Agent-Based Simulation for Service Science

The most important building blocks of service systems are human beings. Because of the dynamic and heterogeneous interactions among human beings with their bounded rationality, a service system is recognized as a complex adaptive system to which quantitative scientific analysis is difficult to apply. In this chapter, we discuss a computational approach for such complex adaptive systems called agent-based simulation. Since the 1990s, agent-based simulation has gained significance as a tool to reproduce complex stock market interactions by modeling human traders as software agents. Computer scientists and social scientists are working together to model social systems with interacting heterogeneous agents and simulating their dynamic behaviors using computers. As our computational resources continue to grow rapidly, the application areas for agent-based simulations are spreading into areas of social science that overlap with SSME research. We will introduce several examples of agent-based simulations for marketing, for emissions trading, for communications, and for traffic systems and discuss the contributions of this scientific approach to the study of service systems.

[1]  Kenneth Steiglitz,et al.  Agent-based simulation of dynamic online auctions , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[2]  Yoshiki Yamagata,et al.  Gaming simulation of the international CO2 emission trading under the Kyoto Protocol , 2005 .

[3]  Hideki Tai,et al.  A platform for massive agent-based simulation and its evaluation , 2007, AAMAS '07.

[4]  Takashi Iba,et al.  Boxed Economy Foundation Model: Model Framework for Agent-Based Economic Simulations , 2001, JSAI Workshops.

[5]  Kenneth Steiglitz,et al.  Effects of price signal choices on market stability , 2003 .

[6]  Danny B. Lange,et al.  Programming and Deploying Java¿ Mobile Agents with Aglets¿ , 1998 .

[7]  Kenneth Steiglitz,et al.  Simulating the Madness of Crowds: Price Bubbles in an Auction-Mediated Robot Market , 1998 .

[8]  Kiyoshi Izumi,et al.  An Artificial Market Model of a Foreign Exchange Market , 2001 .

[9]  Toyotaro Suzumura,et al.  X10-based massive parallel large-scale traffic flow simulation , 2012, X10 '12.

[10]  Albert-László Barabási,et al.  Linked: The New Science of Networks , 2002 .

[11]  Paul P. Maglio,et al.  Toward a Science of Service Systems , 2010 .

[12]  Yoshiki Yamagata,et al.  Agent-based simulation and greenhouse gas emissions trading , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[13]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[14]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[15]  Hideyuki Mizuta,et al.  Agent-based simulation of enterprise communication network , 2005, Proceedings of the Winter Simulation Conference, 2005..

[16]  Takashi Oguchi,et al.  NEW CONCEPTUAL EVALUATION METHOD OF AMOUNT OF EXHAUST EMISSION GAS ON VEHICULAR ROAD TRAFFIC , 2000 .

[17]  Rosario N. Mantegna,et al.  Book Review: An Introduction to Econophysics, Correlations, and Complexity in Finance, N. Rosario, H. Mantegna, and H. E. Stanley, Cambridge University Press, Cambridge, 2000. , 2000 .

[18]  Yoshiki Yamagata,et al.  Agent-Based Simulation for Economic and Environmental Studies , 2001, JSAI Workshops.

[19]  Kathleen M. Carley,et al.  A PCANS Model of Structure in Organizations , 1998 .

[20]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[21]  Koichi Kurumatani,et al.  Agent-based approaches in economic and social complex systems , 2001 .

[22]  Joshua M. Epstein,et al.  Growing artificial societies , 1996 .

[23]  Yoshiki Yamagata,et al.  Transaction cycle of agents and Web-based gaming simulation for international emissions trading , 2002, Proceedings of the Winter Simulation Conference.