Exploring Social Behavior through Transdisciplinary Science:

Description:

Chair: Prof. Nancy Law, Professor, Faculty of Education, The University of Hong Kong

Social behavior can be understood as the response to the stimuli given by others via social connections of various social units. However, there is a set of theories explaining the generalized underlying principles of social behavior, regardless of the complicated and diverse constructions of social systems. Social contagion is one of the social theories, which describes the mechanisms of the process that social contact is able to influence individuals' behavior. Contagion (Kan-tã'jan) roots from the Latin word contagio indicating a process of transmission by touch or contact (Marsden, 1998). The touch or contact in social settings can be interpreted as the causal mechanisms of social contagion, including information diffusion, normative pressure, competitive concern, and performance network effect (Van den Bulte and Lilien, 2001). The traditional studies on social behavior are mainly based on small-group research with observational data that were mostly from field work or experimental creation. In recent years, the advancement of computer-mediated communication and computing techniques has made the studies on large-scale complex social behavior possible. Our understanding of complex networks has also significantly promoted the studies on social agents and social connections, which is coupled with the advances in the knowledge from the areas as diverse as Physics and Biology; Mathematics and Sociology; Organizational science and Psychology. Interestingly, the most commonly cited link between these diverse areas of research is the self-organizing behavior of complex systems. Two of the most frequently mentioned properties of real-world complex systems are clustering behavior and the existence of scale-free network (Barabási, 2009). Scale-free network displays the characteristics of power-law distribution, which states that the probability that a randomly selected node has k links (i.e., degree k) follows P(k) ~ k – γ, where γ is the degree exponent (Ravasz and Barabási, 2003). Research in complex networks indicate that most networks display a high degree of clustering; and many scientific, technical and organizational networks, ranging from biological networks (Jeong et al., 2000) to WorldWideWeb (Albert et al., 1999) have been found to be scale-free. In this presentation, we will discuss the importance of studying social behavior especially social contagion using transdisciplinary science as a unified theoretical and modeling platform. We also will highlight how observation of real-world complex social phenomena using large-scale user-generated data can be used to develop an understanding of social contagion based on social G=(V,E). We believe that the transdisciplinary science provides an unprecedented opportunity for social scientists to investigate human behavior with a large scale spatial-temporal behavioral interactions data and fill the gap of sociological network theories.

References

Albert, R. et al. (1999) Diameter of the World Wide Web, Nature, 401, 130-131 (1999).

Barabási, A (2009) Scale-Free Networks: A Decade and Beyond, Science 325, 412.

Jeong, H et. al. (2000) The large-scale organization of metabolic networks, Nature, 407, 651–654.

Ravasz, E., Barabási, A (2003) Hierarchical organization in complex networks, Phy. Rev. E67, 02611267.

Marsden, P. (1998). Memetics and social contagion: Two sides of the same coin. Journal of Memetics-Evolutionary Models of Information Transmission, 2(2), 171-185.

Van den Bulte, C., & Lilien, G. L. (2001). Medical innovation revisited: Social contagion versus marketing effort. American Journal of Sociology, 106(5), 1409-1435.