A Simple Efficient Technique to Adjust Time Step Size in a Stochastic Discrete Time Agent-based Simulation
Chia-Tung Kuo, Da-Wei Wang, Tsan-sheng Hsu
2012
Abstract
This paper presents a conceptually simple approach on adjusting the time step size in a stochastic discrete time agent-based simulation and demonstrates how this could be done in practical implementation. The choice of time step size in such a system is often based on the nature of the phenomenon to be modelled and the tolerated simulation time. A finer time scale may be desired upon the introduction of new events which could possibly change the system state in smaller time intervals. Our approach divides each original time step into any integral number of equally spaced sub-steps based on simple assumptions, and thus allows a simulation system to incorporate such events and produce results with finer time scale. Regarding the tradeoff between finer scale and higher use of resource, our approach also highlights the implementation techniques that increase the resource usage and simulation time only marginally. We analyze the results of this refinement on a stochastic simulation model for epidemic spread and compare the results with the original system without refinement.
References
- Buss, A. and Rowaei, A. A. (2010). A comparison of the accuracy of discrete event and discrete time. In Proceedings of the 2010 Winter Simulation Conference.
- Buss, A. and Rowaei, A. A. (2011). The effects of time advance mechanism on simple agent behaviors in combat simulations. In Proceedings of the 2011 Winter Simulation Conference.
- Diekmann, O. and Heesterbeek, J. (2000). Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis, and Interpretation. John Wiley and Sons Ltd.
- Germann, T. C., Kadau, K., Ira M. Longini, J., and Macken, C. A. (2006). Mitigation strategies for pandemic influenza in the united states. In Proceedings of the National Academy of Sciences, volume 103, pages 5935- 5940.
- Kelker, D. (1973). A random walk epidemic simulation. Journal of the American Statistical Association, 68(344):821-823.
- Riley, S. (2007). Large-scale spatial-transmission models of infectious disease. Science, 316(5627):1298-1301.
- Tsai, M.-T., Chern, T.-C., Chuang, J.-H., Hsueh, C.-W., Kuo, H.-S., Liau, C.-J., Riley, S., Shen, B.-J., Shen, C.-H., Wang, D.-W., and Hsu, T.-S. (2010a). Efficient simulation of the spatial transmission dynamics of influenza. PloS ONE.
- Tsai, M.-T., Wang, D.-W., Liau, C.-J., and sheng Hsu, T. (2010b). Heterogeneous subset sampling. In Lecture Notes in Computer Science, volume 6196, pages 500- 509.
- Vangheluwe, H. (2001). Discrete event modelling and simulation. Lecture Notes, CS522 McGill University.
Paper Citation
in Harvard Style
Kuo C., Wang D. and Hsu T. (2012). A Simple Efficient Technique to Adjust Time Step Size in a Stochastic Discrete Time Agent-based Simulation . In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-8565-20-4, pages 42-48. DOI: 10.5220/0004056000420048
in Bibtex Style
@conference{simultech12,
author={Chia-Tung Kuo and Da-Wei Wang and Tsan-sheng Hsu},
title={A Simple Efficient Technique to Adjust Time Step Size in a Stochastic Discrete Time Agent-based Simulation},
booktitle={Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2012},
pages={42-48},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004056000420048},
isbn={978-989-8565-20-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - A Simple Efficient Technique to Adjust Time Step Size in a Stochastic Discrete Time Agent-based Simulation
SN - 978-989-8565-20-4
AU - Kuo C.
AU - Wang D.
AU - Hsu T.
PY - 2012
SP - 42
EP - 48
DO - 10.5220/0004056000420048