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SEA-SF : Design of Self-Evolving Agent based Simulation Framework for Social Issue Prediction

Authors:
Joon-Young Jung
Euihyun Paik
Jang Won Bae
Dongoh Kang
Chunhee Lee
Kiho Kim

Keywords: Agent Based Model; Self-Evolvement

Abstract:
Simulation is the imitation of the operation of a real-world process or system over time. Actual real world expectation is expensive and impossible because the modern society is complex and various. Therefore, simulation can be carried out to take proper measures for the problem which may be happened in the future. Agent based model (ABM) models each individuals and interactions among them. ABM mostly defines behaviors based on rule. However, ABM simulation has the weak point that simulation error is accumulated. If long term simulation is conducted, the simulation result will be highly inaccurate because of error accumulation. To overcome error accumulation, the model should be reconfigured using the real data recursively. In this paper, we propose the self-evolving agent based simulation framework (SEA-SF). The SEA-SF is consisted of data management, change recognition, model evolvement, ABM reconfiguration, user interface and ABM simulation environment. The SEA-SF should mitigate the long-term simulation error. Therefore, the SEA-SF performs change recognition between real data and simulation result. And then autonomously, it updates model parameters or the model configuration to increase accuracy of simulation. The proposed framework can be applied to solve the social issue problems because the social issue problems are happened through a long period. Therefore, the social issue simulation, such as the house policy and supply, can be performed using the proposed SEA-SF.

Pages: 67 to 70

Copyright: Copyright (c) IARIA, 2016

Publication date: November 13, 2016

Published in: conference

ISSN: 2519-8351

ISBN: 978-1-61208-519-7

Location: Barcelona, Spain

Dates: from November 13, 2016 to November 17, 2016