Modeling Uncertainty through Agent-Based Participatory Simulation: Implication to Businesses in China

China is a society that fosters complex social networks and they are considered powerful advantage in times of uncertainty. This article proposes agent-based modeling and simulation (ABMS) as a useful tool to analyze such complex relationships in China. It also reviews schools of thought on hard versus soft systems methodologies and brings together the fragmented literature on the nature of agent-based modeling, making the argument that participatory simulation is a distinct tool belonging to soft systems methodology, one which can simultaneously allow for the metrics of ‘hard’ decision sciences to be applied. This is a promising area of research in complex and highly dynamic systems and is particularly relevant to a country such as China due to its complexity and size.

[1]  Itzhak Benenson,et al.  PARKAGENT: An agent-based model of parking in the city , 2008, Comput. Environ. Urban Syst..

[2]  Christian Lebiere,et al.  Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation , 2006 .

[3]  Christopher J. Atkinson,et al.  The ‘Soft Information Systems and Technologies Methodology’ (SISTeM): an actor network contingency approach to integrated development , 2000, Eur. J. Inf. Syst..

[4]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Kyoichi Kijima,et al.  Organization design initiated by information system development: a methodology and its practice in Japan , 2001 .

[6]  R. Raeside,et al.  Analysing communication in a complex service process: an application of social network analysis in the Scottish Prison Service , 2010, J. Oper. Res. Soc..

[7]  R. Sun Cognition and Multi-Agent Interactions: From Cognitive Modeling to Social Simulation , 2005 .

[8]  G. Hofstede The Cultural Relativity of the Quality of Life Concept , 1984 .

[9]  Russell L. Purvis,et al.  An examination of designer and user perceptions of JAD and the traditional IS design methodology , 1997, Inf. Manag..

[10]  Jeffrey L. Whitten,et al.  Systems Analysis and Design Methods , 1986 .

[11]  Peter Checkland,et al.  O.R. and the Systems Movement: Mappings and Conflicts , 1983 .

[12]  Gernot Liedtke,et al.  Principles of Micro-Behavior Commodity Transport Modeling , 2009 .

[13]  N. C. Simpson,et al.  The incident commander's problem: resource allocation in the context of emergency response , 2009 .

[14]  David C. Croson,et al.  Agent learning in supplier selection models , 2005, Decis. Support Syst..

[15]  James P. Neelankavil,et al.  The influence of culture on advertising effectiveness in China and the USA , 1997 .

[16]  Peter Checkland,et al.  Soft Systems Methodology in Action , 1990 .

[17]  Don Y. Lee,et al.  Guanxi, Trust, and Long-Term Orientation in Chinese Business Markets , 2005 .

[18]  Michael J. North,et al.  Tutorial on Agent-Based Modeling and Simulation PART 2: How to Model with Agents , 2006, Proceedings of the 2006 Winter Simulation Conference.

[19]  Jan W. Rivkin,et al.  Speed and Search: Designing Organizations for Turbulence and Complexity , 2005, Organ. Sci..

[20]  N.A.D. Connell,et al.  Evaluating soft OR: some reflections on an apparently ‘unsuccessful’ implementation using a Soft Systems Methodology (SSM) based approach , 2001, J. Oper. Res. Soc..