TY - GEN
T1 - GBM based Policy Influence Analysis of Agent Simulation Parameters
AU - Jung, Joonyoung
AU - Bae, Jang Won
AU - Lee, Chunhee
AU - Kang, Dong Oh
AU - Paik, Euihyun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In this paper, GBM(generalized boosting model) based policy influence analysis is presented for house market agent simulation. In order to execute the agent simulation, various simulation parameters must be set. Among the parameters to be set, there are policy parameters that affect the policy goal, which is the main result of the simulation. Therefore, if simulation is executed by setting policy parameters variously, the results of simulation may be different. Simulation is usually performed by combining policy parameters based on policy objectives and then the result is analyzed whether the simulation result meets policy goals or not. However, it is difficult to analyze what kind of policy parameters affect policy objectives among policy parameters. In this paper, in order to analyze how the policy parameters affect policy goals, we set policy parameters in various combinations, and then execute social phenomenon agent simulations, and then analyze the impact of policy parameters using GBM algorithm.
AB - In this paper, GBM(generalized boosting model) based policy influence analysis is presented for house market agent simulation. In order to execute the agent simulation, various simulation parameters must be set. Among the parameters to be set, there are policy parameters that affect the policy goal, which is the main result of the simulation. Therefore, if simulation is executed by setting policy parameters variously, the results of simulation may be different. Simulation is usually performed by combining policy parameters based on policy objectives and then the result is analyzed whether the simulation result meets policy goals or not. However, it is difficult to analyze what kind of policy parameters affect policy objectives among policy parameters. In this paper, in order to analyze how the policy parameters affect policy goals, we set policy parameters in various combinations, and then execute social phenomenon agent simulations, and then analyze the impact of policy parameters using GBM algorithm.
KW - agent simulation.
KW - policy influence analysis
KW - Policy parameters
UR - http://www.scopus.com/inward/record.url?scp=85078312081&partnerID=8YFLogxK
U2 - 10.1109/ICTC46691.2019.8939694
DO - 10.1109/ICTC46691.2019.8939694
M3 - Conference contribution
AN - SCOPUS:85078312081
T3 - ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
SP - 1324
EP - 1326
BT - ICTC 2019 - 10th International Conference on ICT Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information and Communication Technology Convergence, ICTC 2019
Y2 - 16 October 2019 through 18 October 2019
ER -