Using the Process of Norm Emergence to Model Consensus Formation

• Published in: 2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems pp. 148 - 157
• Main Authors: Hollander, C. D., Wu, A. S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2011
• Subjects: Adaptation models; Agent-Based Model; Complex Systems; Computational modeling; Computational Social Science; Consensus; Convergence; Dynamic equilibrium; Emergence; Lattices; Norms; Receivers; Self-Organization; Simulation; Stability analysis
• ISBN: 1457716143; 9781457716140
• ISSN: 1949-3673
• DOI: 10.1109/SASO.2011.26

Every agent in a society initially possesses a set of personal norms. Group norms emerge when agents interact with one another and exchange information in such a way that multiple agents begin to acquire the same personal norm. This emergence is the result of information transmission, social enforcement, and internalization. If a population contains a single group norm, as a result of every agent in the population acquiring the same personal norm, then it can be said that a consensus has been reached by the population. We model the formation of consensus in silico by adapting a recently developed model of norm emergence to a multi-agent simulation. A screening experiment is conducted to identify the significant parameters of our model and verify that our model is capable of producing a consensus. The experimental results show that our model can attain consensus as well as two additional states of information equilibrium. The results also indicate that both network structure and agent behavior play an important role in the formation of consensus. In addition, it is shown that the formation of consensus is sensitive to the simulation parameter settings, and certain values can prevent its formation entirely.