A Lightweight Social Computing Approach to Emergency Management Policy Selection

Mingsheng Tang; Haibin Zhu; Xinjun Mao

doi:10.1109/TSMC.2015.2484281

A Lightweight Social Computing Approach to Emergency Management Policy Selection

IEEE Trans Syst Man Cybern Syst. 2016 Aug;46(8):1075-1087. doi: 10.1109/TSMC.2015.2484281. Epub 2015 Oct 26.

Authors

Mingsheng Tang¹, Haibin Zhu², Xinjun Mao¹

Affiliations

¹ College of ComputerNational University of Defense Technology Changsha410073China.
² Department of Computer Science and MathematicsNipissing University North BayONP1B 8L7Canada.

Abstract

In order to select effective policies for emergency management in a timely manner, this paper proposes an agile and lightweight social computing approach to facilitating policy selection, evaluation, and adjustment relative to emergency management in both quantitative and qualitative ways. The approach consists of three components represented as PZE: 1) (P) emergency management policy selecting; 2) (Z) modeling artificial societies with the zombie-city model (a general and formal artificial society model); and 3) (E) policy evaluation. The formal specification of the zombie-city model and rigorous expressions of scenarios enable rigorous description and formal reasoning of an artificial society. A feedback loop of this approach supports the iterative adjustment of emergency management policies and the creation of more effective policies. This approach is verified by applying it to a case of an infectious disease transmission with quantitative evaluations, qualitative reasoning and analysis, and iterative adjustments. Results indicate effective emergency management policies can be established with the approach in an iterative way. In contrast with existing research, our proposed approach offers the benefits of being simple, general, rapidly adaptive to changes, and low cost.

Keywords: Agent; artificial society; emergency management; social computing.

Grants and funding

This work was supported in part by the National Nature and Science Foundation of China under Grant 61379051 and Grant 61133001, the Program for New Century Excellent Talents in University under Grant NCET-10-0898, the Open Fund State Key Laboratory of Software Development Environment under Grant SKLSDE-2012KF-0X, and the National Sciences and Engineering Research Council of Canada under Grant RGPIN262075-2013.