information cascade, a collective behavior that agents ignore own signals/observation but follow others, which serves as a foundation of modern social warfare over Internet. We go beyond traditional analytical models of information cascade and develop further views to look into the insights of such dynamic system to enable methods to tilt collective behavior over the Internet or over human society. A subsequently new security mechanism for low-complexity sensor networks enables trustworthy operation even in the presence of a good number of compromised devices based on social learning and information cascade.


Descriptors :   human behavior , data analysis , social networks , societies


Subject Categories : Psychology
      Sociology and Law


Distribution Statement : APPROVED FOR PUBLIC RELEASE