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Fostering artificial societies using social learning and social control in parallel emergency management systems

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Abstract

How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by designers? We propose a framework for fostering artificial societies using social learning mechanisms and social control approaches. We present the application of fostering artificial societies in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, including observational learning, reinforcement learning, imitation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.

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Authors and Affiliations

  1. Research Center of Military Computational Experiments and Parallel Systems Technology, College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha, 410073, China

    Wei Duan & Xiaogang Qiu

Authors
  1. Wei Duan
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  2. Xiaogang Qiu
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Correspondence to Wei Duan.

Additional information

Wei DUAN received his MS degree in 2008 in control science and engineering from the National University of Defense Technology, Changsha, China, where he is currently working toward his PhD degree. His research interests include artificial societies, agent-based modeling, and simulation.

Xiaogang QIU received his PhD degree in system simulation from the National University of Defense Technology, Changsha, China. He is a professor with the College of Mechatronic Engineering and Automation, National University of Defense Technology. His research interests include simulation, multi-agent systems, knowledge management, and parallel control.

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Duan, W., Qiu, X. Fostering artificial societies using social learning and social control in parallel emergency management systems. Front. Comput. Sci. 6, 604–610 (2012). https://doi.org/10.1007/s11704-012-1166-3

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  • DOI: https://doi.org/10.1007/s11704-012-1166-3

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