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Conference Paper
Combining Microsimulation and Agent-Based Model for Micro-Level Population Dynamics
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- Authors
- Jang Won Bae, Euihyun Paik, Kiho Kim, Karandeep Singh, Mazhar Sajjad
- Issue Date
- 2016-06
- Citation
- International Conference on Computational Science (ICCS) 2016, pp.507-517
- Language
- English
- Type
- Conference Paper
- Project Code
- 15MS7400, Distributed/Parallel Multi-Dimensional Demographic Micro Simulation Technologies, Eui Hyun Paik
- Abstract
- Population dynamics illustrates the changes of the size and age composition of populations. Modeling and simulation techniques have been used to model the population dynamics, and the developed models are utilized to design and analyze public polices. One classic modeling method is microsimulation. The microsimulation describes the population dynamics at the individual level, and actions conducted by the individuals are generated by stochastic process. An emerging method is agent-based model, which rather focuses on the interactions among individuals and expects to see unexpected situations created from the interactions. Their similar but different approaches can make them to complement weak points of the opponent in population dynamics analysis. From this perspective, This paper proposes a hybrid model structure combining microsimulation and agent-based model for modeling population dynamics. In the proposed model, the microsimulation model takes a role to depict how an individual chooses its behavior based on stochastic process parameterized by real data; the agent-based model incorporates interactions among individuals considering their own states and rules. The case study introduces Korean population dynamics model developed by the proposed approach, and its simulation results show the population changes triggered by a variance of behavior and interaction factors.
- KSP Keywords
- Case studies, Hybrid Models, Interaction factors, Korean population, Micro-level, Model structure, Modeling and Simulation, Modeling method, Population dynamics model, Proposed model, Real data