diffusion of innovation in an organization, industry, or society, agent-based modeling can be used to model the adaptation of intelligent systems that consist of intelligent individuals. As most intelligent systems are complex in both structure and system dynamics, traditional modeling tools that require too many unrealistic assumptions have become less effective in modeling intelligent systems. In recent years, agent-based modeling has found a wide spectrum of applications such as in business strategic solutions, supply chain management, stock markets, power economy, social evolution, military operations, security, and ecology (North and Macal, 2007). As ABM tools and resources become more accessible, research and applications of agent-based intelligent system modeling are expected to increase in the near future.
Some challenges remain, though. Using ABM to model intelligent systems is a research area that
draws theories from other fields, such as economics,
psychology, sociology, etc., but without its own well established theoretic foundation. ABM has four key assumptions (Macy and Willer, 2002): Agents act locally with little or no central authority; agents are interdependent; agents follow simple rules, and agents are adaptive. However, some of those assumptions may not be applicable to intelligent system modeling. Central authorities, or central authoritative information such as mass media in the innovation diffusion example, may play an important role in intelligent organizations. Not all agents are alike in an intelligent system. Some may be independent, non-adaptive, or following complex behavior rules.
ABM uses a “bottom-up” approach, creating
emergent behaviors of an intelligent system through
“actors” rather than “factors”. However, macro-level
factors have direct impact on macro behaviors of the system. Macy and Willer (2002) suggest that bringing those macro-level factors back will make agent-based modeling more effective, especially in intelligent systems such as social organizations.
Recent intelligent systems research has developed the concept of integrating human and machine-based data, knowledge, and intelligence. Kirn (1996) postulates that the organization of the 21st century will involve artificial agents based system highly intertwined with
human intelligence of the organization. Thus, a new challenge for agent-based intelligent system modeling is to develop models that account for interaction, aggregation, and coordination of intelligent agent and human agents. The ABM will represent not only the human players in an intelligent system, but also the intelligent agents that are developed in real-world applications in those systems.
CONCLUSION
Modeling intelligent systems involving multiple
intelligent players has been difficult using traditional
approaches. We have reviewed recent development in agent-based modeling and suggest agent-based modeling is well suited for studying intelligent systems, especially those systems with sophisticated and heterogeneous participants. Agent-based modeling allows us to model system behaviors based on the actions and interactions of individuals in the system. Although most ABM research focuses on local rules and behaviors, it is possible that we integrate global
influences in the models. ABM represents a novel
approach to model intelligent systems. Combined with traditional modeling approaches (for example, micro- level simulation as proposed in MoSeS), ABM offers researchers a promising tool to solve complex and practical problems and to broaden research endeavors (Wu, 2007).
Agent-Based Intelligent System Modeling
REFERENCES
Abrahamson, E. and L. Rosenkopf ( 1997). Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation. Organization Science. 8(3), 289-309.
Bass, F. M. (1969). A New Product Growth Model for Consumer Durables, Management Science, 13(5). 215-227.
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. PNAS May 14, 2002. 99, suppl. 3, 7280-7287.
Brown, D.G. (2006). Agent-based models. In H. Geist, Ed. The Earth’s Changing Land: An Encyclopedia of Land-Use and Land-Cover Change. Westport CT: Greenwood Publishing Group. 7-13.
Doran J. E. (00). Agent Design for Agent Based Modeling. In Agent Based Computational Modelling: Applications in Demography, Social, Economic and Environmental Sciences, eds. F. C. Billari, T. Fent, A. Prskawetz, and J.Scheffran. Physica-Verlag (Springer). 215-223.
Filippo Castiglione (2000), ‘Diffusion and aggregation
in an agent based model of stock market fluctuations’,
International Journal of Modern Physics C. 11(5), 1-15.
Fritz, Walter (1997).Intelligent Systems and their Societies. First version: Jan 27, 1997 http://www. intelligent-systems.com.ar/intsyst/index.htm
Gupta, J. N. D. and S. K. Sharma (2004). Editors. Intelligent Enterprises for the 21st Century. Hershey, PA: Idea Group Publishing.
Holland, J.H. (1992). Complex adaptive systems. Daedalus. 121(1), 17-30.
Kirn, S. 1996. Organizational intelligence and
distributed artificial intelligence. In Foundations of Distributed Artificial intelligence, G. M. O’Hare and N. R. Jennings, Eds. John Wiley Sixth-Generation Computer Technology Series. John Wiley & Sons, New York, NY. 505-526.
Liang, T. Y. (2002). The Inherent Structure and Dynamic of Intelligent Human Organizations, Human Systems Management. 21(1), 9-19.
Liebowitz, J. (1999). Building Organizational Intelligence: A Knowledge Primer, New York: CRC Press.
Macal, C. M. and North, M. J. (2005). Tutorial on Agent-Based Modeling and Simulation. Proceedings of the 37th Winter Simulation Conference, Orlando,
Florida. 2-15.
Macy, M. W. (2002). Social Simulation, In N. Smelser and P. Baltes, eds., International Encyclopedia of the Social and Behavioral Sciences, Elsevier, The Netherlands.
Macy, M.W, and Willer, R. (2002). From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual Review of Sociology. 28, 143- 166.
McMaster, M. D. (1996). The Intelligence Advantage: Organizing for Complexity. Burlington MA: Butterworth-Heineman.
Midgley, D.F., Marks R.E., and Kunchamwar D. (2007). The Building and Assurance of Agent-Based Models: An Example and Challenge to the Field. Journal of Business Research. 60(8), 884-893.
Neri, F. (2004). Agent Based Simulation of Information Diffusion in a Virtual Market Place. IEEE/WIC/ ACM International Conference on Intelligent Agent Technology (IAT'04). 333-336.
North, M. J. and C. M. Macal, (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-based Modeling and Simulation. Oxford University Press, New York.
Pepper, J. W. (2000) An Agent-Based Model of Group Selection, Santa Fe Institute. Retrieved June 16, 2007 at: http://www.santafe.edu/~jpepper/papers/ALIFE7_ GS.pdf
Rogers, E.M. (1995). Diffusion of Innovations. The Free Press, New York.
Samuels, M.L. (2007). Innovation model. Last updated: 01/08/2007, http://ccl.northwestern.edu/netlogo/ models/community/Innovation.
Wooldridge, M. and N. R. Jennings (1995). Intelligent Agents: Theory and Practice, Knowledge Engineering Review. 10(2), 115-152.
Agent-Based Intelligent System Modeling